ai-agent-book/slides/lesson-13.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 13 — When Should the Agent Decide What to Retrieve?	English video course for AI Agents in Depth	Bojie Li	slide-left	true	false	false	16/9	980	cover	cover

Build · Chapter 3 · Memory and Knowledge

When Should the Agent Decide What to Retrieve?

Agentic RAG, contextual retrieval, and two-tier memory

Lesson 13 of 42 · 19 minutes · Agentic RAG; Contextual Retrieval; Deep Knowledge Extraction

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Why this problem matters

Search decision

The Agent decides whether retrieval is needed.

Query reformulation

New evidence changes the next search.

Stopping

The Agent judges whether evidence is sufficient.

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Three ideas to keep in view

Agentic RAG

Retrieval becomes a tool inside ReAct

Contextual retrieval

Restore document context before indexing each chunk

Two-tier memory

Resident overview + retrieved detail

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The book's visual model

Agentic RAG architecture

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Retrieve once vs. Agentic retrieval

Retrieve once

• Fixed query
• Fixed top-k
• One chance to find evidence

Agentic retrieval

• Iterative queries
• Evidence-aware decisions
• Explicit stopping

Autonomy adds flexibility and a new metacognition failure mode.

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Retrieval becomes an action

while not evidence_sufficient(context):
    query = agent.formulate_search(context)
    passages = search(query)
    context.add(passages)
return agent.answer_with_citations(context)

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Test the claim

3-82 min

Compare fixed and Agentic RAG offline

Observe: Query count, evidence coverage, answer quality, and cost

3-102 min

Compare plain and contextual chunks

Observe: Failures repaired by adding document-level context

3-112 min

Compare two-tier user memory

Observe: Resident overview plus on-demand conversation detail

Demo budget: 6 minutes · one contiguous terminal block

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class: course-terminal

Live demo

Switching to the terminal

$ uv run python chapter3/agentic-rag/compare_offline.py

$ uv run python chapter3/contextual-retrieval/compare_retrieval.py --per-query

$ uv run python chapter3/contextual-retrieval-for-user-memory/contextual_compare.py

Run the command(s), narrate decisions, and point to the observation—not just the output.

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What the evidence supports

Finding 1

Iterative retrieval helps when later queries depend on earlier evidence.

Finding 2

Contextual prefixes repair semantic loss introduced by chunking.

Finding 3

Overview and detail require different storage and access strategies.

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Boundary → design rule

An Agent cannot retrieve what it does not realize it is missing.

Use Agentic retrieval for genuinely multi-step evidence gathering; keep simple questions on a simple path.

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Continue the experiment

Experiment 3-9: Agentic RAG for memory chapter3/agentic-rag-for-user-memory/ Experiment 3-12: structured knowledge extraction chapter3/structured-knowledge-extraction/ Contextual retrieval diagram book-en/images/fig3-14.svg Knowledge extraction pipeline book-en/images/fig3-15.svg

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layout: center class: text-center

Pause and apply

Your turn

What independent signal can tell an Agent that its evidence is insufficient?

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layout: center class: text-center

Chapter 3 complete · Next · Lesson 14

Turn knowledge into actions through carefully designed tools.

→