Part 4 ended with a debt: whether the loop is actually doing the job. When a skip was correct, when a tool lied, and when the audit trail is the only honest record.

This is Part 5 of 6 in my Agentic Coding with Claude Code series. The working model I am keeping is simple: evaluation is how I score a run. Observability is how I see the run. Structured output is the seam between them.

A harness that cannot tell success from a polite hallucination is a demo. Later I will talk about bounded autonomy. This one stays on scoring the loop and seeing it.

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This post continues our coverage of Google’s agent whitepaper series, following Introduction to Agents, Agent Tools & MCP, and Context Engineering. This fourth installment tackles perhaps the most critical challenge: how do we know if an agent is actually good?

Source: Agent Quality (PDF) by Google, November 2025

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Traditional RAG (Retrieval-Augmented Generation) follows a fixed pattern: query in, documents out, response generated. But what if the agent could decide when and how to retrieve? Agentic RAG gives agents control over their own knowledge acquisition. In this post, I’ll explore this dynamic approach to retrieval, then tackle the equally important question: how do we know if our agents actually work?

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