AI Agent companion report

Google has done it again!

This is the most detailed report I have ever seen so far...

Recently,

Google Dropped yet another very detailed whitepaper on AI Agents.

This time, this paper is filled with details on building an Agentic RAG, Multi-agent systems, and so much more.

📌 Here's a summary of what's included in the report:

1. Basis of Agent Architecture: This report builds upon their earlier while paper introducing what Agents are composed of and how can you utilize these concepts to build better Agents.

2. Agent Ops: Agent Operations (AgentOps) focuses on operationalizing agents efficiently, including tool management, prompt engineering, and orchestration, building on DevOps and MLOps principles.

3. Agent Evaluation: Evaluating agents involves assessing their capabilities, trajectory and tool use, and final response quality, using automated metrics and human-in-the-loop feedback for continuous improvement.

4. Multi-agent systems: These systems involve multiple specialized agents collaborating to achieve complex objectives, offering enhanced accuracy, efficiency, and scalability over single-agent systems.

5. Agentic RAG: It involves principles of using autonomous agents to refine search queries, retrieve relevant information, and validate responses, improving accuracy and adaptability in knowledge retrieval tasks.

6. Enterprise Applications: Google shares very detailed and well-written examples of AI-driven tools designed to enhance productivity and streamline workflows in enterprise settings, providing secure and scalable solutions.

7. Contractors Framework: Evolving agents into "contractors" with defined contracts for tasks, outcomes, and feedback loops ensures clarity, negotiation, and iterative improvement in task completion.

8. Automotive AI Case Study: A multi-agent system for automotive AI demonstrates specialized agents (e.g., navigation, media search) collaborating through patterns like hierarchical, diamond, peer-to-peer, and collaborative to provide a seamless in-car experience.

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