ai-agent-book/slides/lesson-32.md

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theme	title	info	author	transition	mdc	lineNumbers	monaco	aspectRatio	canvasWidth	layout	class
seriph	Lesson 32 — How Do Failed Trajectories Become Learning Signals?	English video course for AI Agents in Depth	Bojie Li	slide-left	true	false	false	16/9	980	cover	cover

Improve · Chapter 8 · Continual Evolution

How Do Failed Trajectories Become Learning Signals?

Outcome verification, process rules, Rubrics, and cross-trajectory experience

Lesson 32 of 42 · 19 minutes · Deriving Learning Signals from Operational Trajectories; Consolidating Experience into Knowledge

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Improve · Chapter 8 · Continual Evolution

Problems this chapter will solve

Lesson 32

How Do Failed Trajectories Become Learning Signals?

Lesson 33

Where Should an Agent Store What It Learns?

Lesson 34

How Can a Self-Modifying Agent Change Without Drifting?

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Why this problem matters

Outcome

Read what changed in the environment.

Process

Locate rule violations and ineffective decisions.

Meaning

Use a Rubric for dimensions that code cannot settle.

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Three ideas to keep in view

Trajectory verifier

Outcome checks + process rules + language Rubric

Contrastive evidence

Compare success, partial success, and failure

Experience document

Mechanism + conditions + evidence + exceptions

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The book's visual model

Three-layer trajectory verification from outcomes to an LLM Rubric

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Save the trajectory vs. Consolidate experience

Save the trajectory

• High detail
• Hard to retrieve
• Incidental actions become noise

Consolidate experience

• Cross-run pattern
• Explicit applicability
• Evidence and counterexamples

A trajectory is evidence; it is not yet a lesson.

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Diagnose before updating

outcome = environment_verifier(trajectory)
violations = process_verifier(trajectory)
rubric = semantic_judge(trajectory, outcome)
diagnosis = triangulate(outcome, violations, rubric)
experience = consolidate(similar_diagnoses)

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Test the claim

8-12 min

Diagnose customer-service trajectories with three evidence layers

Observe: False promises, privacy violations, over-refusal, and cited evidence

8-22 min

Consolidate several trajectories into experience documents

Observe: Transfer gain, retrieval cost, negative transfer, and applicability conditions

Demo budget: 4 minutes · one contiguous terminal block

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class: course-terminal

Live demo

Switching to the terminal

$ cd chapter8/trajectory-verifier && python demo.py

$ cd chapter8/gaia-experience && python demo_documents.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

Environment outcomes constrain what a language judge may claim.

Finding 2

Failures and partial successes reveal conditions hidden by successful runs.

Finding 3

Cross-trajectory documents can transfer while using fewer tokens than raw history.

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Where the claim stops

Boundary condition

A pattern supported by past trajectories may become obsolete after an API, policy, or environment change.

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Engineering takeaway

Design rule

Promote experience only with provenance, applicability conditions, counterevidence, and a revalidation trigger.

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Continue the experiment

Trajectory verifier tests chapter8/trajectory-verifier/test_verifier.py Experience-document implementation chapter8/gaia-experience/ Knowledge-document architecture book-en/images/fig8-4.svg

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Pause and apply

Your turn

Which detail in a successful trajectory was causal, and how would you distinguish it from coincidence?

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Next · Lesson 33

Choose the artifact that should change: knowledge, instructions, programs, or parameters.

→