Turing Fixes Enterprise AI Bottlenecks via Unified Architecture

Building Unified AI Systems at Enterprise Scale With Turing — Ai4

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Fragmented frameworks and integration taxes delay production scaling, whereas

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## Background Published by Ai4 on YouTube on August 18, 2026, this presentation features Vinu Krishnaswamy, Director of Solutions Architecture at Turing. The talk addresses why enterprise AI projects stall due to fragmented stacks across agents, evaluation, human review, and observability, and presents a unified architecture to streamline development and meet strict compliance standards.

## TL;DR Vinu Krishnaswamy outlines the transition from isolated AI proof-of-concepts to an enterprise-wide production ecosystem, highlighting how fragmented evaluation frameworks create a costly 'integration tax'. To overcome this bottleneck, Turing advocates for a unified AI architecture based on end-to-end tracing, continuous evaluation, governance logging, and closed-loop human feedback. Using a real-world financial services case study of over 1 million tickets, he demonstrates how this platform approach achieved 81% classification accuracy and generated $15 million to $18 million in operational savings. Ultimately, embedding human oversight and platform-level infrastructure enables organizations to safely scale production AI.

## Atmosphere & style The presentation carries a pragmatic, engineer-driven, and authoritative tone balanced by light self-deprecating humor. Krishnaswamy speaks directly and methodically, translating complex architectural concepts and enterprise pain points into structured, actionable insights. The delivery feels like a focused executive briefing that connects high-level business strategy with practical system design.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) Enterprise AI adoption faces two distinct inflection points: moving from a proof-of-concept to a single production deployment, and scaling from isolated deployments to an enterprise-wide production ecosystem. - [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) Silos across enterprise departments lead to fragmented agentic frameworks, distinct evaluation platforms, and separate human-in-the-loop workflows. - [00:28](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=28s) The "integration tax" represents the heavy infrastructure maintenance and delay cost incurred when engineering teams spend months managing fragmented AI stacks rather than delivering business ROI. - [00:28](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=28s) Fragmented AI architectures fail when answering critical governance and audit questions regarding action attribution, human approvals, reproducibility, and root-cause error fixing. - [04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) A unified AI architecture requires four non-negotiable operational pillars: end-to-end tracing, evaluation systems with golden data sets, governance records, and closed-loop feedback. - [04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) Closing the feedback loop by incorporating human-in-the-loop overrides directly into prompt updates and context pipelines allows AI systems to continuously improve past initial accuracy thresholds. - [08:14](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=494s) Turing's unified infrastructure platform consolidates execution, context, and administration into a single control plane featuring custom agent authoring, event triggers, and a multimodal knowledge hub with RBAC. - [10:50](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=650s) Deploying a unified multi-agent system for a financial client handling over 1 million annual tickets achieved 81% classification accuracy, 40% end-to-end automated resolution, and $15M–$18M in savings. - [12:46](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=766s) System architecture, rather than model selection, is the ultimate bottleneck when scaling enterprise AI across multiple departments. - [12:46](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=766s) Adopting a centralized platform yields compound acceleration, significantly reducing rollout time and operational risk for subsequent AI use cases. - [12:46](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=766s) Embedding human oversight into the core platform gives stakeholders the confidence needed to scale autonomous AI systems in production. - [12:46](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=766s) Turing operates a dual strategy combining a Frontier Lab for benchmark data generation with an Enterprise Solutions group to drive real-world business ROI.

## Detailed walkthrough Here is a detailed, faithful walkthrough of the transcript.

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[00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) The Enterprise Scaling Bottleneck

Vinu Krishnaswamy, Director of Solutions Architecture at Turing, opens by introducing himself as "an engineer at heart, which apparently is the most dangerous kind of engineer there is." In his role at Turing, he drives enterprise initiatives to deliver production AI systems. He observes that organizations face two distinct inflection points when adopting AI: first, transitioning from a proof-of-concept (POC) to a single production deployment, and second, scaling from one or two isolated agentic deployments to an enterprise-wide AI production ecosystem.

While the market offers plenty of guidance on navigating the first milestone—focusing on business outcomes to determine which POCs deserve to be promoted—the real operational crisis hits at the second milestone. When scaling across multiple departments, traditional enterprise silos creep into the AI stack. Different teams independently adopt disparate evaluation platforms, separate human-in-the-loop mechanisms, and different agentic frameworks (ranging from custom ADKs and cloud SDKs to Erlang-based graph frameworks). While managing these isolated stacks might be tolerable for a single use case, doing so across dozens of enterprise projects creates severe friction and turns fragmentation into the primary bottleneck holding back enterprise AI.

- **Speaker:** Vinu Krishnaswamy (Director, Solutions Architecture, Turing). - **Core Problem:** Enterprise silos lead to fragmented agentic frameworks, evaluation systems, and human-in-the-loop workflows across different departments.

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[00:28](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=28s) The Hidden "Integration Tax"

Krishnaswamy humorously blames "live coding" for the explosion of ad-hoc evaluation frameworks that developers continuously build and deploy to production. When every team sets up distinct integration points, the organization gets burdened with heavy infrastructure overhead. Engineering teams end up spending months setting up and maintaining underlying infrastructure rather than delivering actual business ROI. Turing calls this penalty the "integration tax."

``` +-------------------------------------------------------------+ | FRAGMENTED AI STACK | | | | [Agent Frameworks] [Eval Frameworks] [Human Review] | | (ADK / Cloud SDK) (Ad-hoc / Custom) (Isolated Ops) | +-------------------------------------------------------------+ | v HIGH INTEGRATION TAX - Infrastructure bloat & delays - Inability to answer audit questions - Broken operational feedback loops ```

This fragmentation breaks down completely when teams must answer strict compliance, auditing, and governance questions—especially in heavily regulated environments like financial services:

- **Action Attribution:** Why did a specific AI agent take a particular action? - **Human Oversight:** Who approved the decision, and was it an automated rule or a human-in-the-loop sign-off? - **Reproducibility:** Can the exact behavior be reproduced across lower testing environments using the same prompt, context, data set, and inputs? - **Policy Auditing:** Which specific regulatory rules or state-level legal policies (e.g., insurance regulations) did the agent fetch to reach its conclusion? - **Root Cause Fixing:** When an agent fails, what specific part of the system needs to change to prevent recurring errors?

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[04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) Core Pillars of a Unified AI Architecture

To eliminate the integration tax, Turing advocates for a unified architecture built around four non-negotiable operational pillars. While Krishnaswamy acknowledges that systems still require flexibility—allowing individual engineering teams to use their preferred frameworks, UIs, or connectors—these four elements form the core "bread and butter" for any production enterprise deployment:

``` +-------------------------------------------------------------+ | UNIFIED ARCHITECTURE PILLARS | | | | +--------------------+ +--------------------+ | | | EVALUATION | | TRACING | | | | Golden Data Sets | | Full Workflow Runs | | | +---------+----------+ +---------+----------+ | | | | | | +----------------+-----------------+ | | | | | +----------------+-----------------+ | | | | | | +---------v----------+ +---------v----------+ | | | GOVERNANCE RECORDS | | FEEDBACK LOOP | | | | Policy Traceability| | HITL Prompt Updates| | | +--------------------+ +--------------------+ | +-------------------------------------------------------------+ ```

1. **End-to-End Tracing:** Logging every agent input, contextual data payload, and prompt version. Crucially, tracing must follow the entire end-to-end workflow (e.g., tracking a customer ticket from arrival through an orchestrator agent down to final resolution), rather than isolated agent steps. 2. **Evaluation Systems:** Verifying that agents perform as expected using evolving "golden data sets" that pass strict validation checks. 3. **Governance Records:** Maintaining explicit logs of every business rule, policy, and state law evaluated for a specific transaction. 4. **Closed-Loop Feedback:** Capturing human-in-the-loop overrides and operational edge cases to continuously refine prompt versions and context pipelines over time.

Krishnaswamy highlights that the feedback loop is missing from most current production systems. Initial off-the-shelf models or prompt templates might perform adequately for 60% to 70% of standard cases, but initial testing rarely uncovers every real-world production variant. Integrating human review directly back into the prompt and context engineering workflow allows the agentic system to self-correct and improve dynamically.

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[08:14](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=494s) Turing's Unified Infrastructure Platform

Turing addresses these requirements through a unified infrastructure platform designed to consolidate execution, context, and administration into a single control plane.

``` +-------------------------------------------------------------+ | TURING UNIFIED PLATFORM SYSTEM | | | | [Custom Agents / Clusters] [Automations / Triggers] | | | | | | +----------------+------------------+ | | | | | +-------------v--------------+ | | | Multimodal Intelligence | | | | (PDFs, Data Lakes, Rules) | | | +-------------+--------------+ | | | | | +-------------v--------------+ | | | Knowledge Hub & RBAC | | | +-------------+--------------+ | | | | | +-------------v--------------+ | | | Admin Controls & Testing | | | +----------------------------+ | +-------------------------------------------------------------+ ```

- **Custom Agent Authoring:** Enables rapid creation of standalone agents or multi-agent clusters using higher-level orchestration layers to quickly test new operational hypotheses. - **Automations & Event Triggers:** Dynamically schedules or invokes agents based on incoming external signals, such as scheduled timers, data lake updates, or new tickets landing in enterprise systems like ServiceNow. - **Multimodal Knowledge Hub:** Ingests unstructured documents (PDFs, PowerPoint slides, images) alongside structured enterprise data sources (such as Snowflake or cloud data lakes) to construct rich context for agents, strictly governed by role-based access control (RBAC). - **Admin & Compliance Controls:** Centralized logging, role-based access restrictions, and embedded AI chat modules that serve as dedicated testing tools for developers and administrators.

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[10:50](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=650s) Enterprise Case Study: AI-Driven Ticket Intelligence

To demonstrate the architecture in practice, Krishnaswamy shares a real-world deployment for a major financial services client handling over 1 million incoming service tickets annually. Historically, ticket classification and resolution were performed manually through a labor-intensive, multi-step process.

By deploying a multi-agent network integrated directly with Turing's platform, the client established automated routing while enforcing human-in-the-loop oversight. Human review tasks were routed straight into everyday communication channels—such as mobile notifications, Microsoft Outlook, or Teams—allowing human reviewers to approve or adjust agent actions without leaving their existing tools.

- **Annual Ticket Volume:** 1,000,000+ financial service tickets. - **Classification Accuracy:** Reached 81% accuracy across complex ticket categories. - **Automated Resolution:** Successfully resolved 40% of all incoming tickets end-to-end without full human intervention. - **Financial ROI:** Delivered $15 million to $18 million in projected annual operational savings.

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[12:46](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=766s) Key Takeaways and Corporate Overview

Krishnaswamy summarizes three main lessons for enterprise leadership:

1. **Architecture is the Real Bottleneck:** As organizations expand from one production use case to dozens, architecture—not model selection—determines success. A unified control plane for evaluation, observability, and human oversight removes operational roadblocks. 2. **Platform Effects Accelerate Deployment:** Building on a centralized control plane slightly lowers risk for the initial deployment, but dramatically speeds up the rollout of second and third use cases because governance and evaluation infrastructure are already active. 3. **Human Oversight Enables Scale:** Counterintuitively, embedding human oversight directly into the platform is what enables AI to scale rapidly. It gives executive leadership and business stakeholders the confidence to deploy autonomous systems into production.

In closing, Krishnaswamy presents Turing’s mission to "accelerate superintelligence to drive economic progress." Turing operates across two complementary frontiers: a Frontier Lab focused on generating high-quality evaluation data, benchmarks, and model training methodologies, and an Enterprise Solutions group that deploys these models into real-world business workflows to deliver measurable ROI. The learnings from enterprise deployments directly feed back into the Frontier Lab, creating a continuous improvement loop across the entire company.

<FollowUp label="Want me to elaborate on the design patterns for human-in-the-loop integrations?" query="Can you explain the design patterns and best practices for integrating human-in-the-loop (HITL) workflows into enterprise AI architectures based on the Turing presentation?"/>

## Notable quotes > [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) an engineer at heart, which apparently is the most dangerous kind of engineer there is — Vinu Krishnaswamy

> [12:46](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=766s) accelerate superintelligence to drive economic progress — Vinu Krishnaswamy

## Mentioned Vinu Krishnaswamy, Turing, ServiceNow, Snowflake, Microsoft Outlook, Teams, Ai4

## Video description Vinu Krishnaswamy - Director, Solutions Architecture, Turing

Most enterprise AI projects stall because of a fragmented stack: agents, evaluation, human review, and observability all managed separately. That complexity slows teams down and introduces risk before anything reaches production. This talk covers how leading organizations have moved to unified AI architectures that treat execution, evaluation, and human oversight as a single system, cutting development cycles and making governance hold in regulated environments. You'll leave with concrete examples and a clear picture of what it takes to ship AI that’s reliable in production

[Source](https://www.youtube.com/watch?v=lM04Xpg3OwU)

中文简报

Turing通过统一架构破解企业AI瓶颈 碎片化框架与“集成税”拖慢了生产规模化落地，而 Vinu Krishnaswamy概述了从孤立的AI概念验证迈向企业级生产生态系统的转变，并重点指出碎片化的评估框架如何造成高昂的“集成税”。为克服这一瓶颈，Turing主张建立基于端到端追踪、持续评估、治理日志记录和闭环人工反馈的统一AI架构。结合一个涉及100多万张工单的金融服务实际案例，他展示了该平台化方案如何实现81%的分类准确率，并带来1500万至1800万美元的运营成本节省。归根结底，融入人工监督与平台级基础设施能够使机构安全地扩展生产级AI。 企业在 AI 规模化时面临严峻的架构割裂问题，各部门分散的智能体框架与评估体系导致高昂的“集成税”，拖慢业务落地并难以满足合规审计要求。 (0:28) Turing 提出统一企业级 AI 架构的四大核心支柱：端到端链路追踪、基于黄金数据集的评估体系、合规治理记录，以及将人机协同修正融入提示词与上下文的闭环反馈机制。 (4:55) Turing 统一基础设施平台整合了定制智能体构建、事件驱动自动化，以及具备严格 RBAC 权限控制的多模态知识中心，集中管理执行与上下文生态。 (8:14) 在某年处理超 100 万工单的金融客户案例中，该统一多智能体系统实现了 81% 分类准确率和 40% 的全自动处理率，每年预计节省 1500 万至 1800 万美元。 (10:50)

Reading summary

According to Vinu Krishnaswamy, Director of Solutions Architecture at Turing, the primary hurdle in scaling enterprise AI is not model capability, but architectural fragmentation that imposes an unsustainable operational and governance tax. The Enterprise Integration Tax (0:04) Krishnaswamy observes that while companies readily manage the leap from proof-of-concept to a single production deployment, attempting to scale to multiple departments triggers severe architectural gridlock. Individual teams often deploy isolated agent frameworks—ranging from custom ADKs to Erlang-based graphs—alongside ad-hoc evaluation systems and disconnected human-in-the-loop workflows. Turing terms the resulting friction the 'integration tax,' in which engineering teams waste months maintaining duplicated infrastructure rather than generating business value. This lack of standardization creates critical vulnerabilities in regulated environments such as financial services. When systems operate in silos, organizations cannot reliably satisfy audit inquiries, such as attributing why an agent took a specific action, identifying whether humans or automated policies approved an outcome, reproducing behaviors across test environments, or identifying the exact failure point to prevent recurring system errors. Core Pillars of a Unified Architecture (4:55) To eliminate the integration tax while preserving team-level flexibility for preferred UIs and connectors, Turing outlines four mandatory operational pillars: end-to-end tracing, evaluation systems, governance records, and closed-loop feedback. Krishnaswamy emphasizes that tracing must monitor complete workflows—such as a ticket moving through an orchestrator to resolution—rather than isolated actions, while evaluations must continuously validate performance against curated 'golden data sets.' Krishnaswamy identifies closed-loop operational feedback as the element most frequently absent in existing deployments. While static prompt templates or baseline models may resolve 60% to 70% of standard production cases, they fail to anticipate complex edge cases. Capturing human overrides and feeding them systematically back into prompt and context pipelines allows agentic deployments to continuously self-correct and expand coverage over time. Consolidated Control via Unified Platforms (8:14) Turing addresses these requirements through a centralized infrastructure platform that unites execution, context, and administration into a single control plane. The system supports custom agent authoring for standalone and clustered agents, automation tools triggered by schedules or enterprise events in platforms like ServiceNow, and a multimodal knowledge hub that merges unstructured documents with structured sources like Snowflake under role-based access control (RBAC). Embedded administrative and testing modules further centralize policy tracking and logging. Production Proof in Financial Services (10:50) Illustrating the architecture in production, Krishnaswamy points to a deployment for a major financial services organization processing over 1 million customer service tickets each year. By deploying a multi-agent network coupled with human-in-the-loop mechanisms integrated directly into workplace tools like Microsoft Teams, Outlook, and mobile notifications, reviewers could approve actions without switching workflows. The deployment achieved 81% classification accuracy across complex categories and resolved 40% of all incoming tickets automatically without human intervention. Turing reports that this system generated between $15 million and $18 million in projected annual operational savings for the client. Krishnaswamy concludes that scaling enterprise AI depends on architecture rather than model selection, arguing that centralized governance and embedded human oversight paradoxically accelerate deployment speeds. Turing supports this by linking its Frontier Lab's benchmark and training research directly with feedback gathered from its enterprise deployments.

精炼阅读摘要

Turing解决方案架构总监Vinu Krishnaswamy指出，企业将人工智能从单点试点推向全公司规模化应用时，真正的瓶颈已从模型选型转变为架构碎片化；各业务部门各自为政导致的“集成税”与治理黑箱，必须依靠涵盖追踪、评估、审计和人在回路闭环的统一控制平台来化解。 碎片化工具链引发的“集成税”与合规阻碍 (0:28) Krishnaswamy指出，企业在AI应用上通常会经历两个关键拐点：从概念验证（POC）过渡到首个生产部署，以及从个别独立Agent扩展到全组织级的AI生产生态。在第二阶段，传统企业的部门孤岛往往会渗透至技术栈，各团队会独立引入不同的评测工具、人工审查机制以及Agent开发框架（包括定制ADK、云端SDK或基于Erlang的图框架）。这种分散的开发模式导致企业陷入沉重的“集成税”，工程团队被迫耗费数月时间搭建和维护底层基础设施，无法聚焦于业务ROI的交付。 在金融等强监管领域，架构碎片化会导致严重的治理风险。Krishnaswamy强调，分散系统无法有效解答合规审计的核心问题：系统无法明确追溯某个Agent采取具体行动的因果链条，难以区分决策是源自自动化规则还是人工审批，无法在测试环境中以相同的Prompt、上下文和数据集复现偶发故障，也难以审计Agent在得出结论时具体调用了哪些行业法规或州级政策，从而导致系统在发生故障时无法定位根因并予以修复。 支撑企业级生产系统的四大统一架构支柱 (4:55) 为了消除多套系统带来的冗余成本，Turing提出了企业级生产部署不可或缺的四大架构支柱。Krishnaswamy认为，虽然架构应保持一定灵活性以兼容不同团队的界面或连接器偏好，但核心层必须统一：第一是端到端追踪，系统必须记录工作流整体流转过程中的每一个输入、上下文数据载荷与Prompt版本，而非仅追踪孤立的单步动作；第二是评估体系，依靠持续迭代的“黄金数据集”确保Agent通过严格的功能检验；第三是治理记录，完整留存每笔交易所依据的业务规则与法律合规条款。 第四大支柱是闭环反馈机制。Krishnaswamy分析称，当前多数生产环境缺乏有效的反馈回路，导致基础模型或预设Prompt仅能覆盖60%到70%的标准场景，无法应对真实世界中的各类边缘案例。通过将人工干预与重写记录直接连通至Prompt和上下文工程管线，系统能够实现动态自我修正与持续演进。 统一控制平面的功能构成与百万级工单实战验证 (10:50) 针对规模化管控需求，Turing推出了整合执行、上下文与管理的统一基础设施平台。该平台支持单Agent或多Agent集群的编排构建，并能依据定时任务、数据湖变动或ServiceNow工单等外部触发器实现动态调度；平台内置的多模态知识中心可接入PDF、幻灯片等非结构化文档以及Snowflake等结构化数据湖，并在严格的角色权限控制（RBAC）下构建上下文；管理员则通过集成的测试模块与集中日志执行监控。 Krishnaswamy分享了一家年处理超过100万件服务工单的大型金融服务机构的落地案例。该客户通过接入集成Turing平台的多Agent网络，将人工审核任务嵌入员工日常使用的移动端通知、Microsoft Outlook及Teams中，实现不离开现有工作流的审批。该方案使复杂工单的分类准确率达到81%，40%的进线工单实现了全流程自动化解决，据Turing测算，每年为该客户带来1500万至1800万美元的运营成本节约。 平台协同效应与人在回路的规模化价值 (12:46) Krishnaswamy总结了企业领导层推进AI时的核心认知转变：决定扩展成败的关键在于架构而非模型。依托统一控制平面开展建设，虽在首个用例中仅小幅降低风险，却能大幅缩短后续第二、第三个用例的上线周期，因为评估和治理基建已预先就绪。与此同时，在平台内部深度嵌入人在回路机制看似增加了人工步骤，实则是推动AI规模化落地的核心杠杆，它为管理层和业务利益方提供了将自主系统推向生产环境的安全信心。 在公司定位层面，Krishnaswamy介绍了Turing的双轮驱动模式：其Frontier Lab专注于生成高质量评估数据、基准测试及模型训练方法，而Enterprise Solutions部门则负责将模型嵌入实际业务工作流。来自企业真实业务场景的反馈会反哺前沿实验室，形成技术演进与商业落地的闭环循环。 Krishnaswamy强调统一控制平台是消除“集成税”与满足合规审计的必要路径，并以金融工单案例论证了可量化的经济效益。不过，该方案在多部门大型企业落地时，仍取决于各业务线能否顺畅让渡既有的自研工具链并统一接入中心化治理标准，其组织协作与迁移成本在实践中仍是企业需要权衡的现实问题。

Detailed notes — zh

## Background 该视频由 Ai4 频道于 2026 年 8 月 18 日发布在 YouTube 上，演讲嘉宾为 Turing 解决方案架构总监 Vinu Krishnaswamy。视频简介指出，由于 Agent、评估、人工审核与可观测性各自割裂的碎片化架构，多数企业 AI 项目陷入停滞，本次演讲重点探讨领先组织如何通过统一 AI 架构将执行、评估与人类监督融为一体，从而缩短开发周期并在强监管环境下实现合规治理。 ## TL;DR 演讲深入剖析了企业 AI 从单点试点走向全企业规模化部署时遭遇的技术碎片化与“集成税”困境，导致强监管场景下治理与溯源失控。为此，Turing 提出了由全链路追踪、标准化评估、治理记录和闭环反馈构成的统一 AI 架构四大支柱，并依托统一控制平台打破部门工具孤岛。通过某大型金融客户年处理超百万张工单的标杆案例，展示了 81% 分类准确率、40% 全自动解决及年化预计节约 1500 万至 1800 万美元的显著成效。最终，演讲指出人类监督并非自动化的阻碍，而是推动系统持续演进的催化剂，并阐述了 Turing 连接前沿模型研究与企业业务落地的双前沿飞轮体系。 ## Atmosphere & style 演讲整体呈现出务实、严谨且充满工程理性的叙事风格，Vinu 以“骨子里依然是工程师”的幽默自嘲开场，迅速拉近了与技术决策者及架构师的距离。整个分享逻辑环环相扣，从直击痛点的架构反思平稳过渡到结构化方法论与详实的数据成果，语气沉稳笃定而不做空洞的概念包装。聆听过程如同观摩一场条理清晰的高级技术咨询，不仅能切身感受到一线落地踩坑后的深刻总结，更传递出将复杂工程混沌收敛为优雅秩序的掌控感与说服力。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) 企业推进生产级 AI 存在两大关键转折点：一是从 POC 验证业务价值与 ROI 并上线首个生产系统，二是从零散的单点 Agent 试点扩展到全企业范围的规模化部署。 - [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) 企业内部部门孤岛蔓延至 AI 领域，不同团队自选框架与分散工具造成技术栈严重碎片化，成为并行推进多用例时的主要协作与运维瓶颈。 - [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 碎片化技术栈会带来高昂的“集成税”，导致工程团队耗费数月维护底层基建，分散对实际业务成效与 ROI 的关注。 - [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 在金融等强监管行业，碎片化技术栈会导致决策溯源困难、无法界定人工与系统审批责任，且缺乏跨环境复现和合规审计能力。 - [04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) 统一 AI 架构要求在全链路追踪（Trace）、标准化评估（Evaluation）、治理审计记录（Governance）与闭环反馈（Feedback Loop）四大支柱上实现底层统一。 - [04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) 预置模型通常只能覆盖 60% 至 70% 的实际场景，必须依靠闭环反馈机制将人类介入的纠偏数据持续转化为 Prompt 优化与知识库修正。 - [08:14](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=494s) Turing 平台通过统一知识中枢（Knowledge Hub）整合结构化与非结构化数据资产，配合 RBAC 权限控制提升冷门与边缘用例的处理准确率。 - [10:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=655s) 在年处理超百万工单的金融标杆案例中，Turing 系统实现了 81% 的分类准确率与 40% 的全自动处置率，预计年化节省 1500 万至 1800 万美元运营成本。 - [10:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=655s) 人类在环（HITL）支持无缝嵌入至 Outlook、Teams 等现有工作流工具，降低介入门槛的同时为管理层提供合规信心并驱动系统进化。 - [12:50](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=770s) 通过统一控制平面降低首个用例风险后，在既有治理与评估底座支撑下，后续用例的上线速度将呈指数级提升。 - [12:50](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=770s) Turing 依托“前沿实验室”的模型微调能力与“企业级部署”的实际业务场景形成双向飞轮，将企业边缘反馈回流以持续优化模型基准。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) 企业规模化落地 AI 的双重转折点与碎片化困境 演讲者 Vinu Krishnaswamy 是 Turing 的解决方案架构总监。在开场自我介绍时，他幽默地调侃自己是一个“骨子里依然是工程师的人（engineer at heart）”，而这往往被戏称为“最危险的一种工程师”。Vinu 负责推动企业客户将生产级 AI 系统落地。根据 Turing 在市场上的观察，企业在推进生产级 AI 时通常会经历两个截然不同的关键转折点：第一个转折点是从概念验证（POC）走向首个生产系统，此时企业需要筛选出真正能够带来业务价值（Business Outcomes）与投资回报的用例；第二个转折点——也是本次演讲的核心——则是如何从一两个零散的 Agentic AI 系统部署，扩展到全企业范围内的规模化生产系统。 在迈向全企业规模的过程中，企业传统的“部门孤岛（Silos）”现象迅速蔓延到了 AI 领域。不同团队各自为战：有的团队使用特定 Agent 开发框架（如各类云 SDK、LangGraph 等），有的团队自研独立的评估体系，还有的团队搭建专属的“人类在环（Human-in-the-loop, HITL）”审核工具。当这种割裂发生在单个或两个用例时，问题尚不明显；但一旦企业试图并行推进多个生产级 AI 用例，碎片化的技术栈便会带来巨大的协作摩擦与运维负担，最终成为企业规模化落地 AI 的最大瓶颈。 - **第一转折点（POC 阶段到生产环境）**：核心在于评估业务成果，验证用例是否具备投入生产的商业价值。 - **第二转折点（单点试点到企业级规模化）**：核心在于打破技术选型与工具链的孤岛，实现跨部门协同。 - **技术碎片化的表现**：各团队自选框架（如各类 SDK、LangGraph）、分散的评估工具与互不连通的人工介入流程。 --- ### [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 碎片化技术栈的隐形成本：“集成税”与治理失控 企业碎片化技术栈所付出的高昂代价，被 Vinu 形象地称为“集成税（Integration Tax）”。由于各团队频繁现场编写并上线各种评估工具和审核系统，企业内部出现了大量互不相通的集成点。为了支撑这些分散的工具，工程团队往往需要花费数月时间去搭建和维护底层基础设施，导致宝贵的精力被技术琐事耗尽，而非聚焦于 AI 带来的实际投资回报（ROI）与业务成效。 这种架构缺陷在强监管行业（如金融服务业）尤为致命，直接导致系统在面临合规审计时无法回答一系列关键问题： 1. **决策溯源**：“Agent 为什么会做出这一特定操作？” 2. **审批归属**：“该操作是由系统自动批准的，还是经过了人类审核员的确认？” 3. **环境可复现性**：“在给定相同的 Prompt、上下文数据和输入条件下，该结果能否在测试环境或更低阶环境中稳定复现？” 4. **合规审计**：“Agent 在决策时遵循了哪些具体的业务规则？例如它是否准确调用了特定州的保险法规？” 5. **故障修复与反馈闭环**：“当 Agent 出错时，我们应当如何定位并修复问题？” ```mermaid flowchart TD accTitle: 碎片化 AI 技术栈导致的集成税与治理难题 subgraph Silos["企业内部技术孤岛 (Fragmented Stack)"] A["团队 A: 自研评估体系 + SDK"] B["团队 B: 第三方评测 + LangGraph"] C["团队 C: 独立的人工审核工具"] end subgraph Tax["集成税 (Integration Tax)"] D["多点基础设施搭建与维护消耗数月"] E["跨系统数据断层与黑盒决策"] end subgraph Failure["强监管场景下的失控风险"] F1["无法溯源 Agent 决策意图"] F2["无法区分自动审批与人工审核"] F3["跨环境无法稳定复现"] F4["缺乏策略与法规审计能力"] F5["修复与优化反馈链路断裂"] end Silos --> Tax Tax --> Failure ``` --- ### [04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) 统一 AI 架构的四大核心支柱 为了破解碎片化难题，Turing 提出了统一 AI 架构的设计理念。该架构虽然允许团队在前端 UI、连接器或编码框架上保持灵活性，但其底层必须由四大“基本盘（Bread and Butter）”能力构成： ```mermaid flowchart LR accTitle: 统一 AI 架构的四大支柱 subgraph UnifiedArchitecture["统一 AI 架构核心支柱"] direction TB T["1. 全链路追踪 (Trace)<br/>• Prompt 版本与输入上下文<br/>• 端到端工单工作流状态"] E["2. 统一评估体系 (Evaluation)<br/>• 黄金数据集 (Golden Dataset) 校验<br/>• 评估基准的持续动态演进"] G["3. 治理与审计记录 (Governance)<br/>• 业务规则与合规策略执行留痕<br/>• 自动化与人工审批权限分离"] F["4. 闭环反馈系统 (Feedback Loop)<br/>• 人类在环 (HITL) 纠偏数据回流<br/>• 提示词优化与知识库上下文修正"] end T --> E E --> G G --> F F -.->|"持续增强 Agent 表现"| T ``` 1. **全链路追踪（Trace）**：不仅仅是记录单个 Agent 的行为，而是完整记录包含输入内容、上下文、所使用的具体 Prompt 版本在内的完整执行链。更关键的是，它必须能够追踪端到端的工作流——例如当一张工单进入系统，编排 Agent（Orchestrator Agent）进行路径规划与任务分发，追踪系统必须能够全程跟进该工单直到处理完成。 2. **标准化评估（Evaluation）**：验证 Agent 是否持续符合预期表现。企业普遍引入了“黄金数据集（Golden Dataset）”，系统需检验所有交互是否通过各项基线测试，并推动黄金数据集随着业务发展持续自我演进。 3. **治理记录（Governance Records）**：沉淀业务规则与法规策略的执行记录，清晰留存系统在特定工单决策时所采纳的政策依据与合规审计证据。 4. **闭环反馈机制（Feedback Loop）**：这是当前大量生产级 AI 系统中最常缺失的关键一环。在实际业务中，预置模型通常只能覆盖 60% 至 70% 的场景，初始测试根本无法穷尽生产环境的所有边角用例。通过建立“人类在环（HITL）”的反馈回流机制，将人工介入的修正结果持续转化为 Prompt 优化和上下文调整，才能让 Agent 在生产环境中随着时间推移越变越聪明。 --- ### [08:14](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=494s) Turing 统一基础设施平台的模块化解构 基于上述四大支柱，Turing 构建了统一的企业级 AI 基础设施平台。该平台整合了以下核心组件，支持在统一控制平面下灵活扩展： - **自定义 Agent（Custom Agents）与编排集群**：支持工程团队快速创建临时 Agent 以验证各类业务假设，涵盖编排层与多 Agent 协同集群，极大加速了从测试、反馈到正式上线投产的管线流程。 - **自动化引擎（Automations）**：支持基于多元事件源进行定时调度或即时触发，例如由新数据写入或 ServiceNow 等系统接收到新工单直接触发相应的工作流。 - **多模态智能（Multimodal Intelligence）**：统一打通企业内部的结构化与非结构化数据源，包括各类文档（PDF、PPT、图像）以及来自 Snowflake 或各类数据湖的数据资产，为特定业务用例提供完备的上下文输入。 - **统一知识中枢（Knowledge Hub）**：在严格的访问策略与基于角色的访问控制（RBAC）下，汇聚所有业务上下文。即使新引入的边缘文档与其他历史数据关联较弱，统一知识中枢也能通过上下文连接提升 Agent 处理冷门用例的准确度。 - **集中管理与合规审计（Admin Control）**：内置企业级 RBAC 权限控制、合规审计日志，并配备专用的 AI 交互聊天工具，方便工程师对系统进行快速交互式验证与调试。 --- ### [10:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=655s) 标杆案例：某大型金融服务客户的工单智能处理实践 Vinu 重点分享了 Turing 为一家大型金融服务客户落地的“AI 驱动工单智能系统（AI-Driven Ticket Intelligence）”。该客户每年需要处理超过 100 万张工单，以往这些工单从分类到最终解决均依赖重度人工操作，涉及极其冗长且复杂的审批步骤。 针对该场景，Turing 部署了由多个专用 Agent 组成的协同网络，涵盖自动分类与具体处置等环节。依托统一平台内置的全流程治理记录与闭环反馈体系，系统能够端到端跟踪每张工单的流转状态与决策依据。 ```mermaid flowchart TD accTitle: 金融客户智能工单流转与业务成效 subgraph Intake["工单接入 (年均 100 万+ 张)"] T1["用户工单提交 (ServiceNow 等)"] end subgraph Processing["统一平台 Agent 协同处理"] A1["多模态智能理解与上下文检索"] A2["智能分类 Agent (命中率 81%)"] A3{"是否满足全自动处置条件?"} A4["自动化处置 Agent (完成 40% 自动解决)"] A5["分发至人类在环 (HITL) 交互端<br/>(Outlook / Teams / 定制应用)"] end subgraph Feedback["闭环反馈与收益"] F1["人工审核/修正结果反哺知识中枢"] F2["Prompt 与策略持续迭代优化"] R1["预期年化运营成本节省 1500万 - 1800万美元"] end T1 --> A1 A1 --> A2 A2 --> A3 A3 -- 是 --> A4 A3 -- 否 --> A5 A5 --> F1 F1 --> F2 F2 -.->|"增强 Agent 能力"| A1 A4 --> R1 A5 --> R1 ``` - **核心业务成效指标**： - **分类准确率**达到 **81%**； - **工单完全自动化解决比例**达到 **40%**； - **预期年化运营成本节省**预计达 **1500 万至 1800 万美元**。 - **无缝嵌入的人类在环设计**：尽管后台的人工审核与反馈逻辑在平台上高度统一，但前端接入却做到了高度轻量化——审核员可直接在现有的移动端、Outlook 邮件、Microsoft Teams 或定制应用中完成确认与干预，极大地降低了人工参与的门槛，从而持续推高系统的整体准确率。 --- ### [12:50](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=770s) 核心总结与 Turing 的双前沿使命 在演讲的最后阶段，Vinu 提炼了企业在规模化应用 AI 时的三大关键认知，并介绍了 Turing 的整体战略定位： 1. **统一控制平面打破架构瓶颈**：当企业跨越单点试点进入多用例并发阶段时，系统架构往往会成为最大的绊脚石。通过将评估（Evaluation）、可观测性（Observability）与人类在环（HITL）整合进统一的控制平面（Control Plane），首个用例上线的风险将被大幅降低；而在既有治理与评估底座的支撑下，后续第二、第三个用例的上线速度将呈指数级提升。 2. **人类监督（Human Oversight）是规模化扩张的催化剂**：表面上看，“让人类介入”与“AI 自动化规模扩张”似乎自相矛盾，但在实际企业落地中，严格的人类监督不仅为管理层和业务端提供了合规信心，更是驱动 AI 闭环持续进化的核心动力。 3. **Turing 的双前沿（Two Frontiers）飞轮体系**：Turing 的企业使命是“加速超级智能以驱动经济发展（Accelerate superintelligence to drive economic progress）”。公司主要在两大前沿阵地开展业务： - **前沿实验室（Frontier Lab）**：构建高质量数据评估与基准体系，直接参与前沿大模型本身的训练与微调； - **企业级应用部署（Enterprise Deployments）**：将先进模型落地到企业实际业务流中，实现工作流自动化并创造可量化的 ROI。 两大前沿之间同样存在着紧密的反馈闭环：企业实际业务场景中沉淀的反馈数据与真实边缘情况，反向输入给前沿实验室以优化模型训练基准，从而实现技术研发与商业落地的双向促进。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=4s) 骨子里依然是工程师的人（engineer at heart） — Vinu Krishnaswamy > [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 集成税（Integration Tax） — Vinu Krishnaswamy > [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) Agent 为什么会做出这一特定操作？ — Vinu Krishnaswamy > [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 该操作是由系统自动批准的，还是经过了人类审核员的确认？ — Vinu Krishnaswamy > [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 在给定相同的 Prompt、上下文数据和输入条件下，该结果能否在测试环境或更低阶环境中稳定复现？ — Vinu Krishnaswamy > [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) Agent 在决策时遵循了哪些具体的业务规则？例如它是否准确调用了特定州的保险法规？ — Vinu Krishnaswamy > [02:35](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=155s) 当 Agent 出错时，我们应当如何定位并修复问题？ — Vinu Krishnaswamy > [04:55](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=295s) 预置模型通常只能覆盖 60% 至 70% 的场景，初始测试根本无法穷尽生产环境的所有边角用例。 — Vinu Krishnaswamy > [12:50](https://www.youtube.com/watch?v=lM04Xpg3OwU&t=770s) 加速超级智能以驱动经济发展（Accelerate superintelligence to drive economic progress） — Vinu Krishnaswamy ## Mentioned Vinu Krishnaswamy, Turing, Ai4, LangGraph, ServiceNow, Snowflake, Microsoft Teams, Outlook ## Video description Vinu Krishnaswamy - Director, Solutions Architecture, Turing Most enterprise AI projects stall because of a fragmented stack: agents, evaluation, human review, and observability all managed separately. That complexity slows teams down and introduces risk before anything reaches production. This talk covers how leading organizations have moved to unified AI architectures that treat execution, evaluation, and human oversight as a single system, cutting development cycles and making governance hold in regulated environments. You'll leave with concrete examples and a clear picture of what it takes to ship AI that’s reliable in production [Source](https://www.youtube.com/watch?v=lM04Xpg3OwU)

Source quotes

[0:04] Vinu Krishnaswamy: 骨子里依然是工程师的人（engineer at heart） [2:35] Vinu Krishnaswamy: 集成税（Integration Tax） [2:35] Vinu Krishnaswamy: Agent 为什么会做出这一特定操作？ [2:35] Vinu Krishnaswamy: 该操作是由系统自动批准的，还是经过了人类审核员的确认？ [2:35] Vinu Krishnaswamy: 在给定相同的 Prompt、上下文数据和输入条件下，该结果能否在测试环境或更低阶环境中稳定复现？ [2:35] Vinu Krishnaswamy: Agent 在决策时遵循了哪些具体的业务规则？例如它是否准确调用了特定州的保险法规？ [2:35] Vinu Krishnaswamy: 当 Agent 出错时，我们应当如何定位并修复问题？ [4:55] Vinu Krishnaswamy: 预置模型通常只能覆盖 60% 至 70% 的场景，初始测试根本无法穷尽生产环境的所有边角用例。 [12:50] Vinu Krishnaswamy: 加速超级智能以驱动经济发展（Accelerate superintelligence to drive economic progress）

Source quotes

[0:04] Vinu Krishnaswamy: an engineer at heart, which apparently is the most dangerous kind of engineer there is [12:46] Vinu Krishnaswamy: accelerate superintelligence to drive economic progress

Original transcript

[0:04] Hi. Hey everyone. Uh we're here  today to talk about building [0:08] unified AI systems at I don't enterprise scale.  I think this is something that all enterprises [0:12] deal with on a day-to-day basis these days. But  before I get going on that, uh introducing myself, [0:17] I'm Venu Krishnaswamy. I'm director solutions  architecture at Turing. Uh I like to call myself [0:25] an engineer at heart, which apparently is the  most dangerous kind of engineer there is. Uh so [0:31] with that my I I drive initiatives across our  enterprise clients uh delivering production AI [0:37] systems for them. So uh and this is uh what we are  seeing as as Turing uh in in the market, right? So [0:45] um the way that we see it is that there's two  different inflection points when it comes to [0:49] deploying production at scale. One is when you  go from that POC stage to a production system, [0:55] right? That we've heard a lot about that in the  conference uh in the past couple of days. And this [1:00] is something that we see was there's a lot of POCs  that go on, but then when we go into production, [1:04] we really want to as as previous speakers were  talking about talk about the business outcomes [1:10] and uh and you know, uh figure out which ones  are worth taking to production. The second [1:15] inflection point is what really I'm going to focus  on today, which is basically how do you scale from [1:22] that one or two production uh deployments uh of  your agentic AI systems to an enterprise-wide AI [1:29] production system, right? And that is where uh you  know, it enterprise the name of the game is silos [1:37] and uh you know, different systems for different  things and that kind of starts to creep into [1:42] the AI world as well. You'll see multiple teams  doing multiple evaluation systems, for example, [1:48] multiple agentic frameworks that uh that will  pipe up some of them using ADK, some of them [1:53] using cloud SDK, some of them using uh Erlang  graph lengths method, if you will and different [1:58] human in the loop systems different you know that  that starts to cause silos and starts to cause [2:06] things really hard to manage when that happens  across multiple areas cases for the first use case [2:12] going to production grade for the second maybe  okay but as you go into multiple AI production [2:18] systems that starts to cause a problem and it  adds a lot of friction to your workflows um [2:24] as you put those systems into place and that  fragment fragmentation is starting to look [2:28] like the bottleneck for production AI systems  that we're seeing um what is it what is really [2:35] the cost of this fragmented stack is that if  you look at the systems up on there like the [2:39] agentic framework the evaluation platforms  and I I blame the I blame live coding for [2:46] all of this right there's multiple evaluation  frameworks that different teams just start [2:50] building up and and deploying to production if  you will you have multiple human review systems as [2:57] these AI production as these use cases start to  go to production and multiple systems start to [3:03] show up each of them is a different integration  point and you have to set up infrastructure to [3:09] to enable them right and that leads to what the  what the causes delays in these AI production [3:17] deployments is they spend months on setting it up  and managing this infrastructure rather than the [3:24] ROI and the outcomes that we want to get out  of these AI systems the integration tax as [3:29] as we would like to call it um where does it  actually exactly fail is answering these specific [3:36] questions right especially during as during we  work with a lot of financial services clients [3:41] and these are important questions to answer for  those clients for highly any highly regulated [3:46] industry like why did the agent do something  right Uh, may be one example that we always [3:53] talk about because it's a real example  that we've implemented is is a ticketing [3:56] resolution system, right? Why did a particular  agent take this action? Uh, who approved it, [4:03] right? Was it automatic approval or was it  a human in the loop approval? Uh, who uh, [4:08] can we reproduce it, right? Uh, not just in  production, but also in lower environments. [4:13] If we take the same prompt, the same  context, and the and the same uh, [4:18] you know, uh, data set and the inputs, are  we able to reproduce it in multiple places? [4:23] Uh, how can we audit it? Auditing meaning not just  related to what the agent did, but what policies [4:30] did the agent pick up to follow, right? Where  did it pick up the right state's insurance uh, [4:36] laws to follow and make a decision? Did it take  uh, you know, so those kind of policies as well as [4:42] how do we fix it, right? I'd what needs to  change for it to fix it. That feedback loop [4:47] also becomes hard to uh, hard to uh, figure out  uh, because of this fragmented um, approach. So, [4:55] this is what uh, as uh, as a company that  sees this quite a bit across multiple clients, [5:00] this is what in our mind a unified architecture  looks like, right? These and I'm not saying [5:07] these are the only four things that it needs to  care about. Uh, but these are the most important [5:11] things. It also needs to have some flexibility so  different teams can have different uh, frameworks [5:18] that they uh, they want to uh, you know, code in  or different frameworks that they want to follow, [5:24] or different um, UI or different connectors that  they want to have. All of that is still important, [5:29] but these four kind of are the bread and butter  for any unified system that needs to be up there [5:36] uh, for your production use cases. One is  a trace, which is as it says, every action [5:42] that an agent takes, including any input, the  context that it has, the prompt it's using. [5:49] Prompts may change over time, so  the prompt version that it's using, [5:53] uh, all of that has to has to be logged. And  not just for what a particular agent is doing, [5:59] right? But for a particular workflow. Say a  ticket comes in and that ticket uh, there's an [6:05] orchestrator agent that is going to go and figure  out what needs to happen based on the ticket. [6:12] What is the decision that it's making for that one  particular ticket? You have to be able to follow [6:16] that ticket to its completion, right? So that  is the trace that we're talking about, not just [6:22] just an agent. Uh, an evaluation, which is is our  our agents functioning properly as expected. Uh, [6:31] a lot of organizations have adopted the golden  data set, right? And what is the golden data set? [6:36] How is it influencing uh, the and is the  golden data set passing through all of the uh, [6:43] checks and balances that we have in it? And does  the golden data set itself have to evolve, right? [6:48] That is also something that we have to figure out.  Uh, and the unified architecture helps us there. [6:53] Governance records, again, this is the policies  that were followed, the business rules that were [6:58] followed. And what were followed for a particular  ticket that's coming in in that example, [7:03] right? So that uh, that as well as a  feedback loop. And this is one that we see [7:08] that a lot of production systems right  now is aren't is not taking into account [7:13] the feedback loop. When a particular agent or  uh, or a model is deployed for a particular [7:20] uh, use case. Yeah, so the feedback loop is  what is missing in most of use cases right now, [7:25] uh, especially when you're in production. A model  could get you 60-70% of the way there, but in [7:30] your initial testing, you probably have not seen  all the use cases that could [clears throat] hit [7:36] the production system, right? So that is where  we need to be careful about the feedback loop. [7:43] As you have human in the loop configured and  as you have the human in the loop um configured [7:50] and that feedback starts to flow through, you  may want to change your prompts, for example, [7:55] or you may want to change your um you know,  change your prompts, you may want to change the [8:00] context that you're giving it and that feedback  loop will improve the agent over time. So, that [8:05] is also an important piece to have across this  um across this. Okay. So, this is how As Turing [8:14] we solve for it on a unified uh platform that we  built. Uh so, the uniform infrastructure platform [8:21] has a few different uh things that we uh that  uh as best practice uh needs to be included and [8:28] this is re- really what uh it comes down to  is custom agents. Let me start with that. Is [8:35] being able to create agents on the fly to test  out your hypothesis, right? This helps from the [8:43] feedback loop, from the testing perspective,  as well as from uh you know, POC uh [8:51] to production pipeline perspective, right? That is  That is important. Custom agents, building agents [8:56] or clusters of agents that have an orchestration  layer on top of it to uh to test out. [9:02] The other's automations, that means uh scheduling  or triggering agents based on certain uh inputs. [9:09] That could depend on your data sources, that  could depend on time, that could depend on [9:14] a new ticket coming in in into your ServiceNow or  something like that. Uh this is all built on top [9:20] of multimodal intelligence, right? And that is key  uh because enterprises have multiple data sources, [9:28] have a lot of PDFs, PowerPoints, images, all  documents, images, as well as um as data that [9:35] is coming from your Snowflake or you're from any  of your data lakes, right? So, that is uh kind of [9:42] bringing them all together to build context around  each use case. Obviously, this is all subject to [9:48] your access policies and your role-based access  control. Uh so, all of that needs to still be [9:53] taken into context, but having a central uh  governance, a central most mobile intelligence [10:00] that then builds the knowledge hub, which is  the context that all of yeah, all of your data [10:05] feeds into and can be used for the feedback loop.  Say there's a new document coming in that is not [10:11] something that has any any relevance to any other  data it's really marginally connected to other [10:20] data data that you have, having that contextual  knowledge will help in the feedback loop as well. [10:27] And all of that is tied into an admin control,  which again respects your back as well as your [10:35] audit logs and compliance. And this AI chat is  really, really important as a as a testing tool [10:42] as well in this platform. So, that is how  we resolved how we think about a unified AI [10:50] uh platform. Uh one case study that I can I've  been kind of alluding to I've been talking about [10:55] is something that we deployed uh for one of our  clients is an AI-driven ticket intelligence. [11:02] Uh the business case basically is a financial  services client that had about a million plus [11:08] uh tickets that are uh you know, coming per year,  which are all kind of manually uh classified. [11:16] There's multiple uh steps to this as you  can uh as you can probably attest to in your [11:21] organization as well. Uh there's a classification  and there's a resolving as well. So, it required a [11:28] quite a few agents to uh you know, deploy. Uh but  as a as we are doing it as part of the platform, [11:36] uh, it became clear that the the feedback  loop that we built in and the governance [11:40] that we built in kind of helped track these  tickets to completion, uh, as well as have the, [11:48] uh, the closed-loop feedback coming through.  Uh, with that the business the the business [11:54] results were clearly as only as as you can  see there's, uh, 81% accuracy, uh, that we, [12:01] uh, hit for classification and then 40% of the  tickets were, uh, resolved successfully. The human [12:08] human-in-the-loop is really, really key in any any  of these unified platforms. It can it can look, [12:15] uh, like more even though human-in-the-loop  in the platform is kind of centralized, [12:22] it can still come to your mobile, right? So, it  could be Outlook, it could be Teams, it could be [12:27] a separate application that you build on top of  this platform to, uh, to, you know, enable it, [12:32] but it's key to improve these accuracy rates  over time. Uh, with um, and the really the [12:39] ROI was the 15 to 18 million projected annual  operational savings that you see down there. [12:46] All right, three takeaways from this, uh, that I  want to talk through. Um, when we move from one [12:54] use case to multiple use cases in production, the  the architecture starts to become the bottleneck, [13:02] right? Uh, having a unified  platform, uh, will help with, uh, [13:07] with having one control plane, if you will, for  all your AI agents and can, uh, you know, can [13:15] use evaluation, observability, human-in-the-loop,  all kind of plugged into that one control plane [13:21] will help with taking more of your, uh,  AI agents to production or workflows to [13:28] The first the couple of things that we've seen as  we as we go through this process for our clients [13:33] is that the first use case having this control  plane makes it a little more little less risky, [13:41] right? [laughter] And a little more control  to get that first use case to production. [13:45] But as you go into a second and third use cases,  it starts to become a lot faster because you have [13:52] the control plane and you have the evaluation,  observability, governance controls already in [13:57] place in the platform. The human oversight piece  and the feedback piece is also kind of critical [14:04] here. I know it kind of the human oversight scales  AI piece may seem counter intuitive to start with, [14:12] but that is also something that will give  confidence to leadership to businesses that [14:18] you know, there's some oversight that will  keep improving the AI piece as we go forward. [14:25] All right. Before I close out, I just wanted  to give a quick introduction to Turing. [14:30] Our mission is to accelerate superintelligence  to drive economic progress. We operate [14:37] on two different frontiers. One is the frontier  lab where we build data evaluations and benchmarks [14:43] to train the AI models themselves and then  we take those AI models and deploy them [14:48] to into enterprises to automate  workflows and give measurable ROI. [14:53] There's a feedback loop that goes through  that there as well within Turing. That's why [14:57] I was talking a lot about feedback loops  before as well. So, thank you everyone.