Day 3 – Generative Agents

Day 3 – Generative Agents #

1. What Are Generative Agents? #

We start with the definition of agents—AI systems designed to achieve goals by perceiving their environment and taking actions using tools. Unlike static LLMs, generative agents combine models, tools, and orchestration to interact with the world dynamically.

→ what components make these agents truly autonomous and intelligent?

2. Agent Architecture Breakdown #

An agent’s architecture includes:

  • A language model for decision-making.
  • Tools to interface with the outside world (APIs, functions, data).
  • An orchestration layer to manage memory, state, and reasoning techniques like CoT, ReAct, and ToT.

→ how do we structure agents to be effective in real-world applications?

3. From MLOps to AgentOps #

AgentOps is introduced as a specialized branch of GenAIOps focused on the deployment and reliability of agents. It inherits from DevOps, MLOps, and FMOps, and introduces concepts like prompt orchestration, memory handling, and task decomposition.

→ how do organizations build scalable, production-grade agent systems?

4. The Role of Observability and Metrics #

Agents must be measured at every level—goal success rates, user interactions, latency, errors, and human feedback. These form the KPIs for agents and inform ongoing improvements.

→ how do we move beyond proof-of-concept to reliable agent deployment?

5. Evaluating Agents Effectively #

Agent evaluation involves more than just checking output correctness. It requires tracing decision-making, assessing reasoning, evaluating intermediate steps, and gathering structured human feedback.

→ how do we evaluate agents on logic, tool use, and usefulness holistically?

6. Instrumentation and Traceability #

Traces provide fine-grained visibility into what the agent did and why. This supports debugging and performance tuning, enabling trust and iterative refinement.

→ what observability tools are best for multi-step agent workflows?

7. Assessing Core Capabilities #

Before deployment, it’s vital to evaluate agent capabilities like tool use and planning. Benchmarks such as BFCL and PlanBench test these abilities, but should be supplemented with task-specific tests that reflect real use cases.

→ what public benchmarks best reflect your agent’s core capabilities?

8. Trajectory Evaluation #

Agents often follow multi-step trajectories. Evaluation should compare expected vs. actual tool use paths using metrics like exact match, in-order, any-order, precision, recall, and single-tool usage.

→ is the agent taking optimal steps—or just getting lucky in the final answer?

9. Evaluating Final Responses #

The agent’s final output must be evaluated for correctness, relevance, and tone. LLM-based autoraters are useful, but need precisely defined criteria. Human evaluators still offer the gold standard for nuanced feedback.

→ can automated evaluation alone guarantee real-world readiness?

10. Human-in-the-Loop (HITL) #

Subjectivity, nuance, and real-world implications often require human review. Direct scoring, comparative evaluations, and user studies are powerful tools to validate and calibrate automated metrics.

→ when should humans intervene in the agent evaluation loop?

11. Challenges and Future Directions #

Agent evaluation is still maturing. Current challenges include limited evaluation datasets, gaps in process reasoning metrics, difficulty with multimodal outputs, and handling dynamic environments.

The forward-looking insight: agent evaluation is shifting toward process-based, explainable, and real-world-grounded methods.

12. From Single to Multi-Agent Evaluation #

Multi-agent systems are the next evolution in generative AI—multiple specialized agents collaborate like a team. Evaluation must now address not just individual outputs, but also cooperation, delegation, and plan adherence.

→ how do we measure coordination, not just correctness?

13. Architecture of Multi-Agent Systems #

Agents are modular and play distinct roles—planner, retriever, executor, evaluator. Communication, routing, tool integration, memory, and feedback loops form the backbone. These components support dynamic and resilient reasoning.

→ what enables agents to act as a system, not just individuals?

14. Multi-Agent Design Patterns #

Patterns like sequential, hierarchical, collaborative, and competitive enable scalable, adaptive, and parallel agent behavior. These patterns reduce bottlenecks and improve automation for complex workflows.

→ which pattern suits your domain—assembly line, team, tournament, or council?

15. Evaluation at Scale #

Evaluating multi-agent systems includes trajectory traceability, agent coordination, agent-tool selection, and system-wide goal success. Instrumenting each step and agent ensures deeper insights.

The closing reflection: multi-agent systems multiply both the potential and complexity of generative agents—evaluation must evolve accordingly.

16. From RAG to Agentic RAG #

Traditional RAG pipelines retrieve static chunks of knowledge for LLMs. Agentic RAG innovates by embedding retrieval agents that:

  • Expand queries contextually
  • Plan multi-step retrieval
  • Choose data sources adaptively
  • Validate results via evaluator agents

→ how can agents actively reason during retrieval to boost response quality?

17. Engineering Better RAG #

To improve any RAG implementation:

  • Parse and chunk documents semantically
  • Enrich chunks with metadata
  • Tune embeddings or adapt search space
  • Use fast vector search + rankers
  • Implement grounding checks

→ is your RAG problem about generation—or poor search to begin with?

18. The Rise of Enterprise Agents #

2025 marks the rise of two agent types:

  • Assistants: Interactive, task-oriented agents like schedulers or sales aides.
  • Automators: Background agents that observe events and autonomously act.

→ how do organizations orchestrate fleets of agents across roles and workflows?

19. Agentspace and Agent Management #

Google Agentspace provides enterprise-grade infrastructure for creating, deploying, and managing secure, multimodal AI agents. With features like:

  • RBAC, SSO, and data governance
  • Blended RAG and semantic search
  • Scalable agent orchestration and monitoring

→ what’s needed to manage AI agents as a virtual team at scale?

20. NotebookLM Enterprise #

NotebookLM allows users to upload documents, ask questions, and synthesize insights. Enterprise features include:

  • Audio summaries via TTS
  • Semantic linking across documents
  • Role-based access and policy integration

Final insight: intelligent notebooks + agents will redefine enterprise knowledge discovery and interaction.

21. Contract-Adhering Agents #

Prototypical agent interfaces are too vague for real-world, high-stakes environments. Introducing contractor agents enables:

  • Clear outcome definitions
  • Negotiation and refinement
  • Self-validation of deliverables
  • Structured decomposition into subcontracts

→ how can formalized contracts make agents production-ready and trustworthy?

22. Contract Lifecycle and Execution #

Contractor agents follow a lifecycle: define → negotiate → execute → validate. Execution may involve multiple LLM-generated solutions and iterative self-correction until the contract is fulfilled, optimizing for quality over latency.

→ what runtime capabilities are needed for contract-based agents?

23. Co-Scientist: A Real-World Case Study #

Google’s AI co-scientist system uses multi-agent collaboration to accelerate hypothesis generation and validation in scientific research. Roles include:

  • Data processors
  • Hypothesis generators
  • Validators
  • Cross-team communicators

Final reflection: multi-agent systems, when built as collaborative contractors, can extend the scientific method itself.

24. Specialized Agents in the Car #

The automotive domain is a natural fit for multi-agent AI. Here are key agent roles:

  • Navigation Agent: Plans routes, ranks POIs, and handles traffic awareness
  • Media Agent: Plays contextually relevant music or podcasts
  • Messaging Agent: Drafts, edits, and sends messages hands-free
  • Car Manual Agent: Uses RAG to answer questions about car features
  • General Knowledge Agent: Answers follow-up queries to enhance user experience

→ how do you design agent roles that align with contextual user needs?

25. Hierarchical and Diamond Patterns #

  • Hierarchical: A central Orchestrator routes user input to the right agent
  • Diamond: Adds a Rephraser agent for tone/style before speaking responses aloud

→ when does orchestration alone fall short—requiring tone-sensitive agents?

26. Peer-to-Peer and Collaborative Patterns #

  • Peer-to-Peer: Agents hand off queries among themselves for better routing resilience
  • Collaborative: Multiple agents contribute partial answers; a Mixer Agent synthesizes the final response

→ can agents collaborate without central control to produce superior outputs?

27. Response Mixer and Safety-Critical Use #

The Response Mixer evaluates and combines outputs from several agents (e.g., knowledge + tips + manual) to form a cohesive answer, especially for safety-critical queries like aquaplaning.

→ how do we ensure safety-critical information is prioritized in generative settings?

28. Adaptive Loop Pattern #

Agents refine queries iteratively to meet vague or underspecified user needs—e.g., finding a vegan Italian restaurant with fallback strategies.

Closing insight: multi-agent architectures thrive where adaptability, refinement, and specialization are essential.

29. Real-Time Performance and Resilience #

Multi-agent systems in cars prioritize on-device responsiveness for safety (e.g., climate control), while using cloud-based agents for tasks like dining suggestions. This hybrid model balances latency, capability, and robustness.

→ how do agents coordinate local vs. remote processing for safety and personalization?

30. Vertex AI Agent Builder #

Google’s Agent Builder platform integrates secure cloud services, open-source libraries, evals, and managed runtimes for enterprise-grade agent development. Features include:

  • Retrieval via Vertex AI Search or RAG Engine
  • Secure APIs via Apigee
  • Gemini and Model Garden access
  • Evaluation pipelines via Vertex AI Eval Service

→ what developer tools are needed to build, scale, and evaluate enterprise-ready agents?

31. Key Developer Principles #

  • AgentOps matters: memory, tools, trace, orchestration
  • Automate evals, but combine with HITL
  • Design multi-agent architectures for complexity and scale
  • Improve search before Agentic RAG
  • Use agent/tool registries to reduce chaos
  • Prioritize security, flexibility, and developer cycles

32. Future Directions #

Research will focus on:

  • Process-based and AI-assisted evaluation
  • Agent collaboration and communication protocols
  • Memory, adaptivity, explainability, and contracting models

Final insight: the future is agentic—developers must blend engineering, ops, UX, and domain logic to build next-gen intelligent systems.