Google Architect Pushes Agentic RAG to Shift AI From Search to Action

Agents That Think, Fetch, and Act: The Future of RAG in Autonomous Systems With Google — Ai4

Ai4

Static RAG leaves execution burdens on humans because linear retrieval cannot invoke tools or adapt to errors, requiring multi-step agentic loops that autonomously plan, retrieve, and execute workflow tasks.

AI-assisted summary of Agents That Think, Fetch, and Act: The Future of RAG in Autonomous Systems With Google — original source. Check the original for full context.

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## Background Published by Ai4 on YouTube on August 26, 2025, this presentation features Jasleen Singh, Staff Solutions Architect for Generative AI at Google. The session explores how agentic architectures transform Retrieval-Augmented Generation (RAG) from a passive information lookup tool into dynamic, autonomous systems capable of reasoning, planning, and executing real-world actions via APIs.

## TL;DR While traditional linear RAG excels at retrieving context and minimizing hallucinations, it remains fundamentally static and unable to execute actions on behalf of the user. Evolving into agentic architectures pairs large language model reasoning with API tool execution and tiered memory, allowing RAG to serve as dynamic sensory input rather than a one-shot pipeline. By continuously fetching knowledge, planning, and acting across multi-step workflows, agents can adaptively decompose complex tasks, self-reflect, and recover from failures. To successfully realize this shift from passive knowing to active doing, organizations must adopt phased rollouts, leverage managed platforms, and maintain rigorous responsible AI guardrails.

## Atmosphere & style The talk delivers an authoritative, forward-looking, and pragmatic keynote experience grounded in real-world software architecture. Jasleen Singh establishes an engaging narrative flow, transitioning smoothly from relatable metaphors—such as contrasting passive trivia champions with proactive personal assistants—into rigorous architectural breakdowns and decision workflows. The session feels intellectually sharp and inspiring yet deeply grounded, balancing enthusiasm for autonomous agent capabilities with engineering discipline around error recovery, guardrails, and enterprise reliability.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) The AI paradigm is fundamentally shifting from passive question-answering systems to autonomous agents capable of dynamic real-world execution. - [01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) Traditional RAG operates as a linear three-stage pipeline (Retrieve, Augment, Generate) designed primarily to ground LLMs in external data and reduce hallucinations. - [03:32](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=212s) Static RAG suffers from severe operational limits: it lacks a native 'doing capability' to call APIs, relies on one-shot interactions, and cannot independently execute complex decision trees. - [05:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=307s) The anatomy of an AI agent integrates a Brain (reasoning LLM), Hands (tools and APIs), and Memory (short-term context and long-term RAG knowledge). - [07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) Agentic systems transform RAG from a static one-off search into a dynamic 'Fetch, Think, Act' loop used repeatedly across iterative problem-solving phases. - [09:06](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=546s) Agents handle ambiguous objectives through task decomposition, breaking complex business problems into manageable, sequential subtasks. - [09:06](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=546s) Built-in reflection loops provide resilience against API errors and unexpected constraints, enabling agents to dynamically pivot strategies or escalate to human experts. - [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) Enterprise adoption should follow a phased approach, starting with bounded, high-impact use cases to demonstrate measurable ROI before scaling. - [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) Granting agents operational agency over enterprise systems demands robust Responsible AI frameworks, transparent reasoning traces, and deterministic guardrails. - [13:06](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=786s) The future of AI is defined by the transition from 'knowing' to 'doing,' moving from merely diagnosing problems to autonomously resolving them.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) From Question-Answering to Autonomous Action

The paradigm of artificial intelligence is experiencing a fundamental shift away from passive conversational interfaces toward dynamic execution. Users are intimately familiar with asking an AI assistant to fetch trivia, identify the capital of France, or summarize a massive report, receiving an immediate, well-formulated answer. However, the immediate subsequent desire is execution: asking the system if it can actually "do something about it"—whether that means booking the flight to Paris or automatically applying the recommendations from that report.

Addressing this "can you do something moment" requires transforming Retrieval-Augmented Generation (RAG) from a passive question-answering machine into an active problem solver. Instead of functioning merely as a "trivia champion," the next generation of AI systems operates as a "super efficient personal assistant who happens to be a genius and can also get things done."

[01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) Deconstructing Traditional RAG and Its Core Strengths

Traditional Retrieval-Augmented Generation follows a linear three-stage pipeline designed to ground foundation models in verifiable external data:

```mermaid flowchart LR accTitle: Traditional RAG Pipeline subgraph Pipeline ["Traditional Linear RAG"] A["1. Retrieve<br/>Query Knowledge Base"] --> B["2. Augment<br/>Inject Context into LLM"] B --> C["3. Generate<br/>Factual Grounded Output"] end ```

The process begins with retrieval, where the system extracts relevant documents or passages from a knowledge base, live internet index, or corporate wiki in response to a prompt. Next, in the augmentation phase, these extracted contexts are passed directly to the large language model as supplemental prompt context, acting like a librarian delivering the exact required chapters. Finally, the generation step produces an output that is grounded and factual, substantially reducing hallucinations and providing citations for its claims.

- **Retrieve:** Dynamically identifies relevant chunks across enterprise databases, internal wikis, or external web indices. - **Augment:** Injects verified source material directly into the model's working prompt context. - **Generate:** Synthesizes factual, referenced responses without fabricating ungrounded claims.

[03:32](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=212s) The Bottlenecks and Functional Limits of Static RAG

Despite its effectiveness as a research assistant, traditional RAG hits significant functional boundaries when applied to real-world workflows. It operates much like "the smartest person in the room who then asks you to go execute their brilliant plan," leaving the actual burden of operational work on the human user.

First, standard RAG relies on static, one-shot interactions without iterative back-and-forth exploration or proactive next steps. Second, it fundamentally lacks a "doing capability"—it can calculate the fastest route to a destination but cannot hail a cab, or summarize a policy update without pushing the changes into enterprise systems because it cannot natively interact with APIs and external tools. Third, its cognitive capabilities are largely restricted to information synthesis rather than complex, multi-step decision trees. Finally, the principle of "garbage in, garbage out" persists: flawed retrievals or inaccurate underlying data inevitably yield distorted answers. Bridging this operational divide requires autonomous agents capable of dynamic decision-making.

[05:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=307s) The Anatomy of an AI Agent

An AI agent is an integrated architecture engineered to perceive its environment, reason through inputs, devise plans, and execute sequential actions to accomplish a designated objective. Rather than existing as a standalone prompt wrapper, an agent comprises distinct, interconnected functional units working in unison.

```mermaid flowchart TD accTitle: Core Anatomy of an AI Agent subgraph AgentArchitecture ["AI Agent Core Anatomy"] Brain["Brain (LLM)<br/>Reasoning, Planning & Logic"] Hands["Hands (Tools & APIs)<br/>CRM, Email, DB Operations"] Memory["Memory Systems<br/>Short-term Context & RAG Base"]

Brain <--> Hands Brain <--> Memory end

Loop(["Autonomous Loop: Plan ➔ Retrieve ➔ Execute ➔ Reflect"]) AgentArchitecture -.-> Loop ```

At the core sits the **Brain**, powered by the large language model, which handles high-level intelligence, contextual problem analysis, and step-by-step planning. The **Hands** represent external tools and APIs, giving the agent agency to manipulate external environments—such as modifying CRM records, querying live production databases, or triggering emails. The **Memory** module maintains coherence across time through two tiers: a vast long-term knowledge base anchored by RAG for factual grounding, and short-term working context to track ongoing conversational progress and intermediate task states. Connecting these systems is the continuous execution **Loop**, an iterative cycle of planning, retrieving, executing, and reflecting that drives resilient autonomous behavior.

[07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) Orchestrating RAG: Fetch, Think, and Act

Rather than treating RAG as an isolated pipeline, autonomous agents actively orchestrate retrieval mechanisms as dynamic sensory inputs and memory systems. The agent does not simply run a single search; it continuously dictates what information to retrieve, when to fetch it, and how to apply it across iterative problem-solving phases.

- **Fetch:** The agent dynamically initiates targeted retrieval operations across multiple steps to gather the exact contextual evidence required to resolve intermediate ambiguities. - **Think:** The agent synthesizes retrieved knowledge within its reasoning core, evaluating environmental constraints and planning tool invocations. - **Act:** The agent leverages its API toolset to execute tangible operations in external systems, such as dispatching notifications, writing database records, or orchestrating downstream workflows.

[09:06](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=546s) Task Decomposition, Dynamic Planning, and Resilience

Real-world problems cannot be resolved through rigid, pre-programmed scripts; they require adaptive task decomposition. Agents function much like technical project managers, taking a large, ambiguous objective and decomposing it into manageable sequential subtasks.

```mermaid flowchart TD accTitle: Agent Decision and Recovery Flow A["Customer Issue Received"] --> B["Fetch Order Details & Analyze"] B --> C{"Complexity Assessment"} C -- "Simple" --> D["Invoke Refund API"] C -- "Complex" --> E["Summarize & Escalate to Human Expert"] D --> F{"API Execution Check"} F -- "Success" --> G["Confirm Resolution"] F -- "Failure / Error" --> H["Reflect, Pivot Strategy, or Escalate"] ```

In an enterprise customer service setting, an agent first fetches order history and diagnoses the reported defect. If the issue is simple and meets policy parameters, the agent directly invokes an internal refund API; if the dispute is multifaceted, it prepares an analytical summary and escalates the ticket to a human specialist. If an API call fails or the user introduces unexpected constraints, the agent analyzes the failure, refines its strategy, attempts alternate remediation paths, or routes the context cleanly to human staff. This continuous feedback loop of reasoning, execution, and reflection provides the resilience necessary to navigate unpredictable edge cases.

[11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) Strategic Enterprise Adoption and Responsible AI

Deploying autonomous agentic systems into an enterprise environment demands a deliberate, phased strategy rather than an unmanaged full-scale rollout. Organizations must begin by identifying bounded, high-impact use cases where automated tool execution and retrieval can demonstrate quantifiable return on investment early, building institutional confidence.

To accelerate implementation, teams should leverage managed cloud AI services and specialized agent platforms that handle underlying infrastructure, enterprise security controls, and elastic scaling, freeing developers to concentrate on agent logic and domain capabilities. Once deployed in real-world test environments, architectures must be iteratively refined through rigorous evaluation loops. Most critically, because autonomous agents possess operational capabilities and access to private enterprise data stores, prioritizing responsible AI practices—centered around transparent reasoning traces, robust data security, and deterministic guardrails—serves as an essential operational and ethical foundation.

[13:06](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=786s) The Future of AI: Moving from Knowing to Doing

The central transition defining the modern AI landscape is the evolution from passive knowledge retrieval to autonomous real-world execution: "The future of AI is not just about knowing, it's about doing." Autonomous systems are shifting from diagnosing that an operational bottleneck exists to proactively executing the steps required to resolve it.

By automating sophisticated, multi-step business workflows while maintaining strict factual grounding, autonomous agents unlock unprecedented operational efficiencies. Concurrently, they elevate end-user and stakeholder interactions through proactive, rapid, and contextually personalized resolutions. Entering this era marks the arrival of true autonomy—systems that can reliably think, fetch, and act.

## Notable quotes > [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) do something about it — Jasleen Singh

> [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) can you do something moment — Jasleen Singh

> [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) trivia champion — Jasleen Singh

> [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) super efficient personal assistant who happens to be a genius and can also get things done. — Jasleen Singh

> [03:32](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=212s) the smartest person in the room who then asks you to go execute their brilliant plan — Jasleen Singh

> [03:32](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=212s) doing capability — Jasleen Singh

> [03:32](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=212s) garbage in, garbage out — Jasleen Singh

> [13:06](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=786s) The future of AI is not just about knowing, it's about doing. — Jasleen Singh

## Mentioned Jasleen Singh, Google, Ai4, Retrieval-Augmented Generation (RAG), CRM

## Video description Jasleen Singh, Staff Solutions Architect, Generative AI, Google

Abstracts

The next evolution of RAG isn't just about better answers - It's about autonomous action. This session explores how agentic architectures are transforming RAG from a passive information retrieval tool into a dynamic system that can reason, plan, and act. We'll dive into how agents leverage RAG pipelines to not only retrieve and synthesize knowledge, but also to make decisions, interact with APIs, and adapt workflows based on real-time feedback.

Drawing from real-world examples and cutting-edge research, this talk with cover: * The anatomy of an agent-enabled RAG system * Orchestration strategies for multi-step reasoning and task decomposition * Challenges in grounding, latency, and tool use * Emerging patterns for scalable deployment in enterprise and research settings

If you're building or scaling AI systems that need to reason over large knowledge bases, adapt to changing environments, or autonomously complete complex tasks - this session is for you.

[Source](https://www.youtube.com/watch?v=fq9ncIHnGeY)

中文简报

谷歌架构师力推Agentic RAG，推动AI从搜索转向行动 静态RAG将执行负担留给了人类，因为线性检索无法调用工具或自适应错误，这需要能够自主规划、检索并执行工作流任务的多步骤智能体循环。 虽然传统的线性RAG在检索上下文和减少幻觉方面表现优异，但它本质上仍是静态的，无法代表用户执行操作。演进为智能体架构后，大语言模型的推理能力与API工具执行及分层记忆相结合，使RAG成为动态的感官输入，而非单次交互的流水线。通过在多步骤工作流中持续获取知识、规划和行动，智能体能够自适应地分解复杂任务、进行自我反思并从故障中恢复。为了顺利实现从被动认知到主动执行的转变，组织必须采取分阶段推进策略，利用托管平台，并恪守严格的负责任AI安全护栏。 AI 正经历从被动问答向自主执行的根本范式转变，致力于解决传统静态 RAG 缺乏 API 调用能力以及仅依赖单次交互的局限。 (0:04) AI Agent 核心架构整合了负责规划推理的“大脑”（LLM）、操作工具与 API 的“双手”以及长短期“记忆”，将 RAG 升级为动态的“获取、思考、行动”（Fetch, Think, Act）闭环。 (5:07) 面对复杂的业务目标，Agent 具备任务分解与自适应规划能力，并通过内置的反思循环在 API 报错或遇到限制时动态调整策略或升级转交人工处理。 (9:06) 企业推进 Agent 架构落地应采用分阶段策略，优先在边界清晰且回报明确的场景验证 ROI，同时依托透明推理轨迹与安全护栏践行负责任 AI 原则。 (11:07)

Reading summary

Artificial intelligence systems are shifting from passive informational retrieval toward autonomous execution, turning Retrieval-Augmented Generation from a question-answering tool into an active problem solver. According to the presentation, modern architectures combine language models with external APIs and dynamic memory loops to move systems from merely 'knowing' to directly 'doing.' Limits of Passive Linear Retrieval Pipelines (3:32) Traditional Retrieval-Augmented Generation (RAG) follows a linear, three-stage sequence: retrieving relevant passages from databases or wikis, augmenting model prompts with this verified context, and generating factual, citation-backed answers. While this pipeline reduces hallucinations and grounds responses, it functions primarily as an informational research assistant rather than an operational tool. Because standard RAG relies on static, one-shot interactions, it cannot hail a cab after finding a route or update enterprise software after summarizing a policy change. It lacks native tool-use capabilities, multi-step decision logic, and iterative exploration. Furthermore, the architecture remains constrained by the principle of 'garbage in, garbage out,' leaving the burden of executing recommendations entirely on human users. Core Architecture: Brain, Hands, and Dynamic Loops (5:07) To bridge the gap between advice and action, autonomous agents integrate three interconnected functional components. The 'Brain' uses the large language model for contextual reasoning, problem analysis, and planning. The 'Hands' encompass external tools and APIs that modify environments, such as updating CRM records, querying production databases, or triggering emails. The 'Memory' system integrates short-term working context to track ongoing task states alongside a long-term RAG knowledge base for factual grounding. Rather than treating retrieval as an isolated prelude, agents orchestrate RAG within an ongoing autonomous loop. The agent executes a continuous cycle of fetching targeted evidence to resolve intermediate ambiguities, thinking through environmental constraints, acting via tool invocations, and reflecting on operational outcomes. Adaptive Task Decomposition and Error Recovery (9:06) Real-world operational environments require agents to act like technical project managers by decomposing ambiguous objectives into manageable sequential subtasks. In an enterprise customer service scenario, an agent first retrieves order history and evaluates the reported defect. When the issue meets defined policy thresholds, the agent directly calls an internal refund API; if the dispute is complex, it prepares an analytical summary and escalates the ticket to human specialists. System resilience depends on dynamic error handling. If an external API call fails or new constraints emerge, the agent analyzes the breakdown, pivots its strategy, attempts alternative remediation paths, or hands off context cleanly to human staff, preventing brittle failures in unpredictable edge cases. Enterprise Deployment Strategy and Responsible Governance (11:07) Deploying autonomous systems requires a phased organizational strategy rather than an unmanaged rollout. Organizations are advised to target bounded, high-impact use cases where automated tool execution and retrieval demonstrate early, quantifiable return on investment. Leveraging managed cloud AI platforms allows teams to rely on existing infrastructure, security controls, and elastic scaling while focusing resources on domain logic. Because autonomous agents possess operational capabilities across private enterprise data stores, implementation requires strict responsible AI guardrails. Deployments must enforce transparent reasoning traces, robust data security, deterministic boundary controls, and rigorous evaluation loops before systems operate independently. The presentation concludes that autonomous systems will define the next phase of enterprise AI by proactively resolving operational bottlenecks rather than merely diagnosing them. However, realizing reliable autonomy depends on maintaining strict factual grounding, deterministic safety guardrails, and clear escalation protocols for complex or anomalous edge cases.

精炼阅读摘要

演讲指出，人工智能正从被动的知识问答工具演进为具备执行能力的自主系统；通过将检索增强生成（RAG）重构为自主智能体的感知与记忆中枢，AI系统得以跨越单一的信息检索边界，直接调用外部工具以解决现实业务问题。 传统RAG的基础优势与行动力瓶颈 (1:28) 传统检索增强生成（RAG）建立在“检索、增强、生成”三阶段的线性流程之上：系统首先响应查询并从企业数据库、知识库或网络索引中提取相关文档切片；随后将这些外部上下文注入大语言模型（LLM）的提示词中；最终生成具备事实依据且附带引用来源的回答。演讲指出，这种机制显著降低了基础模型的幻觉概率，在回答事实性问题与总结长篇报告等知识密集型任务中发挥了高效的研究助手功能。 然而，演讲强调静态RAG在真实业务流程中存在明显的功能边界。传统RAG主要依赖单次、静态的交互，无法展开多轮探究或主动推进任务。更关键的局限在于其欠缺“行动能力”——它能够为用户规划出最优路线却无法代为叫车，能够提炼企业政策更新却无法调用API将变更推送到业务系统，使得执行负担依然停留在人工端。此外，传统RAG缺乏多步决策能力，且受制于“输入差、输出差”的原则，底层数据的检索缺陷会直接导致生成结果失真。 智能体系统架构与检索编排逻辑 (5:07) 为填补从认知到执行的鸿沟，自主智能体被设计为能够感知环境、逻辑推理、制定计划并执行连续动作的综合架构。其核心解构为四大功能单元：以大语言模型为核心的“大脑”，负责高级推理、问题剖析与分步规划；作为“双手”的工具和API接口，使系统具备操作CRM系统、查询生产数据库或发送邮件等干预外部环境的权限；由RAG长期知识库与短期工作上下文组成的“记忆”模块，用于维持事实支撑与多轮交互状态；以及贯穿始终的“计划—检索—执行—反思”自主循环回路。 在此架构中，智能体不再将RAG视为孤立被动的检索管线，而是将其动态编排为“获取（Fetch）、思考（Think）、行动（Act）”的有机系统。智能体能够自主判断在何种阶段发起多步定向检索，以补全中间推理所需的具体事实依据；随后在思考核心中综合评估环境约束并规划工具调用；最终通过API在外部系统中执行具体操作，从而将知识检索直接转化为驱动现实流程的行动要素。 任务自适应拆解与容错反思机制 (9:06) 现实中的业务挑战通常高度模糊，无法通过固化的预设脚本解决。演讲提出，智能体需像技术项目经理一样运作，自适应地将庞大目标分解为可执行的子任务序列。以企业客服场景为例：系统接收客户问题后，首先检索订单历史并诊断问题性质；若属于规则允许的简易情形，则直接触发退款API完成处理；面对多方纠纷等复杂案件，系统则会自动生成分析摘要并上报人工专员处理。 与传统自动化流程不同，智能体在遭遇异常或API调用失败时展现出动态弹性。系统不会直接中断报错，而是进入反思逻辑，评估失败诱因并调整执行策略，尝试备选补救路径；若仍无法克服阻碍，则将完整业务上下文平滑转交给人工作业。这种融合了推理、执行与反思的持续反馈循环，为企业处理不可预知的边缘个案提供了容错保障。 企业推进策略与负责任AI治理 (11:07) 在企业环境推进自主智能体落地时，演讲建议采取谨慎的分阶段采纳路线，避免未经管控的全面铺开。组织应首先选择业务边界清晰、高影响力的应用场景开展试点，通过工具调用与自动化检索验证可量化的投资回报率，借此建立组织内部的信心。在技术实施层面，团队可借助成熟的托管云AI服务与专业智能体平台，依托现成的基础设施管理安全权限与弹性扩缩容，从而集中资源优化业务专有逻辑。 与此同时，鉴于智能体拥有操作实际系统与读取企业私有数据的实体权限，负责任的AI治理规范成为落地的先决条件。企业在真实测试环境中必须建立严格的评估闭环，重点构建清晰透明的推理轨迹追踪、严密的数据安全控制以及确定性的安全护栏（guardrails），以防止越权操作或决策失控带来的合规与运营风险。 演讲总结认为，人工智能正迎来从“单纯知晓”向“实际行动”的根本性转变，其核心价值在于以严谨的事实检索为底座，自主执行复杂的跨系统业务流程。然而，随着系统被赋予直接干预生产环境的权限，企业在享受效率红利的同时，仍需持续解决复杂决策树的可靠性验证与安全治理边界等现实挑战。

Detailed notes — zh

## Background 该视频由 Ai4 频道于 2025 年 8 月 26 日发布在 YouTube 平台，由 Google 生成式 AI 资深解决方案架构师 Jasleen Singh 带来主题演讲。内容主要探讨了智能体架构如何将 RAG 从被动的信息检索工具，演进为能够进行多步推理、规划决策与自主行动的动态系统，并介绍了其核心架构、落地挑战与企业级部署模式。 ## TL;DR 演讲系统阐述了 AI 从传统“问答机器”迈向具备“思考、检索与行动（Think, Fetch, and Act）”能力的自主智能体演进历程。讲者剖析了经典 RAG 在静态单轮交互和缺乏执行力等方面的局限，提出了由大脑（LLM）、双手（工具/API）、记忆（RAG与上下文）及反思闭环构成的智能体解剖学架构。内容进一步解析了智能体动态编排 RAG、任务拆解与自适应容错的机制，并为企业落地给出了小步快跑与践行负责任 AI 的明确路径。最终指出，能够主动解决问题的新一代自主智能体将从根本上重塑企业的运营效率与交互体验。 ## Atmosphere & style 整场演讲条理清晰、节奏明快，兼具前瞻性的技术视野与接地气的架构实战指导。讲者运用生动形象的比喻将复杂的 AI 智能体原理解构得通俗易懂，使技术探讨既富有趣味性又不失严谨深度。叙事从对传统技术瓶颈的理性审视，逐步升华至对自主智能新时代的展望，整体氛围严谨务实且充满鼓舞力量。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) AI 范式正经历从被动“问答机器”向主动“行动英雄”的根本转变，核心不仅在于给出答案，更在于直接解决问题与执行任务。 - [01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) 传统 RAG 通过检索、增强与生成三步有效缓解了大语言模型的幻觉问题，但本质上仍属于被动的信息提供者。 - [01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) 传统 RAG 在实际业务中面临四大瓶颈：静态单轮交互模式、缺乏 API 调用等执行能力、多步推理深度受限，以及极度依赖检索数据质量。 - [05:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=307s) AI 智能体的核心定义在于能够感知外部环境、对信息展开推理、自主制定行动计划，并执行具体操作以达成目标。 - [05:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=307s) 智能体由大模型驱动的“大脑”、连接工具与 API 的“双手”，以及融合 RAG 与短期上下文的“双层记忆”系统协同构成。 - [07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) 智能体并非单纯调用 RAG，而是在“编排” RAG，将其作为感官与记忆库，在多轮迭代中动态决定何时、以何种方式检索何种知识。 - [07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) 在面对复杂现实业务时，智能体能够像项目经理一样将宏观任务拆解为可执行步骤，并根据环境反馈动态选择执行路径。 - [07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) 自适应反思与容错机制赋予了智能体应对异常的核心韧性，在工具报错或遇阻时能自我修正策略或平滑转交人工专家。 - [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) 企业落地智能体应避免激进的“大爆炸”模式，建议从边界清晰、ROI 明确的高价值封闭场景切入，并借助成熟托管平台加速部署。 - [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) 随着智能体自主决策权限与敏感数据接入程度的提升，透明度、安全隔离和负责任 AI 成为赢得用户信任的商业底线。 - [13:39](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=819s) AI 的未来核心在于“知行合一（Doing over Knowing）”，自主智能体将通过多步骤复杂工作流的端到端自动化，重塑企业的日常运营范式。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) 从“问答机器”到“行动英雄”：AI 范式的演进 演讲开篇探讨了一个具有变革意义的主题：能够“思考、检索并行动（Think, Fetch, and Act）”的智能体，以及 RAG（检索增强生成）在自主系统中的未来。在过去，用户与 AI 系统的交互模式非常固定：询问法国的首都在哪里，或者让它总结一份厚重的长篇报告，AI 瞬间就能给出答案，就像身边随时待命的私人助理。然而，用户的思维很自然会迈向下一个逻辑步骤——“你能不能真正去把事情办了？”例如，既然总结了报告，能不能顺便更新内部政策？或者既然查到了巴黎，能不能直接把飞往巴黎的机票订好？ 这种“你能做点什么吗”的转折点，正是传统 RAG 演进的核心动力。当前的技术发展正在推动 RAG 从单纯的问答机器，演进为一个全功能的“行动英雄”。用生动的比喻来说，未来的 AI “不再仅仅是问答竞赛的冠军，而是一个无所不能、智商超群且真正能把事情办成的超级私人助理”。 ### [01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) 传统 RAG 的机制与四大核心瓶颈 在展望未来之前，讲者回顾了经典 RAG（检索增强生成）的三个基础步骤： 1. **检索（Retrieve）**：当用户提出问题时，系统从庞大的数据语料库（数据库、互联网或企业内部 Wiki）中精准调取所需文档，如同图书管理员从书架上挑出正确的书籍。 2. **增强（Augment）**：将提取出的信息作为额外的上下文传递给大语言模型（LLM），相当于把选好的章节直接送入 AI 的大脑。 3. **生成（Generate）**：LLM 结合这些补充上下文，生成具有事实依据（Grounded）且准确的回答，从而大幅减少大模型凭空捏造信息的“幻觉（Hallucination）”现象。 然而在实际业务落地中，传统 RAG 就像“屋里最聪明的人制定了一套绝妙计划，却转头要求你亲自去执行”，存在着四个致命局限： | 局限性维度 | 传统 RAG 的表现 | 实际业务面临的问题 | |:--- |:--- |:--- | | **交互模式** | 静态单轮交互（Static one-shot） | 问一次答一次，缺乏多轮往复及“下一步该做什么”的主动推进机制。 | | **行动能力** | 缺乏执行能力（No doing capability） | 能规划最优路线却无法叫车，能提炼报告却无法修改系统，无法主动调用外部工具与 API。 | | **推理深度** | 仅限于信息综合（Synthesis） | 擅长提取与归纳，但在处理多步骤推理和复杂决策树时表现乏力。 | | **数据依赖** | 垃圾进，垃圾出（Garbage in, garbage out） | 一旦检索步骤出错或底层数据库不准确，再强大的模型也会给出偏差答案。 | ### [05:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=307s) AI 智能体的解剖学：大脑、双手、记忆与闭环 真实世界的问题从来不是静态的提问，而是需要动态决策与智能行动。AI 智能体（AI Agent）的核心定义在于：它能够感知环境（Perceive）、对所感知的信息进行推理（Reason）、规划后续步骤（Plan），并最终执行步骤（Execute）以达成特定目标。 ```mermaid flowchart TD accTitle: AI 智能体核心架构图 subgraph Core ["智能体核心架构"] Brain["大脑 (LLM)<br/>思考、推理、规划与决策"] Hands["双手 (Tools & APIs)<br/>连接外部系统、更新数据、发送通知"] Memory["记忆系统 (Context & RAG)<br/>短期会话上下文 + 长期企业知识库"] end subgraph Loop ["自适应循环机制"] Plan["规划 (Plan)"] --> Fetch["检索 (Fetch/RAG)"] Fetch --> Exec["执行 (Execute)"] Exec --> Reflect["反思与调整 (Reflect)"] Reflect --> Plan end Brain <--> Hands Brain <--> Memory Core <--> Loop ``` 智能体由几个高度互联的功能单元构成： - **大脑（The Brain）**：由大语言模型（LLM）驱动，负责所有核心的智能思考、复杂推理与任务规划，理解问题本质并决定下一步行动。 - **双手（The Hands）**：代表各类工具和 API 接口，是智能体与外部世界交互的触角，用于从实时数据库拉取数据、更新 CRM 记录或主动触发邮件通知。 - **记忆（The Memory）**：智能体具备双层记忆体系。一层是由 RAG 驱动的庞大企业知识库（长期记忆），提供可靠的事实依据；另一层是短期上下文（Short-term context），用于追踪多轮对话进程与任务推进状态，确保行为的前后连贯。 - **闭环系统（The Loop）**：贯穿于各组件之间的“规划—检索—执行—反思（Planning, Retrieving, Executing, Reflecting）”持续循环，使智能体具备动态适应能力，成为名副其实的“AI 实干家”。 ### [07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) 动态编排与弹性决策：智能体如何驱动 RAG 智能体不仅仅是简单地“使用” RAG，而是在“编排（Orchestrate）” RAG。智能体将 RAG 作为其敏锐的眼睛和记忆库，主动发起检索决策（Fetch），根据需要在多轮迭代中动态决定“在何时、以何种方式抓取何种信息”。获取数据后，智能体进行深思熟虑（Think），随后调用 API “双手”在外部环境中展开行动（Act）。 ```mermaid flowchart LR accTitle: 智能体多步动态决策与自适应流程 A["接收复杂任务"] --> B["任务分解与规划"] B --> C["调用 API 执行操作"] C --> D{"执行是否成功 / 遇到新情况?"} D -- 成功 --> E["完成任务或推进下一步"] D -- 失败/遇阻 --> F["分析结果并反思调整"] F --> G["尝试替代方案 或 移交人工专家"] ``` 在处理复杂现实业务时，智能体展现出两大关键优势： - **任务拆解与动态决策**：智能体像一位经验丰富的项目经理，不会盲目硬碰硬，而是将宏观难题拆解为可管理的执行步骤。它不依赖僵化的预设脚本，而是根据实时反馈动态调整策略。例如客服智能体接收到请求后，首先检索订单详情并分析问题；如果属于标准简单问题，它直接调用退款 API 完成退款；若问题极其复杂，则提炼关键事实后转交人工专家。 - **自适应反思与容错能力**：当外部 API 报错执行失败，或者客户提出意料之外的追问时，智能体能够对异常结果进行反思，自我修正策略并尝试替代路径，或智能汇总案情交由人类处理。这种自适应闭环赋予了智能体应对现实世界不确定性的强大韧性。 ### [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) 企业落地策略与负责任 AI 准则 将具备“思考、检索与行动”能力的智能体引入企业并非一蹴而就的“大爆炸（Big Bang）”，而是一个需要清晰战略规划的渐进旅程： - **从小切入，策略先行（Start strategically）**：切忌第一天就试图自动化全部业务。应优先识别边界清晰、能快速验证投资回报率（ROI）的高价值封闭场景，通过小规模试点的成功在团队内部建立信心。 - **善用托管平台（Leverage managed platforms）**：借助成熟的云端 AI 基础设施和智能体开发平台。底层平台的算力运维、安全隔离和弹性扩展等繁重工作交给云厂商，让企业团队专注于业务逻辑与智能体核心能力的构建。 - **敏捷迭代与实战检验（Iterate and learn）**：在真实生产环境中对试点进行严苛测试，基于真实反馈持续优化智能体的推理与工具调用链路。 - **坚守负责任 AI（Prioritize Responsible AI）**：随着智能体自主权的提升和对核心敏感数据的深度接入，系统的透明度、安全边界与数据防护显得至关重要。负责任 AI 绝不仅是技术维度的考量，更是赢得用户与利益相关者长期信任的商业与伦理底线。 讲者最后总结道，当前 AI 正在发生根本性的范式转移：“AI 的未来不仅在于‘知晓’，更在于‘践行’（The future of AI is not just about knowing, it's about doing）。”整个行业正在告别只能被动提供答案的系统，迈向能够将信息转化为动态、智能行动的新一代自主智能体时代。 ### [13:39](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=819s) 自主智能体带来的企业变革与未来愿景 演讲在最后阶段升华了自主智能体（Autonomous Agents）的核心价值与深远影响。Jasleen Singh 引导观众设想这样一种未来：“想象一下，当你的 AI 不仅仅是告诉你问题的存在，而是主动去解决它时，那将蕴含着多么巨大的潜力。”这种从“被动告知”到“主动执行”的跨越，正是真正开始赋能现代企业的关键所在。自主智能体绝非简单的技术升级，它们注定将从根本上重塑企业未来的运营方式。 这种转变的核心在于通过对多步骤复杂任务的端到端自动化，释放出前所未有的效率。同时，自主智能体能够为用户和利益相关者提供主动（proactive）、个性化（personalized）且极其迅速的响应，从而大幅提升整体体验。演讲以充满鼓舞力的号召作结：我们正在迈入一个“自主智能的新时代”（a new era of autonomous intelligence）——在这个时代，智能体不再局限于简单的对话与交互，而是具备真正的自主性，成为能够“思考、检索与行动”（think, fetch, and act）的全新力量。 ```mermaid flowchart LR accTitle: Evolution of Autonomous Agents subgraph Passive["传统 RAG / 早期 AI"] A["检测与告知问题"] B["被动单轮对话"] end subgraph Agentic["自主智能体时代"] C["思考 (Think)"] D["检索 (Fetch)"] E["行动 (Act)"] end Passive -->|技术演进| Agentic ``` - **业务重塑**：智能体通过自动化多步骤复杂工作流，推动企业运营实现前所未有的效率跃升。 - **体验升级**：为用户和利益相关者带来具备主动性、个性化和即时反馈的高质量交互。 - **核心定义**：新一代智能体的标志是超越纯语言对话，真正实现“思考、检索与行动（Think, Fetch, and Act）”的完整自主闭环。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) 思考、检索并行动（Think, Fetch, and Act） — Jasleen Singh > [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) 你能不能真正去把事情办了？ — Jasleen Singh > [00:04](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=4s) 不再仅仅是问答竞赛的冠军，而是一个无所不能、智商超群且真正能把事情办成的超级私人助理 — Jasleen Singh > [01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) 屋里最聪明的人制定了一套绝妙计划，却转头要求你亲自去执行 — Jasleen Singh > [01:28](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=88s) 垃圾进，垃圾出（Garbage in, garbage out） — Jasleen Singh > [05:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=307s) 规划—检索—执行—反思（Planning, Retrieving, Executing, Reflecting） — Jasleen Singh > [07:36](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=456s) 智能体不仅仅是简单地“使用” RAG，而是在“编排（Orchestrate）” RAG — Jasleen Singh > [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) 从小切入，策略先行（Start strategically） — Jasleen Singh > [11:07](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=667s) AI 的未来不仅在于‘知晓’，更在于‘践行’（The future of AI is not just about knowing, it's about doing） — Jasleen Singh > [13:39](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=819s) 想象一下，当你的 AI 不仅仅是告诉你问题的存在，而是主动去解决它时，那将蕴含着多么巨大的潜力。 — Jasleen Singh > [13:39](https://www.youtube.com/watch?v=fq9ncIHnGeY&t=819s) 自主智能的新时代（a new era of autonomous intelligence） — Jasleen Singh ## Mentioned Jasleen Singh, Google, Ai4, RAG (检索增强生成), LLM (大语言模型), API, CRM ## Video description Jasleen Singh, Staff Solutions Architect, Generative AI, Google Abstracts The next evolution of RAG isn't just about better answers - It's about autonomous action. This session explores how agentic architectures are transforming RAG from a passive information retrieval tool into a dynamic system that can reason, plan, and act. We'll dive into how agents leverage RAG pipelines to not only retrieve and synthesize knowledge, but also to make decisions, interact with APIs, and adapt workflows based on real-time feedback. Drawing from real-world examples and cutting-edge research, this talk with cover: * The anatomy of an agent-enabled RAG system * Orchestration strategies for multi-step reasoning and task decomposition * Challenges in grounding, latency, and tool use * Emerging patterns for scalable deployment in enterprise and research settings If you're building or scaling AI systems that need to reason over large knowledge bases, adapt to changing environments, or autonomously complete complex tasks - this session is for you. [Source](https://www.youtube.com/watch?v=fq9ncIHnGeY)

Source quotes

[0:04] Jasleen Singh: 思考、检索并行动（Think, Fetch, and Act） [0:04] Jasleen Singh: 你能不能真正去把事情办了？ [0:04] Jasleen Singh: 不再仅仅是问答竞赛的冠军，而是一个无所不能、智商超群且真正能把事情办成的超级私人助理 [1:28] Jasleen Singh: 屋里最聪明的人制定了一套绝妙计划，却转头要求你亲自去执行 [1:28] Jasleen Singh: 垃圾进，垃圾出（Garbage in, garbage out） [5:07] Jasleen Singh: 规划—检索—执行—反思（Planning, Retrieving, Executing, Reflecting） [7:36] Jasleen Singh: 智能体不仅仅是简单地“使用” RAG，而是在“编排（Orchestrate）” RAG [11:07] Jasleen Singh: 从小切入，策略先行（Start strategically） [11:07] Jasleen Singh: AI 的未来不仅在于‘知晓’，更在于‘践行’（The future of AI is not just about knowing, it's about doing） [13:39] Jasleen Singh: 想象一下，当你的 AI 不仅仅是告诉你问题的存在，而是主动去解决它时，那将蕴含着多么巨大的潜力。 [13:39] Jasleen Singh: 自主智能的新时代（a new era of autonomous intelligence）

Source quotes

[0:04] Jasleen Singh: do something about it [0:04] Jasleen Singh: can you do something moment [0:04] Jasleen Singh: trivia champion [0:04] Jasleen Singh: super efficient personal assistant who happens to be a genius and can also get things done. [3:32] Jasleen Singh: the smartest person in the room who then asks you to go execute their brilliant plan [3:32] Jasleen Singh: doing capability [3:32] Jasleen Singh: garbage in, garbage out [13:06] Jasleen Singh: The future of AI is not just about knowing, it's about doing.

Original transcript

[0:04] Today I'm going to be talking about a [0:06] very transformative topic. Agents that [0:09] can think, fetch, and act. The future of [0:12] rag in autonomous systems. So let's get [0:15] started. [0:17] Take a moment to look at this video [0:19] which has been generated by AI. This [0:23] perfectly illustrates a fundamental [0:25] shift in how we think about artificial [0:27] intelligence. [0:29] We've all been there, right? We ask our [0:31] AI system a question. Hey, can you tell [0:34] me the capital of France or can you [0:36] summarize this monster report for me? [0:39] And bam, you get an answer. It's like [0:42] having a great personal assistant at [0:44] your beck and call. But then the next [0:48] logical step comes to your mind. Can you [0:51] actually do something about it? Can you [0:53] summarize that report or can you book [0:56] that flight to Paris? [0:58] That can you do something moment is [1:00] exactly what we are tackling today. We [1:04] are moving our fantastic rag or [1:07] retriever augmented generation from [1:09] being just a question answering machine [1:12] to a fully blown action hero. Think less [1:16] trivia champion and more super efficient [1:19] personal assistant who happens to be a [1:22] genius and can also get things done. So [1:24] let's get started. [1:28] Before we dive into the existing f into [1:30] the exciting future, let's do a quick [1:32] memory refresh on what drag or retrieval [1:36] augmented generation means in its [1:37] traditional terms. [1:40] The first step is retrieve. So you ask [1:43] your AI system a question and it [1:45] extracts just the right documents or [1:48] pieces of information from a watch [1:51] corpus of data. Could be your database, [1:54] the entire internet or your company's [1:57] internal wiki. It's like pulling just [2:00] the right documents on the shelf. [2:04] The next step is augment. It now takes [2:06] that documents, those pieces of [2:08] information and hands it over to the [2:11] large language model as extra context. [2:15] It's like the librarian handing over [2:17] just the right chapters directly to the [2:20] AI's brain. [2:23] And finally, generate. Now, the large [2:25] language model takes these extra pieces [2:28] of information, this newly generated [2:30] context, and uses it to to generate a [2:34] response that is factual and grounded. [2:37] No more making things up because it has [2:40] all the right information right there. [2:43] So, what's the advantage of rag? It [2:45] reduces hallucinations. You know when [2:48] your AI confidently starts making things [2:51] up which is wildly inaccurate. [2:55] Rag provides solid factual grounding. [3:01] So rag is great in generating responses [3:04] which is grounded and factual and [3:07] accurate. It's like having a great [3:09] research assistant who can also quote [3:12] sources. But, and there's always a [3:15] button tech, traditional rag often has [3:18] its limitations, especially when you [3:21] actually want it to do something. It's [3:24] like having the smartest person in the [3:26] room who then asks you to go execute [3:28] their brilliant plan. [3:32] Firstly, it's often static oneshot [3:35] interaction. You ask a question, you get [3:37] an answer. There is no back and forth, [3:40] no what do I do next? [3:44] Secondly, and this one's very critical, [3:47] it has no doing capability. [3:50] It can tell you the best route to a [3:52] specific destination, but cannot book a [3:54] cab for you. It can summarize a report, [3:58] but cannot update the policy for you. It [4:01] does not inherently interact with [4:03] external tools and APIs. [4:07] Thirdly, its reasoning is often limited [4:10] to synthesis. [4:12] It's great at extracting information, [4:14] but it fails at multi-step synthesis or [4:18] complex decision trees. [4:21] And finally, the age-old rule still [4:23] applies. Garbage in, garbage out. If [4:27] your retrieval step is flawed or if your [4:30] database has incomplete or [4:34] inaccurate information, even the [4:37] smartest rag can still give you wonky [4:39] answers. [4:41] So while rag has significantly advanced [4:43] our ability to extract information, it [4:47] leaves a gap, the ability to act [4:50] autonomously. [4:52] And that's exactly what agents bring to [4:54] the table. [4:56] Because real world problems are not [4:59] static questions. They require dynamic [5:02] decision making and intelligent action. [5:07] So if the previous slide talked about [5:09] the why, why do we need AI that acts? [5:12] This slide introduces the how of AI [5:15] agents. [5:16] Think of an AI agent as a system that [5:19] can perceive the environment, reason [5:22] about what it perceives, plan the next [5:24] steps, and then execute those steps to [5:27] achieve a certain goal. It gets us from [5:31] just getting answers to getting things [5:33] done. Let's take a peek inside the core [5:37] anatomy of an AI agent. [5:40] Think of an AI agent as a system, a [5:43] sophisticated system that consists of [5:46] distinct yet highly interconnected [5:49] functional units. [5:53] At the very top, we have the brain which [5:55] is powered by the large language model. [5:58] This is where the intelligent thinking, [6:01] reasoning, planning truly resides. [6:04] It's the central intelligence that [6:08] understands the problem and decides what [6:10] to do next. [6:13] Then we have the hands which signifies [6:15] the tools and APIs. This is how the [6:19] agent interacts with the external world. [6:22] Be it pulling information from a live [6:24] database, updating a CRM policy or [6:27] initiating an email. The agent uses [6:31] these hands to take actions in the real [6:34] world. [6:37] Then we have the memory which is very [6:40] critical for context and grounding. Now [6:43] an agent has two types of memory. It has [6:46] the vast knowledge base which is often [6:49] powered by rag which provides factual [6:52] grounding by pulling in information from [6:55] your database and then it has the [6:58] short-term context which allows the AI [7:01] agent to remember previous conversation [7:04] and task progression. [7:06] This is what makes these agents truly [7:09] coherent. [7:12] Underpining all of this is the loop. [7:15] This continuous cycle of planning, [7:18] retrieving, executing and then [7:21] reflecting is what truly allows these [7:24] agents to take complex decision making [7:27] and intelligent action and helps them to [7:30] adapt. Truly making them the doers of [7:33] AI. [7:36] Now this is where the magic truly [7:38] happens. Agents don't just use rag, they [7:42] orchestrate it, turning static [7:45] information into dynamic intelligent [7:48] action. [7:50] Agents use rag as their critical eyes [7:54] and memory to think smarter and act more [7:58] effectively all grounded in verifiable [8:01] facts. [8:04] It starts with the agent needing [8:06] information. It actively decides fetch [8:10] asking rag to dynamically extract what [8:14] information is needed when and how. [8:18] And this is not a one-step process. It's [8:21] a multi-step iterative process to [8:24] extract just the right context that is [8:27] needed to be able to solve the problem. [8:31] Now with this new information in hand, [8:33] the agent moves on to think. This is [8:36] where the tools and APIs and the agents [8:39] hands comes into play. The agent uses [8:42] these tools and APIs to interact with [8:44] the external world and to actually take [8:47] actions which could be [8:50] sending an email, updating a CRM policy [8:53] or it could be triggering another [8:56] workflow in your system. [9:00] And finally, it moves on to act. [9:06] Now, how do these agents orchestrate [9:09] complex decision making? [9:11] Now, the magic here lies in the fact [9:14] that agents do not tackle a big problem [9:17] head-on. [9:18] They are designed to break it down into [9:21] smaller, more manageable tasks. Think of [9:25] it like a project manager who takes in a [9:28] big initiative and then creates a [9:30] stepbystep approach. [9:34] The most critical part is that agents [9:37] are capable of smart dynamic decision [9:40] making. [9:41] They [9:43] they do not tackle a big problem headon. [9:46] They are not designed to have a [9:49] pre-program script but they take in a [9:52] problem and then create dynamic steps on [9:56] how to solve it. [9:58] For instance, a customer service agent [10:01] might first find the order details and [10:03] then analyze the problem. Based on this [10:06] problem, if the problem is simple, it [10:08] might issue a refund directly by using [10:11] one of the APIs. [10:13] If the problem is complex, then it might [10:16] summarize the issue and hand it over to [10:18] a human expert. [10:20] Now, what's truly powerful here is the [10:22] agent's ability to learn and adapt. If [10:26] one of these actions does not succeed, [10:29] for instance, if the refund API fails to [10:31] execute or if the customer asks an [10:34] unexpected follow-up question, the agent [10:37] can then analyze the outcome. It can [10:40] refine its approach, try a different [10:43] approach, or decide to summarize it and [10:46] hand it over to a human expert. [10:49] This continuous cycle of planning, [10:52] reasoning, taking actions, learning from [10:55] its actions is what truly makes these [10:58] agents resilient and effective at [11:00] tackling the unexpected nature of real [11:03] world problems. [11:07] So, we've seen the power of agents that [11:09] can think, fetch, and act. [11:12] The next logical question that comes to [11:14] your mind is how do I bring this to life [11:18] in my organization? [11:20] Now adopting such advanced AI solutions [11:23] require key strategic planning. [11:26] Think of it not as a big bang but as a [11:29] journey of key considerations. [11:33] Firstly start strategically. Don't try [11:36] to automate everything on day one. [11:39] Identify critical contained use cases [11:43] that can demonstrate return on [11:45] investment early on. [11:48] A small successful pilot initiates [11:51] confidence within the team [11:55] and to achieve that leverage manage [11:57] platforms. Use cloud AI based services [12:00] or agent development platforms. These [12:03] platforms handle the heavy lifting of [12:06] infrastructure security and scalability [12:10] allowing your teams to focus on the [12:12] capability and of agent building. [12:17] Then iterate and learn. Start with a [12:20] small pilot. Test it rigorously in the [12:24] actual real world scenario and based on [12:27] that improve its capabilities. [12:32] And finally, and most critically, [12:35] prioritize responsible AI. With these [12:38] agents now gaining so much autonomy and [12:40] having access to secure data, creating [12:44] something with transparency, security, [12:46] and safety is paramount. [12:50] This is how you gain trust with your [12:52] users and stakeholders and maintain a [12:54] long-term relationship. [12:56] Prioritizing responsible AI is not just [12:59] a technical consideration, but it's a [13:01] business and ethical imperative. [13:06] So, we've seen a lot today from the [13:08] evolution of RAG to the enormous [13:10] potential of AI agents. But if there's [13:13] one core message that I'd like you to [13:15] take away, it's this. The future of AI [13:19] is not just about knowing, it's about [13:21] doing. [13:23] We are moving away from AI agents that [13:26] can just fetch information and give you [13:28] an answer. [13:30] We are creating AI agents that can take [13:33] that information and generate dynamic [13:36] intelligent action. [13:39] Imagine the potential when your AI [13:41] doesn't just tell you that a problem [13:42] exists, but actively works to solve it. [13:47] This capability is what will truly start [13:49] empowering businesses. [13:52] Autonomous agents are not just a [13:54] technological upgrade. They are poised [13:57] to transform how businesses will operate [14:00] in the future. [14:02] They have the capability to drive [14:04] unprecedented levels of efficiency by [14:07] automating [14:09] multi-step [14:11] and complex tasks. They can greatly [14:14] improve user and stakeholder experience [14:16] by providing them with proactive, [14:19] personalized, and rapid responses. [14:22] So as we step into the journey ahead, [14:24] get ready. We are stepping into a new [14:27] era of autonomous intelligence. An era [14:31] where agents are not just capable of [14:35] conversation, but they are capable of [14:37] genuine and true autonomy. Agents that [14:40] can think, fetch, and act. Thank you so [14:43] much.