Banks Risk 30% AI Error Cascade Without Human Oversight, Artigen Says

Building Trustworthy Agentic AI for Finance: From Pilots to Production With Artigen AI — Ai4

Ai4

Multi-agent workflows compound minor errors into systemic operational failures unless financial institutions enforce continuous telemetry and mandatory human sign-off gates under SR 11-7 model risk standards.

AI-assisted summary of Building Trustworthy Agentic AI for Finance: From Pilots to Production With Artigen AI — original source. Check the original for full context.

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## Background Published on YouTube by the Ai4 channel on August 28, 2025, this session features Rajendra Gangavarapu, Advisor at Artigen.ai, addressing the challenges of deploying multi-agent AI systems in high-stakes financial environments. According to the description, the talk covers critical hurdles such as reliability breakdowns, data security, and regulatory compliance, while providing practical strategies to architect compliant workflows without sacrificing trust or human oversight.

## TL;DR Gangavarapu explores the shift from generative tools to autonomous multi-agent systems in banking, emphasizing that fully autonomous execution without human-in-the-loop governance creates catastrophic operational and regulatory risks. He demonstrates how baseline agent errors compound exponentially across multi-step chains and why institutions must implement continuous telemetry, robust sandboxing, and strict model risk management aligned with Federal Reserve SR 11-7 and the EU AI Act. Using high-profile case studies across trading, real estate valuation, and AML/KYC investigations, the talk presents a gated canary deployment playbook and champion-challenger pipeline. The session concludes with Artigen AI's four-pillar enterprise framework—combining a central policy gateway, real-time observability, secure IAM/RAG connectors, and reusable CI/CD evaluation pipelines to safely balance agent autonomy with enterprise trust.

## Atmosphere & style The presentation carries a pragmatic, authoritative tone that speaks directly to enterprise risk officers and engineering leaders navigating heavily audited environments. Gangavarapu presents with measured, seasoned clarity, balancing the transformative speed of agentic AI against stark cautionary tales of multi-billion-dollar compliance failures and runaway trading systems. The pacing feels disciplined and deeply structured, evoking the rigorous atmosphere of an executive board briefing where technological ambition must strictly answer to regulatory accountability.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=4s) In heavily regulated financial services, AI must be built on a human-centric paradigm with strict regulatory governance to satisfy supervisory bodies like the Fed, OCC, and FDIC. - [01:31](https://www.youtube.com/watch?v=YokJh1BSFOw&t=91s) The industry is shifting from generative AI to Agentic AI, where autonomous multi-step execution demands strict human-in-the-loop oversight to avoid catastrophic trading errors and liability. - [05:01](https://www.youtube.com/watch?v=YokJh1BSFOw&t=301s) Core technical vulnerabilities in agentic systems include fragile long-term memory, inter-agent misalignment, and probabilistic hallucinations, requiring selective context retrieval and enterprise RAG over brute-force context windows. - [06:52](https://www.youtube.com/watch?v=YokJh1BSFOw&t=412s) Multi-agent pipelines suffer from compounding errors, where a 1% error rate at the initial agent can amplify to a 20% to 30% failure rate across a 20-agent chain without continuous telemetry and supervisor agents. - [10:16](https://www.youtube.com/watch?v=YokJh1BSFOw&t=616s) Adversarial risks such as prompt injections, jailbreaking, and data poisoning require sandboxing and MITRE/NIST-aligned defenses, alongside strict data fencing to comply with GDPR and the EU AI Act. - [13:11](https://www.youtube.com/watch?v=YokJh1BSFOw&t=791s) Federal Reserve SR 11-7 Model Risk Management standards apply to end-to-end agentic pipelines, necessitating independent conceptual soundness validation and staged champion-challenger canary deployments. - [16:20](https://www.youtube.com/watch?v=YokJh1BSFOw&t=980s) Enterprises must adopt a five-phase gated deployment playbook—spanning use case definition, sandboxing, multi-dimensional evals, shadow-mode red teaming, and dynamic HITL production rollout. - [18:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1109s) In high-stakes AML/KYC operations, hybrid LLM orchestration drastically cuts false-positive alert fatigue while ensuring mandatory SAR/CTR reporting compliance within strict regulatory deadlines. - [21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) Enterprise agentic architecture relies on four pillars: a Central LLM Gateway, real-time Observability, secure IAM-integrated RAG/MCP context layers, and CI/CD model evaluation pipelines. - [23:48](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1428s) To avoid single-model vendor lock-in and manage compute costs, organizations should use reusable evaluation pipelines to benchmark fine-tuned Small Language Models (SLMs) against frontier LLMs.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=4s) Human-Centric AI and Regulatory Governance in Finance

Opening with references to morning keynote remarks by AI pioneers like Geoffrey Hinton and Fei-Fei Li, the session underscores a foundational principle: AI must be built to empower humans, not the other way around. In heavily regulated domains like banking and financial services, adopting a "human-centric" paradigm is not merely philosophical—it is an operational and compliance necessity.

Rajendra Gangavarapu introduces his background, drawing on over 25 years of experience building enterprise AI and machine learning systems in secure financial environments, spanning credit underwriting, fraud detection, customer experience, marketing optimization, and sales enablement. Over the past 15 years, his work has interfaced directly with major domestic and international regulatory authorities, including the Federal Reserve Bank, the OCC, the FDIC, the Bank of England, and regulatory bodies across Europe and Asia. He also highlights his forthcoming book, *Mastering AI Governance*.

- 25+ years of financial services AI/ML engineering experience across fraud, credit decisioning, and customer intelligence. - 15+ years of direct engagement with global banking regulators (Fed, OCC, FDIC, Bank of England, European and Asian authorities).

---

[01:31](https://www.youtube.com/watch?v=YokJh1BSFOw&t=91s) The Shift to Agentic AI and the Imperative of Human-in-the-Loop

While generative AI witnessed a historic "watershed moment" with ChatGPT's rapid consumer democratization, 2025 marks the industry-wide transition toward Agentic AI. Unlike purely generative systems that produce text or images, agentic architectures plan multistep workflows, invoke external tools, execute transactions, monitor results, and generate structured reporting. However, fully autonomous execution in financial services is fundamentally non-viable; human oversight and rigid guardrails must govern every step.

```mermaid flowchart TD accTitle: Dual Paths of Agentic Execution in Financial Services subgraph Supervised["World's Largest Bank (Compliant Workflow)"] A1["24/7 Agentic Scanning: Markets & Feeds"] --> A2["Portfolio Rebalancing Recommendations"] A2 --> A3["Mandatory Human Approval"] A3 --> A4["Trade Execution & Audit Log"] end subgraph Unsupervised["Broker-Dealer Failure (Unchecked Workflow)"] B1["Autonomous Agent Direct Trading"] --> B2["Runaway Flawed Transactions"] B2 --> B3["Massive Client Losses"] B3 --> B4["Active Class-Action Lawsuit"] end ```

The contrast between governed and ungoverned deployment is stark. One of the world’s largest banks successfully launched an agentic AI system for portfolio rebalancing: agents continuously scan real-time market feeds and portfolio objectives around the clock, formulating stock and bond allocation adjustments to optimize risk-return trade-offs. Crucially, the system executes trades only after explicit human sign-off. Conversely, an unnamed broker-dealer allowed autonomous agents to execute trades directly without a human in the loop, resulting in catastrophic trading losses and an ongoing class-action lawsuit. In banking, "trust is critical"—once lost, it is nearly impossible to regain. Traceability, auditable logs, and clear lines of human ownership are required to satisfy scrutiny from the SEC, OCC, Federal Reserve, and FDIC.

- Core Agentic capabilities: Multi-step planning, tool-calling, autonomous execution, self-monitoring, and dynamic reporting. - Human-in-the-loop (HITL) requirement: Full traceability, explicit sign-offs, and verifiable accountability for regulatory auditability.

---

[05:01](https://www.youtube.com/watch?v=YokJh1BSFOw&t=301s) Core Technical Hurdles: Fragile Memory, Orchestration, and Hallucination

Deploying agentic systems in production reveals three primary technical vulnerabilities: fragile memory, multi-agent misalignment, and probabilistic hallucinations. Large language models inherently prioritize short-term conversational context, leaving long-term episodic memory brittle. A recent insurance claims deployment faltered because the agent failed to maintain historical claim context, causing significant processing breakdowns. While some platform providers tout "unlimited context" windows, brute-force context dumping often backfires into information overload; systems must selectively retrieve relevant historical facts rather than flooding the model with noisy data.

```mermaid flowchart LR accTitle: Key Technical Hurdles in Production Agentic Systems subgraph Hurdles["Core Production Bottlenecks"] H1["Fragile Memory<br/>(Short vs. Long-Term Context)"] H2["Agent Misalignment<br/>(Orchestration Gaps)"] H3["Probabilistic Hallucinations<br/>(Factual Drift)"] end subgraph Remedies["Mitigation Strategies"] R1["Intelligent Long-Term Context Retrieval"] R2["Supervisor Agents & Rigorous Orchestration"] R3["Intelligent RAG & Specialized Fine-Tuning"] end H1 --> R1 H2 --> R2 H3 --> R3 ```

Beyond memory constraints, inter-agent communication gaps represent a major operational risk: agents must be tightly aligned with one another before passing outputs to human reviewers. Finally, because LLMs remain probabilistic, they will attempt to answer every query even when uncertain. The widely publicized Air Canada chatbot refund case demonstrated the severe financial and legal repercussions of unchecked hallucinations, emphasizing why financial institutions must rely on advanced Retrieval-Augmented Generation (RAG), fine-tuning, and robust prompt boundaries.

- Memory tradeoff: Selective, relevant long-term context retrieval vs. brute-force "unlimited context" token dumping. - Hallucination safeguard: Enterprise RAG pipelines and domain-specific fine-tuning to bound probabilistic generative outputs.

---

[06:52](https://www.youtube.com/watch?v=YokJh1BSFOw&t=412s) Telemetry, Compounding Multi-Agent Errors, and Model Drift

Decades of mergers and acquisitions have left financial institutions with deeply fragmented data silos and scarce specialized AI engineering talent. Rather than waiting years and spending millions of dollars constructing monolithic enterprise data lakes, firms are leveraging agentic AI to bridge fragmented data pipelines and extract on-demand insights in real time. However, multi-agent architectures introduce compounding failure modes that make continuous telemetry indispensable.

```mermaid flowchart LR accTitle: Multi-Agent Error Compounding and Telemetry Alarms A1["Agent 1<br/>(99% Accuracy / 1% Error)"] --> A2["Agent 2"] A2 --> A3["Agent 5"] A3 -->|"Degradation Exceeds Tolerance"| Sup{"Supervisor Agent<br/>Telemetry Monitor"} Sup -->|"Trip Alarm & Halt"| Human["Human Intervention"] A3 --> A4["Agent 20<br/>(Compounded Error: 20-30%)"] ```

In an end-to-end chain of 20 interacting agents, an individual agent operating at an apparently acceptable 99% accuracy rate will pass its 1% error downstream. As intermediate outputs cascade from agent to agent, this error compounds exponentially, resulting in total degradation of 20% to 30% by the 20th agent. To prevent this, institutions like Goldman Sachs implement end-to-end telemetry across every phase of the lifecycle—data preprocessing, feature engineering, model training, deployment, and post-production inference. An overarching supervisor agent must monitor inter-agent drift, triggering automated alarms the moment output degradation breaches preset tolerance thresholds.

The financial danger of unmonitored drift is illustrated by Zillow’s iBuying collapse, where the company incurred a $550 million loss, shuttered a primary business unit, and laid off thousands of workers because its valuation models were trained on pre-COVID dynamics and failed to detect post-deployment real estate market shifts.

- Compounding error risk: A 1% baseline error at Agent 1 can amplify to a 20%–30% failure rate across a 20-agent chain. - Continuous telemetry: Full-lifecycle tracking from feature engineering through inference, backed by automated drift-detection alarms. - Zillow case study: $550M write-down and thousands laid off due to unmonitored model drift in shifting real-world distributions.

---

[10:16](https://www.youtube.com/watch?v=YokJh1BSFOw&t=616s) Adversarial Attacks, Model Security, and Global Data Privacy Regulations

Financial services represent the primary target for adversarial cyber threats. Vulnerabilities such as prompt injection and jailbreaking emerge almost immediately upon model release—exemplified by new foundation models facing exploits within 24 hours of launch. In addition to prompt injection, data poisoning in open-source pretraining datasets and tampering with open-source model weights present critical supply-chain risks. While open-source AI drives tremendous industry innovation, institutions must deploy rigorous sandboxing, independent internal and external model validation, and comprehensive data provenance audits to verify data origin and custodial integrity.

```mermaid flowchart TD accTitle: Security, Governance, and Data Privacy Controls subgraph SecurityFrameworks["Security & Threat Mitigation"] S1["MITRE ATT&CK & ATLAS Frameworks"] S2["NIST AI Risk Management"] S3["Pre- & Post-Production Red Teaming"] S4["Sandboxing & Provenance Audits"] end subgraph PrivacyRegulations["Regulatory & Jurisdictional Boundaries"] P1["GDPR & CCPA/CPRA Compliance"] P2["EU AI Act (Enforceable Aug 2026)"] P3["Strict Cross-Border Data Fencing (No EU data for US models)"] P4["Penalties up to 4% of Global Annual Turnover"] end ```

Institutions align their defenses with NIST guidelines and MITRE frameworks (such as MITRE ATT&CK and ATLAS), enforcing red teaming before deployment as well as continuously in production. On the data governance front, customer data is an institution's core "alpha," governed by strict privacy regimes including GDPR, CCPA/CPRA, and the European AI Act (taking full effect by August 2026). Non-compliance carries severe reputational fallout and fines of up to 4% of global annual turnover. Cross-border data transfers are strictly fenced; European customer data cannot be exported or utilized to train US models without triggering severe regulatory violations.

- Adversarial threats: Rapid jailbreaking, prompt injections, open-source data poisoning, and weight manipulation. - Security protocols: Sandboxing, rigorous MITRE/NIST-aligned defenses, and mandatory pre/post-deployment red teaming. - Privacy compliance: EU AI Act enforcement by August 2026, GDPR data residency boundaries, and fines reaching up to 4% of annual revenue.

---

[13:11](https://www.youtube.com/watch?v=YokJh1BSFOw&t=791s) Regulatory Compliance (SR 11-7), Fairness, and Champion-Challenger Pipelines

In US banking regulation, the Federal Reserve’s SR 11-7 guidance represents the gold standard for Model Risk Management (MRM). Originally established for traditional statistical models, SR 11-7 applies equally to machine learning, deep learning, generative AI, and agentic workflows. Regulators now require independent validation of "conceptual soundness" not just for the underlying foundation model, but across the entire system—including RAG retrieval pipelines, prompt engineering logic, and multistep agent orchestrations. In a recent regulatory examination of a midsize bank’s mortgage AI assistant, supervisors halted the deployment until the complete end-to-end agentic workflow underwent independent third-party validation.

```mermaid flowchart LR accTitle: Champion-Challenger Model Deployment and Rollback Traffic["Inbound Production Traffic"] --> Split{"Traffic Router"} Split -->|"90%-95% Volume"| Champion["Champion Model (Production)"] Split -->|"5%-10% Volume"| Challenger["Challenger Model (Shadow / Canary)"] Challenger -->|"Evaluate 3-6 Months<br/>(Accuracy + Fair Lending)"| Eval{"Performance Gate"} Eval -->|"Outperforms Champion"| Promote["Scale to 100% Traffic"] Eval -->|"Drift / Fair Lending Anomaly"| Rollback["Instant Rollback to Champion"] ```

Model optimization in finance requires a dual-objective framework balancing predictive accuracy with algorithmic fairness. Automated credit underwriting and line assignment systems must not discriminate against protected classes across race, ethnicity, gender, or age.

To mitigate production risk, new models enter a canary or shadow deployment using a champion-challenger structure: 1. **Initial Shadow Phase:** The challenger model runs in parallel, processing an initial 5% to 10% of production volume. 2. **Longitudinal Evaluation:** Over a 3- to 6-month evaluation window, performance is rigorously benchmarked against the champion across accuracy, stability, and fair lending metrics. 3. **Gradual Traffic Migration:** Traffic is incrementally ramped from 10% to 20%, 30%, and eventually 100% if the challenger consistently outperforms the incumbent. 4. **Automated Rollback:** Robust routing pipelines maintain instant failover capabilities to revert 100% of traffic back to the legacy model if anomalies or drift occur.

- SR 11-7 MRM Standard: Comprehensive, independent evaluation of conceptual soundness covering RAG pipelines, prompts, and agentic chains. - Dual-Objective Tuning: Simultaneous optimization for statistical predictive power and demographic fairness across protected classes. - Rollout protocol: Shadow/canary staging starting at 5%–10% traffic over 3–6 months with immediate rollback capability.

---

[16:20](https://www.youtube.com/watch?v=YokJh1BSFOw&t=980s) Human-in-the-Loop Orchestration and the Gated Production Playbook

As enterprise agent ecosystems expand from single assistants to multi-agent networks, clear operational boundaries become vital. Within the next 5 to 10 years, the global ratio of AI agents could swell to 10 billion or even 80 billion agents operating globally. To maintain control over this proliferation, human owners must explicitly define the strategic objectives, KPI thresholds, and operational guardrails within which agents are permitted to act.

```mermaid flowchart TD accTitle: Gated Agentic AI Production Playbook G1["1. Use Case & KPI Definition<br/>(Establish Operational Guardrails)"] --> G2["2. Sandbox Environment Testing<br/>(Synthetic & Historical Data)"] G2 --> G3["3. Multi-Dimensional Evaluations<br/>(Accuracy, Fairness, Privacy, Security)"] G3 --> G4["4. Red Teaming & Canary Deployment<br/>(Shadow Mode / 5-10% Traffic)"] G4 --> G5["5. Full Production Deployment<br/>(Real-Time Telemetry & Dynamic HITL)"] ```

Human-in-the-loop governance cannot be a static checkpoint tacked onto the final output; it must function as a dynamic, continuous feedback loop integrated into intermediate steps of the workflow. Before any agentic workflow reaches production, it must advance through a structured, gated deployment playbook: - **Phase 1 (Definition):** Explicitly define business use cases, operational constraints, and quantitative KPIs. - **Phase 2 (Sandboxing):** Isolate agents within a secure sandbox to stress-test multi-agent interactions. - **Phase 3 (Comprehensive Evals):** Subject workflows to multi-dimensional evaluations encompassing accuracy, bias/fairness, data privacy, and MITRE-aligned security protocols. - **Phase 4 (Red Teaming & Shadow Mode):** Conduct adversarial red teaming and deploy the agent in shadow mode on limited canary traffic (5%–10%). - **Phase 5 (Full Production & Telemetry):** Roll out to production backed by supervisor-agent telemetry and dynamic human review.

---

[18:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1109s) High-Stakes AML/KYC Transformation: DBS vs. Regulatory Sanctions

Anti-Money Laundering (AML) and Know Your Customer (KYC/KYCC) compliance represents one of the highest-stakes operational environments in banking. Mandated under the Bank Secrecy Act (BSA) and post-9/11 counter-terrorist financing rules, financial institutions face acute "AML fatigue." One global bank previously had to employ an army of 3,000 to 4,000 compliance analysts solely dedicated to manually investigating overwhelming volumes of false-positive transaction alerts.

```mermaid flowchart LR accTitle: AML False-Positive Reduction via LLM Orchestration Tx["Transaction & KYC Feeds"] --> Rules["Rule-Based AML Filter"] Rules --> LLM["LLM Orchestration Layer<br/>(Contextual Entity Resolution)"] LLM -->|"Drastic False-Positive Reduction"| Queue["Optimized Analyst Queue"] Queue --> FastSAR["Expedited SAR/CTR Filing<br/>(< 24-Hour Regulatory Window)"] ```

DBS Bank in Singapore modernized this process by integrating an LLM orchestration layer directly into its existing rule-based AML engine. This hybrid architecture drastically reduced false-positive rates while simultaneously boosting true-positive detection rates, freeing human analysts to focus on genuine financial crime risks. The operational speed of these systems is critical: banks are legally required to file Suspicious Activity Reports (SARs) and Currency Transaction Reports (CTRs) within strict regulatory timeframes—often under 24 hours—whenever accounts link to narcotics trafficking, terrorist organizations, or politically exposed persons (PEPs).

The cost of compliance failures in AML is severe: - **TD Bank:** Hit with over $1 billion in regulatory penalties for systemic anti-money laundering monitoring breakdowns and failure to file SARs on illicit transactions. - **HSBC:** Received historic multi-billion dollar penalties for AML and sanctions failures, which ultimately forced the bank to shut down and divest its extensive US retail banking branch network.

- Compliance overhead: Traditional AML operations frequently require 3,000–4,000 human analysts to triage massive false-positive backlogs. - DBS Bank benchmark: Hybrid LLM orchestration combined with rule engines drastically reduced false alarms while increasing true-positive detection. - Regulatory penalty benchmarks: Multi-billion-dollar enforcement actions and forced market exits (e.g., TD Bank, HSBC) for SAR/CTR reporting failures within mandatory 24-hour windows.

[20:11](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1211s) The High Stakes of AML Compliance and Empowering Human Analysts

Regulatory penalties in finance carry severe operational consequences. Failing to identify illicit transactions can lead to drastic enforcement actions where institutions have their assets frozen and are restricted to maintaining only a single correspondent banking branch in New York. While human oversight has traditionally served as the final line of defense, human analysts are also prone to errors and fatigue when processing massive transaction volumes. The objective of agentic AI is not to replace human judgment, but to empower analysts with automated intelligence capable of detecting increasingly sophisticated criminal behavior. Bad actors, from drug cartels to terrorist organizations, continuously design intricate schemes—such as breaking illicit funds into small, structured deposits and executing complex layers of fictitious transactions to obfuscate source funds.

Traditional rule-based compliance engines fail because criminal networks evolve rapidly and remain "always 10 steps ahead of the curve." Agentic workflows bridge this gap by analyzing subtle behavioral patterns across disparate data feeds. By elevating true positives and drastically reducing false-positive alert fatigue, agentic systems improve investigative efficiency and ensure that bad actors are caught and reported in a timely manner to regulatory bodies like the Financial Crimes Enforcement Network (FinCEN) and the Office of Foreign Assets Control (OFAC).

- **Failure of Rule-Based Systems**: Static rule engines cannot adapt quickly enough to new, multi-layered money laundering and obfuscation schemes. - **Regulatory Reporting**: Timely escalation and automated dossier assembly support mandatory compliance filings with regulators such as FinCEN and OFAC.

```mermaid flowchart TD accTitle: Agentic Financial Crime Detection Workflow subgraph Ingestion["1. Ingestion & Detection"] A["Transaction Feeds & External Data"] --> B["Agentic Detection Engine"] end subgraph Intelligence["2. Investigation & Human Oversight"] B --> C["True Positive Escalation & Pattern Analysis"] C --> D["Human Compliance Analyst Review"] end subgraph Filing["3. Regulatory Reporting"] D --> E["FinCEN / OFAC Filings"] end ```

---

[21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) Artigen AI's Four-Pillar Enterprise Architecture

Drawing on 25 years of experience working with tier-one banks, financial institutions, and regulators, the Artigen AI framework is designed around three foundational engineering principles: *security by design*, *observability by default*, and *scalable architecture*. This enterprise platform addresses the structural vulnerabilities of multi-agent deployments across four core components:

```mermaid flowchart TD accTitle: Enterprise Agentic AI Architecture subgraph Gateway["Central LLM Gateway"] GW["Policy & Regulatory Enforcement\n(NIST, MITRE, SR 11-7, EU AI Act)"] end subgraph Observability["Observability Layer"] OBS["Real-Time Monitoring\n(Accuracy, Bias, Fairness, Security, Privacy KPIs)"] end subgraph DataSecurity["Enterprise Data & Access Layer"] IAM["IAM Integration & RBAC"] RAG["Secure Custom RAG & MCP Connectors"] IAM --- RAG end subgraph ModelLayer["Model Evaluation & Pipeline"] PIPE["Reusable CI/CD Evaluation Pipelines\n(SLMs vs. Frontier LLMs, Fine-Tuning)"] end GW --> OBS OBS --> DataSecurity DataSecurity --> ModelLayer ```

The first pillar is a **Central LLM Gateway** that programmatically enforces governance policies and regulatory mandates across all model interactions, including the NIST AI Risk Management Framework, the MITRE ATLAS framework, Federal Reserve SR 11-7 supervisory guidance on model risk management, and the EU AI Act. The second pillar is an **Observability Layer** that tracks mission-critical KPIs in real time—monitoring model drift, output accuracy, algorithmic bias, fairness, security vulnerabilities, and privacy violations to instantly flag exceptions to compliance operators.

The third pillar governs **Data Security, IAM, and Contextual Retrieval**. By integrating directly with existing enterprise Identity and Access Management (IAM) infrastructures, the system maintains strict role-based access privileges. Leveraging custom Retrieval-Augmented Generation (RAG) and standardized Model Context Protocol (MCP) data connectors, enterprises can safely ground models on private internal assets without risking external data leakage. Emphasizing that "your data is your alpha," the architecture guarantees that proprietary enterprise data is never exposed or used to train or fine-tune third-party commercial foundation models.

The fourth pillar focuses on **Model Selection, Optimization, and CI/CD**. Rather than relying exclusively on massive frontier models—which are frequently more susceptible to prompt injection, jailbreaking, and high operating costs—organizations must deploy reusable CI/CD evaluation pipelines to systematically determine whether a specialized Small Language Model (SLM) or a larger LLM is the right fit for a specific task. Pairing fine-tuned SLMs with custom RAG pipelines controls hallucinations while optimizing latency and inference costs.

- **Regulatory & Governance Frameworks Enforced**: NIST AI RMF, MITRE ATLAS, Federal Reserve SR 11-7, and the EU AI Act. - **Core Design Pillars**: Security by design, observability by default, and a scalable multi-model backbone. - **Model Specialization**: Reusable CI/CD evaluation pipelines benchmark tasks against both SLMs and frontier LLMs to minimize jailbreak vulnerabilities and eliminate unnecessary compute overhead.

---

[23:48](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1428s) The Future of Agentic AI: Continuous Evaluation and Trust

Organizations cannot afford to wait for absolute technological perfection before adopting agentic AI, because the underlying technology is moving at breakneck speed. Rather than locking into or placing blind trust in a single proprietary foundation model, enterprises must establish automated, reusable evaluation pipelines that benchmark emerging models on an ongoing basis—continuously testing whether an updated Claude model, an OpenAI release, or a specialized open-weights model best serves a given workflow.

Scaling agentic innovation in enterprise finance ultimately hinges on governed, observable systems. Without rigorous guardrails and transparent oversight, agentic workflows introduce unmanageable model and operational risks. The defining competitive challenge for the next generation of financial technology is successfully solving "how do you balance autonomy with trust."

- **Avoid Single-Model Lock-In**: Continually evaluate performance across frontier providers (e.g., Anthropic Claude, OpenAI) and internal fine-tuned SLMs. - **Guardrails Precede Scale**: Innovation cannot safely scale across financial workflows without automated governance and real-time observability. - **Core Imperative**: Sustainable production deployment requires striking a calibrated equilibrium between agentic autonomy and verifiable human trust.

## Notable quotes > [00:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=4s) AI must be built to empower humans, not the other way around. — Rajendra Gangavarapu

> [01:31](https://www.youtube.com/watch?v=YokJh1BSFOw&t=91s) In banking, trust is critical—once lost, it is nearly impossible to regain. — Rajendra Gangavarapu

> [05:01](https://www.youtube.com/watch?v=YokJh1BSFOw&t=301s) While some platform providers tout unlimited context windows, brute-force context dumping often backfires into information overload; systems must selectively retrieve relevant historical facts rather than flooding the model with noisy data. — Rajendra Gangavarapu

> [10:16](https://www.youtube.com/watch?v=YokJh1BSFOw&t=616s) Customer data is an institution's core alpha, governed by strict privacy regimes including GDPR, CCPA/CPRA, and the European AI Act. — Rajendra Gangavarapu

> [13:11](https://www.youtube.com/watch?v=YokJh1BSFOw&t=791s) Regulators now require independent validation of conceptual soundness not just for the underlying foundation model, but across the entire system—including RAG retrieval pipelines, prompt engineering logic, and multistep agent orchestrations. — Rajendra Gangavarapu

> [16:20](https://www.youtube.com/watch?v=YokJh1BSFOw&t=980s) Human-in-the-loop governance cannot be a static checkpoint tacked onto the final output; it must function as a dynamic, continuous feedback loop integrated into intermediate steps of the workflow. — Rajendra Gangavarapu

> [20:11](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1211s) Traditional rule-based compliance engines fail because criminal networks evolve rapidly and remain always 10 steps ahead of the curve. — Rajendra Gangavarapu

> [21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) The Artigen AI framework is designed around three foundational engineering principles: security by design, observability by default, and scalable architecture. — Rajendra Gangavarapu

> [21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) Your data is your alpha, the architecture guarantees that proprietary enterprise data is never exposed or used to train or fine-tune third-party commercial foundation models. — Rajendra Gangavarapu

> [23:48](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1428s) The defining competitive challenge for the next generation of financial technology is successfully solving how do you balance autonomy with trust. — Rajendra Gangavarapu

## Mentioned Rajendra Gangavarapu, Artigen.ai, Geoffrey Hinton, Fei-Fei Li, Federal Reserve Bank, OCC, FDIC, Bank of England, SEC, Mastering AI Governance, ChatGPT, Air Canada, Goldman Sachs, Zillow, NIST AI Risk Management Framework, MITRE ATT&CK, MITRE ATLAS, GDPR, CCPA/CPRA, EU AI Act, SR 11-7, Bank Secrecy Act (BSA), DBS Bank, TD Bank, HSBC, FinCEN, OFAC, Model Context Protocol (MCP), Claude, OpenAI

## Video description Rajendra Gangavarapu, Advisor, Artigen.ai

Abstracts

Are you confident that your AI agents are truly ready for prime time in the high-stakes world of finance? In this session, I will tackle the toughest challenges, such as reliability breakdowns, data security risks, and the daunting demands of regulatory compliance that arise when deploying multi-agent AI systems at scale. I will share lessons on error compounding, orchestration, and why careful boundaries and human oversight remain essential. Attendees will learn best practices for architecting robust, compliant agentic workflows, along with practical strategies for integrating AI without sacrificing trust or control. Join me in discovering how to turn today’s risks into tomorrow’s competitive advantages while adopting Agentic AI.

[Source](https://www.youtube.com/watch?v=YokJh1BSFOw)

中文简报

Artigen表示：若缺乏人工监督，银行或面临30%的AI级联错误风险 除非金融机构根据SR 11-7模型风险标准实施持续遥测和强制性人工签批把关，否则多智能体工作流会将细微错误复合放大为系统性运营故障。 Gangavarapu探讨了银行业从生成式工具向自主多智能体系统的转变，强调缺乏人机协同（human-in-the-loop）治理的完全自主执行将引发灾难性的运营和监管风险。他阐明了基础智能体错误如何在多步骤链条中呈指数级复合放大，以及金融机构为何必须推行持续遥测、稳健的沙箱机制，并落实符合美联储SR 11-7与《欧盟人工智能法案》的严格模型风险管理。结合交易、房地产估值及AML/KYC调查等知名案例，本次演讲展示了设有把关机制的金丝雀部署方案和冠军-挑战者管线。会议最后介绍了Artigen AI的企业级四支柱框架——将中央策略网关、实时可观测性、安全IAM/RAG连接器与可复用的CI/CD评估管线相结合，以在智能体自主性与企业信任之间实现安全平衡。 金融领域正从生成式 AI 转向 Agentic AI，由于具备多步规划与自主执行能力，必须引入严格的 Human-in-the-Loop（人在回路）与审计追踪，以防范灾难性交易亏损与法律诉讼。 (1:31) 多智能体系统存在严重的误差复合效应，首个智能体 1% 的基础错误率在 20 个智能体链式传递后可能恶化至 20% 到 30%，必须依赖端到端全生命周期遥测与主管智能体（Supervisor Agent）进行漂移监控。 (6:52) 美联储 SR 11-7 模型风险管理标准现已覆盖全流程智能体管线，包括 RAG 与提示词逻辑；企业需采用涵盖沙盒测试、红队演练与灰度（Champion-Challenger）分流的五阶段门禁发布机制。 (13:11) 在反洗钱（AML/KYC）高风险场景中，混合 LLM 编排层能大幅削减误报率并提升真实阳性检出率，协助合规团队在严格法定时限内完成 SAR 和 CTR 报送。 (18:29) Artigen AI 提出涵盖中央 LLM 网关、可观测性层、数据安全 IAM 与 CI/CD 评估管线的四大支柱架构，通过兼顾专精小模型（SLM）与前沿大模型，平衡自主性与监管合规信任。 (21:29)

Reading summary

Drawing on over two decades of enterprise financial engineering, Rajendra Gangavarapu argues that deploying autonomous agentic AI in regulated finance requires strict human-in-the-loop controls, continuous telemetry, and rigorous model governance to prevent compounding errors and severe compliance failures. Autonomous Execution Versus Human-in-the-Loop Governance (1:31) As the financial sector moves from conversational generative AI to agentic systems capable of multistep planning, tool invocation, and automated execution, fully autonomous workflows remain non-viable. Gangavarapu contrasts two deployment models: one of the world's largest banks successfully operates a portfolio rebalancing system where agents scan market feeds around the clock but only execute adjustments after explicit human sign-off. Conversely, an unnamed broker-dealer permitted autonomous agents to trade without human checkpoints, resulting in runaway erroneous transactions, severe client losses, and an active class-action lawsuit. Because institutional trust is fragile, regulatory bodies such as the SEC, OCC, Federal Reserve, and FDIC demand strict traceability, immutable audit logs, and clear human ownership. Autonomous agents must operate within explicit operational boundaries rather than acting as unchecked proxies. Technical Bottlenecks and Compounding Multi-Agent Degradation (5:01) Production deployments face three primary technical vulnerabilities: brittle episodic memory, probabilistic hallucinations, and multi-agent misalignment. Large language models prioritize short-term conversational context over long-term recall; an insurance claims pilot failed precisely because the agent lost historical context. Gangavarapu warns that brute-force context dumping into expansive token windows causes information overload, necessitating selective Retrieval-Augmented Generation (RAG) and domain fine-tuning to prevent public failures like the Air Canada hallucinated refund dispute. In complex multi-agent architectures, small imperfections escalate dramatically. An individual agent operating at 99% accuracy passes a 1% error downstream; across a chain of 20 interacting agents, that degradation compounds to a 20% to 30% overall failure rate. To avoid catastrophic drift—such as the real estate model miscalculations that caused Zillow's $550 million write-down and extensive layoffs—institutions must implement end-to-end telemetry and supervisor agents that halt execution when performance breaches tolerance thresholds. Model Risk Management, Security, and Phased Staging (10:16) Financial systems face rapid adversarial threats, including prompt injections and jailbreaks that emerge within 24 hours of a model's release, as well as data poisoning in open-source pretraining datasets. Defenses must align with NIST guidelines and the MITRE ATT&CK and ATLAS frameworks. Concurrently, firms must comply with strict data sovereignty mandates, including GDPR cross-border data fencing and the European AI Act (enforceable by August 2026), which penalizes non-compliance with fines of up to 4% of global annual turnover. In the United States, the Federal Reserve's SR 11-7 Model Risk Management guidance requires independent validation of conceptual soundness across the entire agentic system—including prompts and RAG pipelines, not just base foundation models. Automated underwriting models must balance statistical predictive accuracy against demographic fairness across protected classes. Gangavarapu advises mitigating operational exposure using canary champion-challenger deployments: a challenger model runs in shadow mode on 5% to 10% of traffic over a 3- to 6-month evaluation period, backed by automated rollback controls before full rollout. Modernizing AML Operations Through Enterprise Architectures (18:29) Anti-Money Laundering (AML) and Know Your Customer (KYC) workflows represent high-liability environments where institutions face acute alert fatigue, historically requiring 3,000 to 4,000 analysts at a single global bank to triage false positives. Regulatory enforcement for compliance breakdowns is severe: TD Bank incurred over $1 billion in penalties for monitoring failures, while HSBC faced multi-billion-dollar penalties that forced the divestment of its US retail branch network. DBS Bank modernized this workflow by integrating an LLM orchestration layer with existing rule engines, dramatically reducing false alarms while accelerating mandatory Suspicious Activity Report (SAR) filings within strict 24-hour regulatory windows. To institutionalize these safeguards, Gangavarapu outlines the Artigen AI enterprise framework. The architecture relies on four pillars: a Central LLM Gateway enforcing policies like SR 11-7 and the EU AI Act; an Observability Layer tracking drift, fairness, and security metrics; an Enterprise Data Layer integrating Identity and Access Management with Model Context Protocol connectors to protect proprietary data; and CI/CD evaluation pipelines that benchmark tasks against specialized Small Language Models (SLMs) and frontier models to curb latency, compute costs, and jailbreak risks. With the global agent population projected to expand to between 10 billion and 80 billion over the next decade, Gangavarapu concludes that financial institutions must avoid proprietary vendor lock-in by maintaining reusable, automated benchmarking pipelines. The defining operational challenge remains balancing expanding agentic capabilities with observable guardrails and verifiable human trust.

精炼阅读摘要

金融领域的代理式人工智能（Agentic AI）正从实验阶段步入生产部署，但其全自主运行面临严重的合规与运营风险；Artigen AI 指出，机构必须通过“人在回路”、端到端可观测性与分阶段防护栏，在自动化效率与监管信任之间建立平衡。 从生成式迈向代理式：人在回路与失控风险 (1:31) 随着行业在2025年向具备多步骤规划、工具调用与执行能力的代理式AI演进，纯粹自主的交易和决策在金融严监管场景下被证明不可行。某大型银行通过代理系统实现24小时市场监控和资产配置建议，但严格将实际交易权限保留给人类签署；对比之下，某经纪交易商允许自主代理直接下单，导致严重交易亏损并引发集体诉讼。包括美联储、货币监理署（OCC）、联邦存款保险公司（FDIC）及英国央行在内的监管机构，均对系统的可追溯性与明确的人类责任链提出了硬性要求。 为满足合规审计，金融机构不能将“人在回路”（HITL）视作最终输出环节的形式化审查，而必须将其设计为嵌入工作流中间节点的动态反馈闭环。针对可能引发法律诉讼的概率性幻觉（如引发赔偿纠纷的加拿大航空聊天机器人案例），机构必须依靠高级检索增强生成（RAG）、领域微调以及严格的提示词边界来加以约束。 多代理技术瓶颈：级联误差、模型漂移与脆弱记忆 (5:01) 多代理架构在实际运行中面临长短期上下文断裂、协同脱节以及复合误差三大工程瓶颈。针对长期情境记忆脆弱的问题，盲目堆砌“无限上下文”窗口往往会导致信息过载，系统需要依赖智能事实检索而非无序灌入冗余数据。此外，在长链条的多代理系统中，单体代理看似理想的99%准确率会向下游层层传递1%的误差，演进到第20个代理时，累计失效退化率可放大至20%到30%。 缺乏全生命周期遥测还会加剧模型漂移的财务风险。例如，Zillow的iBuying业务由于算法模型严重依赖疫情前的数据分布，未能及时识别部署后的市场动态变化，最终造成5.5亿美元资产减记、主要业务关停及数千名员工被裁。因此，像高盛等机构均要求在特征工程、模型训练至推理阶段建立端到端遥测，并通过主管代理（Supervisor Agent）在输出退化突破阈值时自动触发报警并中断执行。 对抗安全防御与严格的跨境数据隐私合规 (10:16) 金融系统作为网络对抗的首要目标，在新基座模型发布24小时内即面临越狱与提示注入攻击，开源模型预训练数据集投毒和权重篡改同样构成严重的供应链隐患。金融机构需对齐NIST人工智能风险管理框架与MITRE（ATT&CK及ATLAS）标准，在模型投产前后执行常态化红队对抗演练、沙盒隔离和严格的数据出处审计。 在数据要素治理方面，客户数据被视作机构的核心超额收益来源（Alpha），受到GDPR、加州CCPA/CPRA以及即将于2026年8月全面实施的《欧盟人工智能法案》严格约束。违规行为最高可面临全球年营业额4%的重罚。跨国机构必须执行严格的数据物理隔离与围栏管理，严禁违规将欧洲客户数据导出用于训练部署在美国本土的模型。 SR 11-7模型风险管理与冠军-挑战者发布机制 (13:11) 美联储发布的SR 11-7模型风险管理指南，已从传统统计模型全面延伸适用于包含RAG检索管道、提示词工程逻辑及多步骤代理编排在内的整套系统。在实际监管审查中，曾有中型银行因抵押贷款AI助手的端到端代理工作流未通过独立第三方的“概念健全性”验证而被迫叫停部署。信贷审批等系统还必须在预测准确性与受保护人群（如种族、性别、年龄）的算法公平性之间达成双重优化。 为防范线上风险，生产发布通常采用受控的“冠军-挑战者”（Champion-Challenger）金丝雀分流管道：挑战者模型初期仅承接5%至10%的线上影子流量；在经过长达3至6个月的横向周期评估后，若其在稳定性、精度及公平借贷指标上持续优于既有模型，流量才会逐步阶梯式提升至100%；一旦监测到数据漂移或公平性异常，路由系统可立即执行全量回滚。 高风险反洗钱转型与Artigen AI四支柱架构 (18:29) 在反洗钱（AML）和了解你的客户（KYC/KYCC）领域，传统规则引擎无法应对犯罪组织多层化分散存取的洗钱套路，常迫使单家跨国银行动用3000至4000名分析师人工排查误报。若违反《银行保密法》并错过24小时可疑活动报告（SAR）申报窗口，将面临严厉制裁——道明银行（TD Bank）因监控失效被处以逾10亿美元罚款，汇丰银行（HSBC）更因反洗钱与制裁合规崩塌遭受数以十亿美元计的历史性重罚并最终剥离其美国零售分支网络。新加坡星展银行（DBS）通过将大语言模型编排层接入规则引擎，大幅压缩误报率并提升真实风险捕获率。 针对此类金融场景，Artigen AI提出了四大架构支柱：一是集成NIST、SR 11-7和欧盟法案规则的中央LLM网关；二是实时监控漂移、偏差与安全事件的可观测层；三是绑定企业身份与访问管理（IAM/RBAC）、确保专有数据绝不泄露给第三方模型的安全数据与上下文检索层；四是建立可复用的CI/CD评估管道，动态权衡微调专用小语言模型（SLM）与前沿通用大模型，从而兼顾推理成本、幻觉抑制与对抗鲁棒性。 该报告强调，未来五至十年全球AI代理规模预计将膨胀至百亿级别，金融机构既不能盲目等待技术的绝对完美，也不应绑定于单一专有大模型提供商。如何在构建可复用CI/CD评估管道与自动化监控防线的同时，平衡代理系统的自主执行度与监管审计信任度，是金融AI落地的核心挑战。

Detailed notes — zh

## Background 该内容由行业峰会频道 Ai4 于 2025 年 8 月 28 日在 YouTube 平台发布，由 Artigen.ai 顾问兼资深金融 AI 治理专家 Rajendra Gangavarapu 主讲。页面介绍指出，本场演讲重点探讨金融高风险场景下 Agentic AI 的可靠性故障、数据安全风险与严苛监管合规要求，并分享如何通过人在回路与工程化护栏将试点项目稳步推向生产环境。 ## TL;DR 演讲围绕金融机构落地 Agentic AI 展开，指出系统面临脆弱记忆、级联误差放大以及模型幻觉等核心技术瓶颈，单纯依靠超长上下文或完全自主化并不可行。主讲人结合汇丰银行反洗钱重罚、Zillow 房产定价漂移巨亏及加航客服败诉等真实案例，阐明了在投资再平衡与信贷审批中设置人在回路（HITL）、模型独立验证（SR 11-7）及 Champion-Challenger 灰度发布的重要性。最后，他系统介绍了 Artigen AI 提出的中央 LLM 网关、默认可观测性及安全 RAG/MCP 架构，强调企业唯有在坚固护栏下平衡自主性与信任，方能实现金融级规模化落地。 ## Atmosphere & style 整场演讲呈现出一种深谙强监管内幕的专业度与清醒沉稳的警示感。主讲人凭借 25 年的银行从业履历与面对美联储、OCC 的交道经验，不讲空泛的行业黑话，而是用数亿美元罚单和业务叫停的惨痛事实打破对 AI 全自主化的盲目迷信。台风务实且充满工程底气，将前沿智能体浪潮拉回到金融审计底线与风控治理的现实重力之中。 ## Key takeaways - [01:31](https://www.youtube.com/watch?v=YokJh1BSFOw&t=91s) 金融级 Agentic AI 必须以人为本（Human-Centric），绝不能追求 100% 的完全自主化，必须在每个核心环节设置人在回路（HITL）与安全护栏以确保可追溯性与合规问责。 - [05:01](https://www.youtube.com/watch?v=YokJh1BSFOw&t=301s) 智能体面临脆弱记忆（过度依赖短期上下文）、多智能体编排对齐断层以及底层概率幻觉三大核心技术痛点，盲目扩增上下文窗口无法解决信息过载问题。 - [06:52](https://www.youtube.com/watch?v=YokJh1BSFOw&t=412s) 级联多智能体流水线存在严重的误差累积放大效应（Error Compounding），即使单体准确率达到 99%，20 个智能体级联后误差可能膨胀至 20%~30%，必须引入主管智能体（Supervisor Agent）进行全局遥测拦截。 - [06:52](https://www.youtube.com/watch?v=YokJh1BSFOw&t=412s) Zillow 因定价模型发生现实数据分布漂移（Drift）却未设有效监控预警，导致 5.5 亿美元巨额亏损并关停核心业务，成为金融与资产定价 AI 的重大前车之鉴。 - [10:16](https://www.youtube.com/watch?v=YokJh1BSFOw&t=616s) 金融领域面临严峻的提示词注入、越狱和数据投毒等对抗性攻击风险，开源模型与数据必须审慎使用，数据主权与数据管理（Data Stewardship）构成了金融机构的核心超额收益（Alpha）。 - [12:19](https://www.youtube.com/watch?v=YokJh1BSFOw&t=739s) 美联储 SR 11-7 模型风险管理指引对 AI 的监管审查范围已从单一核心模型扩展至 RAG 架构、提示词工程及端到端工作流，任何环节违规都可能导致系统被叫停。 - [14:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=844s) 信贷审批等金融决策模型必须兼顾准确率与算法公平性感知（Fairness-aware），且生产上线需遵循严格的冠军/挑战者（Champion-Challenger）灰度测试与持续监控机制。 - [16:20](https://www.youtube.com/watch?v=YokJh1BSFOw&t=980s) 智能体投产需严格遵循标准化门禁手册：从用例定义、沙箱测试、鲁棒性与攻防评测（Red Teaming），再到影子模式部署与全生命周期动态闭环监控。 - [18:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1109s) 传统反洗钱（AML）饱受高误报率与人工疲劳折磨，引入 LLM/Agentic 编排层能有效压制误报并及时识别复杂洗钱拓扑，避免如道明银行、汇丰银行等面临的数十亿美元合规天价罚单。 - [21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) Artigen AI 提出依托中央策略网关、默认可观测性层、安全 RAG/MCP 集成及 SLM/LLM 动态路由构建企业级安全底座，主张在很多垂直场景优先采用更可控经济的小语言模型。 - [23:56](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1436s) 切勿盲目信任或深度绑定单一基础大模型，应通过可复用的流水线进行持续基准评估，在架构上实现解耦与动态优选。 - [23:56](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1436s) 金融领域规模化创新必须以坚固的护栏与可观测性为前提，Agentic AI 落地成熟的核心命题在于有效平衡智能体的自主性与人类的信任（Autonomy vs. Trust）。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=4s) 金融领域以人为本的 Agentic AI 与演讲者背景 会议开篇呼应了当天早些时候 Geoffrey Hinton 与李飞飞（Fei-Fei Li）关于“以人为本的 AI（Human-Centric AI）”的讨论。AI 的核心初衷是赋能人类，而非本末倒置；在银行业与金融服务业等强监管行业，以人为本的设计更是生死攸关。演讲者 Rajendra Gangavarapu 介绍了自己过去 25 年在银行与金融服务领域的从业经历，他长期致力于在安全可控的企业级环境中构建 AI/ML 模型，涵盖信贷审批（Credit Underwriting）、反欺诈（Fraud）、客户体验、营销模型及销售赋能等多个关键场景。 在过去 15 年中，Rajendra 广泛对接了国内外各类金融监管机构，包括美国联邦与州一级的监管机构（如美联储 Federal Reserve Bank、货币监理署 OCC、联邦存款保险公司 FDIC），以及英国央行（Bank of England）和欧洲、亚洲的多国监管部门。此外，他撰写的新书《Mastering AI Governance》即将在 Amazon 和 Barnes & Noble 正式发售。 - **行业深耕**：25 年金融与银行服务经验，主导信贷、风控、营销等多项企业级 AI 模型落地。 - **监管合规经历**：15 年与美联储（Fed）、OCC、FDIC、英格兰银行（BoE）及亚欧监管机构打交道的合规治理经验。 - **专著**：《Mastering AI Governance》（即将出版发行）。 --- ### [01:31](https://www.youtube.com/watch?v=YokJh1BSFOw&t=91s) 从 GenAI 到 Agentic AI：人在回路与合规问责 2025 年，“Agentic（智能体化）”已成为整个行业最炙手可热的关键词。从最初以 ChatGPT 为代表的生成式 AI（GenAI）掀起破圈浪潮、快速下沉普及至各行各业，到如今 Agentic AI 的兴起，AI 的角色已经从单纯的“文本生成”跃升至自主规划（Plan）、调用合适工具（Tool Calling）、执行操作（Execute）以及持续监控与报告。然而，在金融场景下，绝不能追求 100% 的完全自主化，必须在每个流程节点设置护栏（Guardrails）与人在回路（Human in the Loop, HITL）。 演讲者对比了近期发生的两个真实案例：全球某头部大型银行部署了用于投资组合再平衡（Portfolio Rebalancing）的 AI 智能体，该智能体 7x24 小时持续扫描市场行情与突发事件，结合投资目标与投资策略生成标的调整建议；但该行严格规定所有交易必须经由人工审批后方可执行，因而运转稳健。相反，另一家券商（Broker-Dealer）在没有设置人在回路的情况下，放任智能体自主执行大量交易，导致客户遭受巨额亏损，目前正面临严重的集体诉讼（Class Action Lawsuit）。在金融行业，信任一旦崩塌便极难重建，因此全链路操作的可追溯性（Traceability）、明确的审批责任归属以及迎战 SEC、OCC、美联储、FDIC 严格审计的能力，是 Agent 落地不可逾越的红线。 ```mermaid flowchart TD accTitle: 投资组合再平衡与人在回路治理架构 accDescription: 展示金融级 Agentic AI 在投资组合管理中结合市场扫描与人工审批的闭环流程。 subgraph Monitoring ["7x24 智能监控与分析"] A["市场数据源 & 实时事件"] --> B["Agentic AI 分析引擎"] C["投资目标 & 风险偏好"] --> B end subgraph Decision ["决策生成与风控拦截"] B --> D["生成调仓/交易建议"] D --> E{"人在回路 (HITL) 审批"} end subgraph Execution ["执行与全链路审计"] E -- "人工驳回 / 调整" --> B E -- "人工核准" --> F["执行投资组合交易"] F --> G["审计日志 & 溯源存证 (SEC/OCC/Fed)"] end ``` --- ### [05:01](https://www.youtube.com/watch?v=YokJh1BSFOw&t=301s) 智能体落地的三大核心痛点：脆弱记忆、对齐与幻觉 在实施 Agentic AI 方案时，企业面临着几大独特的技术挑战： 1. **脆弱的记忆机制（Fragile Memory）**：当前的 LLM 与智能体高度依赖短期上下文（Short-term Memory），对历史与长期上下文（Long-term Context）的捕捉能力非常薄弱。某保险公司在理赔自动化（Claims Processing）场景中，正是由于智能体无法获取历史关键上下文，导致大量理赔案件处理出错。盲目追求超长上下文窗口（如 Perplexity 等推出的无限上下文）并非灵丹妙药，向模型无脑灌入海量信息只会造成过载与噪音，如何精准筛选出“最相关的高质量长期上下文”才是破局关键。 2. **通信与对齐断层（Communication Gap & Alignment）**：在智能体将最终决策交付给人类之前，多智能体（Multi-Agent）框架内部各成员之间能否高效编排（Orchestration）与精准对齐，是决定系统稳定性的前置条件。 3. **模型幻觉（Hallucination）**：由于 LLM 底层的概率统计特性，模型倾向于对所有问题“不懂装懂”。加拿大航空（Air Canada）因客服机器人胡乱承诺退票政策而败诉并引发全网舆论危机的惨痛教训即是明证。业界目前主要通过 RAG（检索增强生成）、智能微调（Fine-Tuning）与优化框架来遏制此类风险。 --- ### [06:52](https://www.youtube.com/watch?v=YokJh1BSFOw&t=412s) 金融场景的适配价值：破除数据孤岛与级联误差控制 金融机构在经历多年的并购重组（M&A）后，普遍沉淀了庞大而割裂的数据孤岛（Siloed Data）与职能壁垒，部门协同极其困难；与此同时，全球正处于激烈的顶级 AI 人才争夺战中，专业人才极度匮乏。在此背景下，Agentic AI 成为连接数据孤岛的高效桥梁——企业无需再耗资数百万美元、耗时数年去搭建传统企业级数据湖（Data Lake），而是能够直接利用智能体按需、敏捷地生成跨系统业务洞见。 为了保障可靠性，全生命周期的遥测机制（Telemetry）不可或缺。例如高盛（Goldman Sachs）在数据预处理、特征工程、模型开发、部署到后期监控的每个环节都嵌入了遥测与强管控机制。演讲者着重剖析了**误差累积放大（Error Compounding）**现象：假设一个业务链路串联了 20 个 Agent，Agent 1 的单体准确率为 99%，看似很高，但经过层层传递，那 1% 的偏差传导至第 19 或第 20 个 Agent 时，整体误差可能被放大至 20% 到 30%。因此，不仅需要单体监控，还必须引入全局视角的“主管智能体（Supervisor Agent）”，一旦在 Agent 5 到 Agent 6 之间发现指标漂移（Drift）超出容忍阈值，便立即发出告警并阻断传递。 演讲者以 Zillow 的惨痛教训为例：Zillow 几年前因房产定价 AI 模型发生严重漂移，直接导致 5.5 亿美元的巨额亏损，被迫关闭整个 iBuying 业务线并裁员数千人。其核心诱因在于模型训练采用的是疫情前的数据，上线后面临现实分布漂移却未能及时识别和预警。 ```mermaid flowchart LR accTitle: 级联多智能体误差放大与主管监控架构 accDescription: 展示多智能体串联执行时误差放大的风险，以及主管智能体如何进行跨节点遥测拦截。 subgraph MultiAgentPipeline ["智能体流水线 (误差级联风险)"] A["Agent 1<br>(99% 准确率)"] -->|"输出传导 (1% 误差)"| B["Agent 2"] B -->|"... 层层累积"| C["Agent 5 -> Agent 6<br>(出现漂移)"] C -->|"... 误差放大至 20%-30%"| D["Agent 20<br>(高风险输出)"] end subgraph Oversight ["全局治理与遥测监控 (Telemetry)"] S["主管智能体 (Supervisor Agent)"] -.->|"实时监控准确率"| A S -.->|"捕获容差超标 & 阻断"| C S ==>|"触发告警 / 介入"| D end ``` - **误差放大效应**：单 Agent 99% 的准确率，在 20 个 Agent 级联后，末端误差可膨胀至 20%~30%。 - **Zillow 漂移案例**：因训练数据与现实环境脱节导致模型漂移，造成 5.5 亿美元亏损、业务关停及数千人被裁。 --- ### [10:16](https://www.youtube.com/watch?v=YokJh1BSFOw&t=616s) 安全对抗与数据治理：提示词注入、投毒与非协商性合规 金融机构始终是网络对抗性攻击（Adversarial Attacks）的首要目标。在 Agentic AI 落地过程中，安全与合规治理属于绝对不可妥协的非协商性要求（Non-negotiable Requirements）： - **对抗性注入与越狱（Prompt Injection & Jailbreaking）**：即便如 GPT-5 这类前沿模型，在发布后不足 24 小时内即遭遇越狱和提示词注入攻击。 - **数据投毒与开源风险（Data Poisoning）**：开源数据集若未经极其严格的审查，极易被恶意攻击者注入有害数据；开源模型的权重也存在被篡改的潜在风险。演讲者强调，虽然开源社区赋能了所有人，但必须“谨慎使用（Use with caution）”。 - **沙盒与多重验证**：在模型正式上线前，必须执行严格的沙盒隔离（Sandboxing）、内部验证（Internal Validation）与外部验证（External Validation）。 - **数据来源与数据管理（Data Provenance & Stewardship）**：必须明晰“数据来自何处、可信度如何、最后一次由谁验证”，确立明确的数据管家（Data Stewards）责任制。 - **安全评估框架与红队演练**：主流金融机构正普遍采用 MITRE 与 NIST 风险管理框架，在模型部署前（Pre-deployment）及投产后（Post-production）进行全生命周期的常态化红队对抗演练（Red Teaming）。 演讲者最后强调：“数据就是你的超额收益（Your data is your alpha）”，随着全球各类合规监管政策的密集出台，数据隐私与数据治理将成为金融智能体系统的底层支柱。 ### [12:19](https://www.youtube.com/watch?v=YokJh1BSFOw&t=739s) 数据隐私监管与金融模型合规要求 在数据隐私与人工智能合规领域，监管环境正迅速收紧。诸如 GDPR、CCPA 等法律早已生效，而《欧盟人工智能法案》（EU AI Act）也计划于 2026 年 8 月全面实施。任何数据隐私的泄露与违规，都可能给企业带来灾难性的声誉风险，并面临高达全球年营业额 4% 的巨额罚款。以 GDPR 和欧盟相关法规为例，它们明确禁止将欧洲用户的数据用于训练部署在美国的模型，必须建立极其健全的护栏（Guardrails），且严禁数据未经授权跨司法管辖区流动。数据血统（Data Lineage）追踪与数据隐私影响评估（DPIA）已成为不可或缺的底层合规动作。 在银行与金融服务行业中，美联储的 SR 11-7 监管指引被视为模型风险管理（MRM）的“黄金标准”，无论底层采用的是传统机器学习、深度学习，还是生成式 AI 与 Agentic AI，都必须严格遵守。近期有一家中型银行推出了按揭贷款 AI 助手（Mortgage AI Assistant），在面对监管审查时遭遇重大波折。SR 11-7 要求进行独立的模型验证（Independent Validation），定期评估模型的概念健全性（Conceptual Soundness）并确保其符合既定业务目标；上线前不仅需要独立验证，还必须通过第三方审计。在最新的监管审查中，监管机构直接叫停了该按揭 AI 系统的推进，原因在于监管层期望评估的范围不再仅限于核心模型本身，而是将检索增强生成（RAG）框架、提示词工程（Prompt Engineering）以及端到端的工作流全都纳入独立评估的边界内。 ```mermaid flowchart TD accTitle: 金融 AI 系统的监管审查范围扩展 subgraph RegulatoryScope["监管审查覆盖范围 (SR 11-7 标准)"] A["核心模型评估<br/>(概念健全性 / 准确性)"] B["RAG 检索增强架构<br/>(知识库与检索链路)"] C["提示词工程<br/>(Prompt Engineering)"] D["端到端智能体工作流<br/>(Agentic Workflow)"] end A --> E["独立合规验证 & 第三方审计"] B --> E C --> E D --> E E --> F{"是否准予生产上线"} F -- 满足标准 --> G["投入生产运营"] F -- 存在漏洞 --> H["监管叫停整改"] ``` - **GDPR 与 EU AI Act 处罚**：隐私合规违规罚款最高可达企业全球年营业额的 4%。 - **数据本地化限制**：严禁将欧盟受保护数据直接跨司法管辖区传输用于美区模型训练。 - **SR 11-7 审查广度**：监管要求从单一模型扩展至 RAG 架构、提示词及全链路智能体编排。 --- ### [14:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=844s) 算法公平性、变更控制与 Champion-Challenger 部署测试 银行业监管不仅要求模型具备高准确率，还严令模型必须做到公平、无偏见。当将 AI 模型应用于信贷审批（Credit Underwriting）和贷款决策时，系统往往在极少甚至没有人工干预的情况下决定批准、拒绝、授信额度（Line Assignment）及风险定价。在这一过程中，模型绝对不能针对受保护群体（如基于种族、性别、年龄等特征）产生歧视。因此，在对候选模型进行正则化与优化时，团队必须将“准确性”与“公平性感知（Fairness-aware）”作为双重优化目标，缺一不可。 变更控制（Change Control）与持续监控在运维中同样生死攸关。系统必须清晰记录“谁做出了变更”以及“谁批准了变更”。讲者再次提到 Zillow 曾因缺乏持续监控导致 5.5 亿美元的巨额资产减值并被迫关停核心业务，遭受了巨大的声誉毁损。为了规避上线风险，新模型必须通过严格的 A/B 测试机制：先在影子模式（Shadow Mode）下运行，随后以“冠军/挑战者”（Champion/Challenger）架构切入，初始仅分配 5% 到 10% 的生产流量。如果挑战者模型的表现持续优于生产环境中的冠军模型，再逐步将流量提升至 20%、30%，在 3 到 6 个月的周期内充分验证后，方可实现 100% 全量迁移。同时，底层必须具备稳健的流水线支持一键回滚（Rollback）与流量智能重定向，以从容应对市场环境与动态变化的冲击。 - **信贷双重目标**：模型调优必须同时最大化准确率并满足反歧视合规（Fairness-aware）。 - **Zillow 前车之鉴**：缺乏持续监控与异常拦截导致 5.5 亿美元损失与业务关闭。 - **灰度发布周期**：从 5%～10% 流量起步，经 3 至 6 个月验证逐步替代旧模型，支持快速回滚。 --- ### [16:20](https://www.youtube.com/watch?v=YokJh1BSFOw&t=980s) 人机协同（Human-in-the-Loop）与智能体门禁发布体系 人机协同（Human-in-the-Loop）是构建可靠智能体系统的核心。人类专家负责定义战略目标、合规护栏、核心 KPI 以及模型有效性的边界，智能体则在预设的护栏内自主运转、生成可落地的洞见并制定解决方案。人类在关键节点对建议进行审批。例如在投资组合动态再平衡（Portfolio Rebalancing）中，AI 给出调仓建议后，必须经由人工审核批准，交易才会被正式执行。 随着智能体规模爆发式增长——正如大会早间主题演讲所提到的，全球 80 亿人口未来可能伴随着 100 亿甚至 800 亿个 AI 智能体——整个交互必须采用动态闭环反馈（Dynamic Feedback Loop），在流程的每一步实时捕获反馈以遏制误差复合传递。为了将 Agentic AI 安全推向生产，必须遵循标准化的门禁发布手册（Gated Playbook）： ```mermaid flowchart LR accTitle: 智能体生产发布门禁流程 subgraph Phase1["阶段一：定义与沙箱"] P1["定义用例与 KPI"] --> P2["沙箱环境测试"] end subgraph Phase2["阶段二：评估与红队"] P3["鲁棒性评测<br/>(准确/偏见/隐私/安全)"] --> P4["红队攻防测试<br/>(Red Teaming)"] end subgraph Phase3["阶段三：灰度与监控"] P5["影子模式试运行"] --> P6["正式投产发布"] --> P7["全生命周期持续监控"] end P2 --> P3 P4 --> P5 ``` - **智能体爆发预测**：未来 5 到 10 年内，全球智能体数量预计将膨胀至 100 亿至 800 亿个。 - **逐步门禁机制**：业务 KPI 定义 $\rightarrow$ 沙箱测试 $\rightarrow$ 多维评测（准确/偏见/安全） $\rightarrow$ 红队攻防 $\rightarrow$ 影子部署 $\rightarrow$ 生产持续监控。 --- ### [18:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1109s) 反洗钱（AML）实战：从人力疲劳到 LLM 赋能 反洗钱（AML）以及“了解你的客户”（KYC）与“了解你客户的客户”（KYCC）是“9·11”事件后所有银行在《银行保密法》（BSA/AML）约束下的强制合规底线。传统银行饱受“反洗钱警报疲劳（AML Fatigue）”的折磨。讲者分享了自己曾在一家大型跨国银行工作的经历，该行当时为了人工核查海量的误报（False Positives），不得不雇佣了整整 3,000 到 4,000 名全职员工。 相比之下，新加坡星展银行（DBS）等先锋机构将大语言模型（LLM）编排层与传统的基于规则（Rule-based）的 AML 框架深度整合，大幅降低了误报率，同时显著提升了真正可疑交易（True Positives）的捕获率。合规对于时效的要求极高，可疑交易报告（SAR）与大额现金交易报告（CTR）必须在法定严格时限内（如 24 小时内）完成报送。如果未能及时识别并上报涉及恐怖组织、毒贩或政治敏感人物（PEP）的洗钱网络，银行将面临天价罚单： - **近期处罚案例**：道明银行（TD Bank）因未能有效捕获真实洗钱行为、漏报 SAR 而被处以 10 亿美元巨额罚款； - **历史极端案例**：汇丰银行（HSBC）数年前同样领到 10 亿美元级罚单，更被迫变卖在美的大量零售业务与资产，从全美排名前十的银行萎缩至仅获准在纽约保留一家代理行业务网点。 传统的毒贩和洗钱分子手段极为老练，擅长将贩毒收益拆分成微小金额，通过多层虚构交易进行分层（Layering）以混淆资金来源，传统规则引擎永远落后于犯罪手段 10 步以上。引入 Agentic AI 不仅解放了合规审查员的生产力，还能抢在犯罪分子前面识别新型洗钱拓扑，及时向 FinCEN（美国金融犯罪执法局）和 OFAC（美国财政部海外资产控制办公室）等监管机构完成报送。 --- ### [21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) Artigen AI 架构：安全网关、可观测性与敏捷落地 讲者在金融合规与大模型领域深耕 20 余年后创立了 Artigen AI，致力于将过去 25 年应对监管机构与大型金融机构的经验凝聚成一套高可扩展的企业级技术框架。该架构由四大核心支柱构成： 1. **中央 LLM 网关（Central LLM Gateway）**：统一策略执行点，原生嵌入 NIST AI 风险管理框架、MITRE 威胁框架、美联储 SR 11-7 以及欧盟 AI 法案等合规规则。 2. **默认可观测性层（Observability Layer）**：全天候实时监控准确性、公平性、偏见、安全渗透和数据隐私等指标，对偏离阈值的异常实时报警，并与企业现有的 IAM（身份与访问管理）深度集成，保障权限最小化。 3. **安全 RAG 与 MCP 数据集成**：依托模型上下文协议（MCP）和企业定制 RAG 框架，构建严格的数据隔离屏障，确保企业敏感资产绝不泄露给外部大模型提供商用于训练或微调。"Your data is your alpha"（数据就是你的超额收益来源），保护数据主权是维护企业核心竞争力的前提。 4. **灵活的模型选型与 CI/CD 编排**：体量越大的通用大模型越容易暴露攻击面、遭遇越狱（Jailbreak）或带来高昂成本。在很多金融垂直场景中，经过针对性微调的小语言模型（SLMs）往往更高效、可控且经济。架构基于可复用的 CI/CD 流水线，根据具体场景在 SLM、LLM 与 RAG 之间动态优选，最大程度压制幻觉。 ```mermaid flowchart TD accTitle: Artigen AI 企业级架构组件 Client["业务应用与智能体交互端"] --> Gateway["1. 中央 LLM 网关<br/>(NIST / MITRE / SR 11-7 / EU Act 策略强制执行)"] Gateway --> Obs["2. 全局可观测性层<br/>(实时指标监控 / IAM 权限整合 / 异常拦截)"] Obs --> Engine{"3. 动态模型调度与路由"} Engine --> SLM["定制微调小语言模型 (SLMs)"] Engine --> LLM["通用前沿大语言模型 (LLMs)"] Engine --> MCP["4. 安全 RAG & MCP 上下文层<br/>(企业私有数据主权隔离)"] ``` 最后，讲者呼吁金融从业者在面对 Agentic AI 时切勿陷入“等待技术完美无瑕”的误区。AI 技术演进一日千里，应当坚持“架构设计即安全（Security by Design）”和“默认可观测（Observability by Default）”的原则，在受控的护栏下快速启动试点并迭代演进。 ### [23:56](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1436s) 模型迭代、治理护栏与自主性平衡：金融智能体规模化的终极法则 随着底层大模型日新月异的技术迭代——从 GPT-4 迈向未来的 GPT-5 等更高级形态——技术选型绝不能固步自封。演讲者明确告诫道："不要盲目信任任何单一模型（Don't trust a given model）"。面对飞速演进的 AI 格局，企业必须构建具备高度复用性的技术流水线（Reusable pipelines），并辅以严格的持续评估（Continuous evaluations）机制。无论是在具体金融场景中衡量 Anthropic 的 Claude 更契合，还是 OpenAI 的前沿模型（如 GPT-5）更胜一筹，企业都必须依靠动态评测来筛选出最合适的基础模型，而非将业务逻辑死锁在某个特定供应商或模型上。 更为关键的基石在于系统的合规治理与可观测性。演讲者极力强调，必须“从受治理且具备可观测性的系统起步（Start with governed observable systems）”。在金融这样高风险、强监管的严肃领域，缺乏健全的安全防护网就谈不上真正的业务拓展——“除非拥有坚固的护栏（Guardrails），否则你的创新根本无法实现规模化（scale your innovation）”。演讲者最后总结指出，未来 Agentic AI 发展的核心命题，始终在于“如何平衡智能体的自主性与人类的信任（How do you balance autonomy with trust）”。在划定边界、确保可控与透明的前提下释放智能体的行动力，才是企业从概念验证（PoC）真正走向生产落地的破局之道。演讲至此结束，演讲者向全场致谢并正式开启问答环节（Q&A）。 ```mermaid flowchart TD accTitle: 金融智能体系统架构与治理循环 subgraph S1["动态模型评估与适配"] A["多模型候选池\n(Claude / GPT-4 / GPT-5)"] --> B["可复用流水线\n(Reusable Pipelines)"] B --> C["持续评估机制\n(Continuous Evaluation)"] end subgraph S2["底线治理与工程化落地"] D["受治理且可观测系统\n(Governed & Observable)"] --> E["坚固的安全护栏\n(Robust Guardrails)"] E --> F["规模化业务创新\n(Scaled Innovation)"] end C --> D F --> G["终极目标：\n平衡自主性与信任 (Autonomy vs. Trust)"] ``` - **技术解耦与持续评测**：拒绝与单一模型深度绑定，通过搭建可复用的管道架构，在 Claude、GPT 系列等主流大模型之间进行横向对比与常态化基准测试，动态选出最适配金融业务场景的底层模型。 - **治理前置与安全护栏**：坚固的护栏机制（Guardrails）与端到端的可观测性（Observability）并非创新的枷锁，而是智能体系统能够从沙盒试点走向规模化落地的必要前提。 - **核心哲学**：未来 Agentic AI 在金融领域的核心竞争优势，取决于企业能否在赋予智能体自主执行能力的同时，牢牢守住风险控制与审计信任的底线。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=4s) 在强监管的银行业与金融服务业中，以人为本的设计更是生死攸关。 — Rajendra Gangavarapu > [01:31](https://www.youtube.com/watch?v=YokJh1BSFOw&t=91s) 在金融场景下，绝不能追求 100% 的完全自主化，必须在每个流程节点设置护栏与人在回路（Human in the Loop）。 — Rajendra Gangavarapu > [05:01](https://www.youtube.com/watch?v=YokJh1BSFOw&t=301s) 向模型无脑灌入海量信息只会造成过载与噪音，如何精准筛选出最相关的高质量长期上下文才是破局关键。 — Rajendra Gangavarapu > [06:52](https://www.youtube.com/watch?v=YokJh1BSFOw&t=412s) 假设一个链路串联了 20 个 Agent，单个 Agent 99% 的准确率经过层层传递，误差可能在末端放大至 20% 到 30%。 — Rajendra Gangavarapu > [10:16](https://www.youtube.com/watch?v=YokJh1BSFOw&t=616s) Your data is your alpha（数据就是你的超额收益）。 — Rajendra Gangavarapu > [12:19](https://www.youtube.com/watch?v=YokJh1BSFOw&t=739s) 监管层期望评估的范围不再仅限于核心模型本身，而是将 RAG 架构、提示词工程以及端到端智能体工作流全部纳入独立评估的边界内。 — Rajendra Gangavarapu > [14:04](https://www.youtube.com/watch?v=YokJh1BSFOw&t=844s) 在对候选模型进行正则化与优化时，团队必须将‘准确性’与‘公平性感知’作为双重优化目标，缺一不可。 — Rajendra Gangavarapu > [21:29](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1289s) 在很多金融垂直场景中，经过针对性微调的小语言模型（SLMs）往往比通用大模型更高效、可控且经济。 — Rajendra Gangavarapu > [23:56](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1436s) Don't trust a given model（不要盲目信任任何单一模型）。 — Rajendra Gangavarapu > [23:56](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1436s) 除非拥有坚固的护栏（Guardrails），否则你的创新根本无法实现规模化。 — Rajendra Gangavarapu > [23:56](https://www.youtube.com/watch?v=YokJh1BSFOw&t=1436s) 未来 Agentic AI 发展的核心命题，始终在于如何平衡智能体的自主性与人类的信任（How do you balance autonomy with trust）。 — Rajendra Gangavarapu ## Mentioned Rajendra Gangavarapu, Geoffrey Hinton, 李飞飞 (Fei-Fei Li), Artigen AI, 《Mastering AI Governance》, 美联储 (Federal Reserve Bank), 美国货币监理署 (OCC), 联邦存款保险公司 (FDIC), 英格兰银行 (Bank of England), 美国证券交易委员会 (SEC), 加拿大航空 (Air Canada), 高盛 (Goldman Sachs), Zillow, Perplexity, NIST AI 风险管理框架, MITRE, GDPR, CCPA, 欧盟人工智能法案 (EU AI Act), 美联储 SR 11-7 指引, 新加坡星展银行 (DBS), 道明银行 (TD Bank), 汇丰银行 (HSBC), 美国金融犯罪执法局 (FinCEN), 美国海外资产控制办公室 (OFAC), OpenAI (GPT-4 / GPT-5), Anthropic (Claude) ## Video description Rajendra Gangavarapu, Advisor, Artigen.ai Abstracts Are you confident that your AI agents are truly ready for prime time in the high-stakes world of finance? In this session, I will tackle the toughest challenges, such as reliability breakdowns, data security risks, and the daunting demands of regulatory compliance that arise when deploying multi-agent AI systems at scale. I will share lessons on error compounding, orchestration, and why careful boundaries and human oversight remain essential. Attendees will learn best practices for architecting robust, compliant agentic workflows, along with practical strategies for integrating AI without sacrificing trust or control. Join me in discovering how to turn today’s risks into tomorrow’s competitive advantages while adopting Agentic AI. [Source](https://www.youtube.com/watch?v=YokJh1BSFOw)

Source quotes

[0:04] Rajendra Gangavarapu: 在强监管的银行业与金融服务业中，以人为本的设计更是生死攸关。 [1:31] Rajendra Gangavarapu: 在金融场景下，绝不能追求 100% 的完全自主化，必须在每个流程节点设置护栏与人在回路（Human in the Loop）。 [5:01] Rajendra Gangavarapu: 向模型无脑灌入海量信息只会造成过载与噪音，如何精准筛选出最相关的高质量长期上下文才是破局关键。 [6:52] Rajendra Gangavarapu: 假设一个链路串联了 20 个 Agent，单个 Agent 99% 的准确率经过层层传递，误差可能在末端放大至 20% 到 30%。 [10:16] Rajendra Gangavarapu: Your data is your alpha（数据就是你的超额收益）。 [12:19] Rajendra Gangavarapu: 监管层期望评估的范围不再仅限于核心模型本身，而是将 RAG 架构、提示词工程以及端到端智能体工作流全部纳入独立评估的边界内。 [14:04] Rajendra Gangavarapu: 在对候选模型进行正则化与优化时，团队必须将‘准确性’与‘公平性感知’作为双重优化目标，缺一不可。 [21:29] Rajendra Gangavarapu: 在很多金融垂直场景中，经过针对性微调的小语言模型（SLMs）往往比通用大模型更高效、可控且经济。 [23:56] Rajendra Gangavarapu: Don't trust a given model（不要盲目信任任何单一模型）。 [23:56] Rajendra Gangavarapu: 除非拥有坚固的护栏（Guardrails），否则你的创新根本无法实现规模化。 [23:56] Rajendra Gangavarapu: 未来 Agentic AI 发展的核心命题，始终在于如何平衡智能体的自主性与人类的信任（How do you balance autonomy with trust）。

Source quotes

[0:04] Rajendra Gangavarapu: AI must be built to empower humans, not the other way around. [1:31] Rajendra Gangavarapu: In banking, trust is critical—once lost, it is nearly impossible to regain. [5:01] Rajendra Gangavarapu: While some platform providers tout unlimited context windows, brute-force context dumping often backfires into information overload; systems must selectively retrieve relevant historical facts rather than flooding the model with noisy data. [10:16] Rajendra Gangavarapu: Customer data is an institution's core alpha, governed by strict privacy regimes including GDPR, CCPA/CPRA, and the European AI Act. [13:11] Rajendra Gangavarapu: Regulators now require independent validation of conceptual soundness not just for the underlying foundation model, but across the entire system—including RAG retrieval pipelines, prompt engineering logic, and multistep agent orchestrations. [16:20] Rajendra Gangavarapu: Human-in-the-loop governance cannot be a static checkpoint tacked onto the final output; it must function as a dynamic, continuous feedback loop integrated into intermediate steps of the workflow. [20:11] Rajendra Gangavarapu: Traditional rule-based compliance engines fail because criminal networks evolve rapidly and remain always 10 steps ahead of the curve. [21:29] Rajendra Gangavarapu: The Artigen AI framework is designed around three foundational engineering principles: security by design, observability by default, and scalable architecture. [21:29] Rajendra Gangavarapu: Your data is your alpha, the architecture guarantees that proprietary enterprise data is never exposed or used to train or fine-tune third-party commercial foundation models. [23:48] Rajendra Gangavarapu: The defining competitive challenge for the next generation of financial technology is successfully solving how do you balance autonomy with trust.

Original transcript

[0:04] trustworthy agent for finance. I'm sure [0:07] you all hearing about Jeffrey Hinton Fay [0:11] this morning talking about how critical [0:13] it is to have humanentric AI right AI is [0:18] built to empower humans not other way [0:21] around so in a regulated industry like [0:25] banking financial services humanentric [0:28] AI is extremely critical uh about me uh [0:31] my name is Ra Rajendra Ganga [0:35] um I've been working in banking [0:36] financial services over the last 25 [0:38] years. Uh building [0:41] enterprise a IML models in a safe [0:44] secured environment um like for credit [0:48] underwriting, fraud, customer [0:50] experience, marketing models, sales [0:52] enablement so on so forth. Over the last [0:54] 15 years, I dealt with the various wide [0:57] range of regulatory agencies, [1:00] international as well as domestic, [1:02] federal, state level regulators such as [1:03] Federal Reserve Bank, OC, FDIC as well [1:07] as Bank of England and other regulators [1:10] in Europe and Asia. [1:13] I recently wrote a book and uh it is uh [1:15] going to be released next month uh [1:18] pre-released in Barnes & Noble, Amazon [1:20] uh Mastering AI governance. check it [1:23] out. Uh if you are interested [1:27] with that background, I would like to go [1:31] for I'm sure all of you are [1:33] experimenting agentic. Uh I mean in 2025 [1:37] the buzzword is agentic. Raise your [1:40] hands. How many of you already started [1:42] working on agentic? [1:46] Good number. How many of agents have [1:48] already been deployed from P to [1:50] production? [1:53] Okay, few the central theme is geni has [1:57] been you know extremely popular over the [1:59] last couple of years like chat GPT is a [2:03] watershed moment in couple of months [2:06] gaining quick traction and it got [2:09] democratized it touched everyone [2:10] starting from kindergarten to you know [2:14] every walks of the life now agenti is [2:17] revolutionalizing in the sense that not [2:19] only just generating the text but It can [2:22] plan, it can call the appropriate tools, [2:24] execute the action as well as it can [2:27] monitor, report so on so forth. But the [2:31] central theme here is [2:34] you cannot completely 100% autonomize. [2:37] You have to have guardrails have to have [2:39] human in the loop at every step of the [2:42] process. I would like to give few [2:45] examples with the recent incidents. I [2:46] don't want to quote the names but one of [2:48] the world's largest bank [2:51] has recently deployed agent EKI for the [2:54] portfolio rebalancing. [2:56] The agent will continuously scan 24 by7, [2:59] scan the market feeds, events, look at [3:02] the portfolio objective, investment [3:05] thesis [3:06] and then it would recommend what how [3:09] what are the kind of appropriate stocks [3:11] or bonds to be included as well as you [3:14] know excluded to maximize the returns or [3:17] you know to meet their risk return [3:19] trade-off or objectives. [3:21] So the way they implemented is it can [3:24] generate the recommendations with human [3:27] approval. It will execute the trades. So [3:30] human in the loop is critical. On the [3:32] contrary, one of the broker dealer [3:36] without having human in the loop they [3:39] started executing lot of deals that [3:41] caused significant losses. As a result [3:43] there was a class action lawsuit is [3:46] going on by the clients against the [3:48] broker dealer. The key thing here is as [3:51] Sara mentioned in the prior call as [3:53] well, trust is critical, right? The [3:57] moment [4:01] Yeah. I mean the trust is lost, it is [4:04] hard to regain in banking and financial [4:06] services. Banking is all about [4:08] establishing the trust. [4:12] So to enable that, human in the loop is [4:15] critical. We'll be talking about a human [4:17] in the loop quite often in the [4:18] presentation. At the same time, full [4:20] traceability, what actions has been [4:23] taken, who has approved when those kind [4:26] of accountability and ownership is [4:28] critical if things goes bad. At the same [4:31] time, financial services is subjected to [4:33] wide range of scrutiny like SEC for [4:37] example on the you know portfolio [4:39] management side, banking and financial [4:41] services obviously OC, Federal Reserve, [4:44] FDIC are the federal level as well as [4:46] state level regulations. They will come [4:47] and audit and check you know how the you [4:51] know actions were taken, who has taken [4:53] the accountability, who has approved, [4:55] what are the changes, you know those [4:56] kind of things. [5:01] What are some of the unique challenges [5:02] while you are implementing agenti? As [5:05] you all may aware, the fragile memory is [5:08] the biggest challenge. Always [5:11] LLMs are agentic fed with the short-term [5:14] memory most recent conversations. But [5:16] how about long-term memory? This is [5:18] always a big challenge. one of the [5:20] insurance recent claims processing it [5:23] was not able to capture some of the [5:25] historic context long-term context that [5:27] cause significant issues challenges in [5:29] terms of resolution of the claims. [5:32] So to avoid that some of the you know [5:35] recent changes like perplexity is [5:37] planning to announce unlimited context. [5:41] Is it a right solution? Not necessarily [5:44] you know right trade-off. what is the [5:47] right appropriate relevant long-term [5:49] context is important rather than [5:52] throwing the entire you know garbage or [5:56] so much of information overload to the [5:58] LLMs [6:00] and then communication gap alignment [6:02] between agents and humans before even [6:04] going to the humans how do we ensure [6:06] that there is an alignment there is an [6:08] proper orchestration among the agentic [6:11] AI framework is extremely critical Well, [6:16] hallucination is a big challenge. Third [6:18] big challenge like uh you know LLMs are [6:21] always probabilistic in nature. They [6:23] will try to answer each and every [6:25] question. I'm sure you all hear about [6:28] you know Air Canada's refund policy [6:30] case. It became so viral it caused [6:32] significant damage to the Air Canada. [6:36] To prevent that you know rag framework [6:40] became quite popular. [6:42] Now intelligent drag fine-tuning [6:44] optimizations those kind of things help [6:47] to mitigate those risks [6:52] when agent KI is a good fit particularly [6:55] in banking and financial services you [6:57] know they have grown through several [6:59] mergers and acquisitions as a result you [7:02] know significant amount of siloed data [7:05] you know siloed functions often times [7:09] collaboration is a challenge [7:11] and hiring good quality of AI talent is [7:14] is is always a scarce. I mean AI talent [7:18] war is going on right now. Um [7:23] so whenever these kind of problems [7:25] definitely agent KI can help to bridge [7:28] these kind of siloed communications, [7:31] siloed data for example rather than [7:33] these days instead of organizations [7:35] going for enterprise data like spending [7:37] millions of dollars waiting for years [7:40] using agenti to generate insights on [7:44] demand quicker and faster. [7:49] As Sara and everyone mentioned, trust is [7:52] critical at every step of the process. [7:55] You need to have a telemetry. Goldman [7:57] Sachs implemented recently telemetry at [7:59] every step of the data pipeline, data [8:01] prep-processing or you know feature [8:04] engineering and post-processing, model [8:06] development, deployment, [8:09] monitoring at every step of the process. [8:12] There are appropriate robust controls in [8:14] the entire AI life cycle. [8:18] So telemetry is critical [8:21] that can help. One more question is uh [8:24] know why telemetry is important at every [8:27] stage or for example in a given use case [8:30] you are deploying hypothetically 20 [8:33] agents. Agent one's output is an input [8:36] to the agent two. Agent one level of [8:39] accuracy probably 99%. Imagine that 1% [8:43] error when it goes from agent one to [8:45] agent 19 or agent 20th the error term [8:48] will amplify and it could be 20% 30%. So [8:53] if there is a kind of degradation or a [8:56] drift at agent five to six for example [9:00] agent six should be able to alarm that [9:03] this degradation is beyond the tolerance [9:05] levels. So it's not only monitoring at [9:09] individual monitoring uh agent level [9:11] monitoring but holistic level also is [9:13] critical to make sure that overarching [9:15] like supervisor agent has to [9:17] continuously monitor how is the [9:19] individual agent [9:21] accuracy when before the passing that [9:23] output to the next level of agent that [9:25] is critical. [9:28] How many of you heard about Zillow? [9:31] Zillow [9:32] incurred I think a couple of years ago [9:34] $550 million of loss and they shut down [9:37] one line of business and fired couple of [9:39] thousands of employees. It is all [9:41] because of significant drift model drift [9:44] that has happened because your training [9:45] data used for the model AI model was [9:49] pre-COVID level and training data as [9:53] that cause significant drift and they [9:55] did not realize how much of the drift [9:56] that is happening you know in a training [9:59] environment models works very well but [10:01] once you deployed in the production [10:03] drift will always happen how do you [10:05] proactively identify the drift and [10:07] mitigate the drift drift on timely [10:09] manner is extremely critical. [10:16] Always banking and financial services is [10:18] the prime target for the adversarial [10:20] attacks [10:22] have to have more robust governance [10:24] controls around AI agentic AI side [10:28] prompt injection attacks GPT5 was [10:31] launched less than 24 hours it was [10:34] prompt injections jailbreaking [10:38] became big issue So not only prompt [10:42] injection there are lot of data [10:44] poisoning has been happening in recent [10:46] times because LLMs have been trained on [10:48] open-source data sets. These opensource [10:51] data sets unless you do rigorous [10:53] monitoring and you know scrutiny [10:55] somebody you know unscrupulous or bad [10:58] actor can contaminate the open source [11:00] data sets and that can cause security [11:03] vulnerabilities and so on so forth. Same [11:05] way open source AI models weights can be [11:08] manipulated. So have to have robust [11:12] guardrails or rigorous scrutiny are when [11:15] you are using open source models. I'm [11:16] not against to the open source [11:17] community. Open source community can [11:19] empower all of us in a big way. [11:22] Use with caution [11:25] whenever you are doing before you deploy [11:27] sandboxing [11:29] validation internal validation external [11:31] validation is critical data provenence [11:34] where is that data is coming from how [11:37] trustworthy is the data is who has [11:39] verified last time right data stewards [11:42] data accountability ownership is [11:44] critical [11:46] and particularly from security [11:47] standpoint of view most of the banks and [11:50] financial institutions initutions are [11:51] using Mitray as well as NIST framework [11:55] to identify the you know any kind of [11:59] security vulnerabilities and red team [12:02] not just in production but even [12:04] pre-eployment as well as postp [12:06] production is absolutely critical these [12:08] are all non-negotiable requirements [12:12] data privacy data governance is [12:14] extremely critical your data is your [12:16] alpha lot of regulations are coming up [12:19] like GDPR CC CP already in you know live [12:23] European AI act is going to be live in [12:26] by August of 2026 [12:30] any compromise of the data privacy can [12:33] cause significant reputational risk huge [12:35] penalties up to like 4% of your revenue [12:38] annual revenue can be fines can be [12:40] imposed [12:42] so [12:44] GDPR or EU act says that you cannot use [12:48] European data for training US models [12:51] there has to be robust guardrails. Data [12:54] cannot be moved from one jurisdiction to [12:56] the next jurisdiction. Same [12:59] sorry any question? [13:01] Okay. [13:03] Data lineage, data privacy impact [13:05] assessments also extremely critical [13:11] in banking financial services. SR17 is [13:14] one of the gold standard whether it is [13:16] machine learning, deep learning or [13:18] generative AI, agentic AI. Recently one [13:22] of the midsize bank has launched [13:24] mortgage AI assistant [13:27] and they looked at SR17 is meant for [13:30] independent validation evaluating [13:32] conceptual soundness making sure that [13:34] model is fit for the purpose at regular [13:36] intervals. So before you deploy in [13:39] production independent validation is [13:41] required as well as third party audits [13:45] are essential. So recent regulatory [13:48] examination against one of the mortgages [13:50] AI chart but regulators stopped they [13:53] expect not only the model evaluation but [13:56] also their rag framework the prompt [13:58] engineering end-to-end workflows also [14:00] need to be included in the independent [14:02] assessment. [14:04] Second thing is in banking and [14:05] regulation not only the accuracy but [14:07] also making sure that models are fair [14:10] unbiased because when we use these EI [14:12] models for credit underwriting because [14:15] loan decisioning for example is [14:18] determined you know without any minimal [14:20] human intervention whether loan will be [14:23] approved or declined or you know if loan [14:26] is approved what is the appropriate line [14:29] assignment and what is the kind of [14:31] pricing. So when we are doing it we need [14:33] to make sure that models are fair. We [14:35] are not discriminating based on ethnic [14:37] group, gender, age so on so forth [14:39] against protected classes. [14:42] So when you are regularizing optimizing [14:44] models not only the accuracy but also [14:46] making sure that fairness aware. So it's [14:49] a dual objective [14:51] before we finalize the candidate model. [14:54] Change control is extremely critical [14:56] when we are making the change who made [14:58] the change who approved. Those kind of [15:00] guardrails are critical. Continuous [15:02] monitoring extremely critical. As I said [15:05] before, Zillow not having continuous [15:07] monitoring caused $550 million of loss, [15:10] shutting down the business. Huge [15:12] reputational damage for them. AB testing [15:16] before you launch any new model, we [15:18] always put the model in the shadow mode [15:20] and make sure that you know it is [15:22] working like start with like 5% 10% [15:25] volume go through champion challenger [15:27] model framework. So if the model is [15:30] working good candidate model or a [15:32] challenger model is working better than [15:34] production then increase the volume 5% [15:37] to 10% 20% 30% over a period of 3 to 6 [15:40] months time frame we evaluate any new [15:43] model before we migrate the traffic 100% [15:46] to the challenger model. If a given time [15:50] let's say for example if the new model [15:52] is not working we should be able to roll [15:53] back the model and redirect the older [15:56] version of the model kind of thing. So [15:58] you have to have the robust pipelines so [16:00] that you can redirect the how the [16:02] traffic management happens. Same way you [16:04] have to have a robust pipelines model [16:06] pipelines so that you can efficiently [16:10] leverage whatever the model that is [16:12] working better as the you know market [16:14] dynamics changes. [16:20] Human in the loop is extremely critical [16:22] like human has to define what are the [16:24] objectives what are the guardrails how [16:26] you know you know the key KPIs for model [16:30] evaluation so on so forth boundary lines [16:32] of the effectiveness of the model has to [16:34] be selfidentified by the model owner [16:36] model user [16:39] then agent can work operate within those [16:43] guardrails [16:45] provide actionable insights develop [16:47] solutions and then human can eval [16:49] evaluate those insights as I mentioned [16:51] like you know portfolio rebalancing once [16:54] a TKI gives recommendation then human [16:56] approves then trade will be executed [16:59] so it's a iterative fashion it's a [17:01] continuous learning process has to be [17:03] developed so the feedback loop has to be [17:06] dynamic rather than waiting until the fi [17:08] final output as the beginning of the [17:11] slide at every step of the process you [17:13] have to capture the feedback capture the [17:16] feedback dynamically so that you can [17:18] minimize the error when you are using [17:20] large number of agents. I think in the [17:21] morning keynote session we have 8 [17:24] billion population could be like [17:28] you know maybe 10 billion agents might [17:30] be evolved or 80 billion agents in the [17:32] next 5 to 10 years. If you have agents [17:35] proliferate in your organization you [17:38] have to have a robust guardrails. You [17:40] you need to have human oversight at [17:42] every step of the process to make sure [17:44] that you get the best value out of it. [17:47] Before [17:49] you productionalize any agentic AI [17:52] typically we use gated playbook. So [17:56] define the use case, what is the success [17:58] criteria or KPIs? [18:01] Test this in a sandbox environment. [18:04] Have robust evals making sure that you [18:07] can evaluate not only accuracy, bias, [18:10] fairness related, security, data privacy [18:12] related issues as well. Red teaming and [18:16] then can redeployment in a shadow mode [18:18] first. see how it is deployed and then [18:21] finally you deploy into production and [18:23] then continuous monitoring has to [18:24] happen. [18:29] AML stands for anti-moneyaundering. Um [18:33] so know your customer know your [18:34] customer's customer is critical post 911 [18:37] attacks. Every bank is subjected to BSA [18:39] AML act. Most of the banks have AML [18:43] fatigue. I know one of the bank that I [18:46] worked a global bank in the past [18:48] literally they [18:51] recruited almost 3 to 4,000 staffs to [18:54] evaluate all the false positives. [18:56] Imagine [18:58] on the contrary now one of the bank like [19:01] DBS Singapore they have integrated LLM [19:05] orchestration layer integrated with AML [19:08] rulebased framework they are able to [19:10] reduce drastically the false positive [19:14] rates at the same time true positives [19:16] gone up this will empower people so one [19:21] of the key thing is this AML is [19:23] subjected to suspicious activity [19:25] reporting Currency transaction reporting [19:27] has to be filed in a stipulated time [19:29] frame like less than 24 hours. Bank has [19:32] to report any suspicious activity if the [19:35] customer customers customer connected to [19:37] any terrorist organization or a drug [19:39] dealer or a politically exposed persons [19:42] you know immediately. If you don't file [19:45] these kind of suspicious activities on [19:48] time regulators can impose huge [19:50] penalties. Recently TD Bank got $1 [19:52] billion penalty not filing cash fine not [19:56] filing SARS not able to identify some of [19:59] the true positives HSBC got a billion [20:03] dollar fine couple of years back and [20:04] asked to shut down the business at the [20:06] time HSBC was the top 10 bank in the US [20:09] they were asked to shut down sold all [20:11] their assets and just they were allowed [20:13] to maintain only one single branch [20:15] correspondent bank in New York so the [20:18] impact is so profound So not having uh [20:22] identifying true positives always human [20:25] in the loop I mean humans also can make [20:28] errors. So how do you empower human [20:32] using agentic identifying [20:35] these suspicious activity behaviors? [20:37] It's not just you know terrorists or [20:40] these drug dealers can always come up [20:42] with more sophisticated uh schemes like [20:45] traditionally they try to place drug [20:48] dealer money small amounts layer [20:50] fictitious transactions they will do to [20:53] obfuscate the source of funds but you [20:56] know they'll come up with rule-based [20:57] framework will not be able to catch up [20:59] because they're always 10 steps ahead of [21:01] the curve come up with new schemes of [21:04] bringing illotten money into the banking [21:05] system and to fund nefarious activities. [21:08] So AI is playing a predominant role [21:11] nowadays empowering people, identifying [21:14] true positives, minimizing false [21:16] positives, not only improving human [21:18] efficiency but catch the bad actors on [21:21] time, reporting to the regulators like [21:23] Fininsson, office of foreign asset [21:25] control so on so forth. [21:29] Uh I recently started artisan after [21:32] working for 20 years and trying to build [21:34] this is a startup. Um so based on the [21:36] experience that I gained dealing with [21:38] regulators working with large banks and [21:41] financial institutions over the last 25 [21:43] years trying to create a robust [21:45] framework architecture to control these [21:49] kind of challenges empower people in a [21:51] more scalable manner primarily this [21:55] architecture has four different [21:56] components. Central LLM gateways. This [21:59] is where you can enforce all policies [22:01] like NIS framework to Mitri framework to [22:03] SR17 or UI act and observability layer. [22:08] You can define all the key KPIs not only [22:10] accuracy, bias, fairness, security, data [22:13] privacy related metrics, thresholds. It [22:15] can continuously monitor, identify, flag [22:18] exceptions to the users on real-time [22:20] basis and uh manage integrate with your [22:23] existing IM so that security access [22:26] privileges can be maintained and uh it [22:29] can allow to have rag framework as well [22:33] as uh through MCP uh rigorous assessment [22:36] of data and fine-tuned models in a [22:39] secured custom manner for the [22:40] enterprises so that your data will never [22:43] be used for fine-tuning are training of [22:46] some other model. So as I said before [22:48] your data is your alpha. You need to [22:50] safeguard your data so that you can [22:53] continue to serve your customers better [22:56] maximize your profitability safeguard [22:58] your growth and profitability standpoint [23:00] of view. I think most of the [23:02] organizations started realizing that I'm [23:04] not against to the big players. The [23:06] bigger the LLM they are more sec you [23:08] know prone to the security attacks or [23:11] jailbreaking so on so forth but [23:13] sometimes SLMs can be more powerful you [23:16] have to evaluate which model is the [23:18] right fit for a given problem have to [23:20] have robust CI/CD pipelines reusable [23:23] pipelines to check which LLM is most [23:25] appropriate for a given use case and [23:28] then you know fine-tune optimize or use [23:32] rag so that you can minimize control [23:34] hallucinations this is the kind [23:36] framework, security by design, [23:38] observability by default, scalable [23:40] architecture. These are the fundamental [23:42] principles based on which this [23:44] architecture has been built. [23:48] Future of agenti don't wait for the [23:51] perfection. It is things are evolving so [23:53] fast, so quick. You know how often you [23:56] know GPT4 to GPT5 kind of things. So [23:59] things are evolving. Don't trust a given [24:01] model. Have to have reusable pipeline. [24:03] continuously evaluate you know whether [24:05] cloud is be better fit versus GPT you [24:08] know OpenAI 5 is a better fit what is [24:11] the right model reusable pipelines [24:13] continuous evaluations are critical [24:18] start with governed observable systems [24:20] that is extremely critical unless you [24:22] have robust guardrails [24:24] you cannot scale your innovation [24:28] okay future will be is all about how do [24:30] you balance autonomy with the trust with [24:32] that note Thank you very much. If they [24:34] you have any questions, I'm open for [24:36] Q&A.