Thomas Piacentini
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14 · Concevoir des agents

Concevoir des agents

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The core of modern AI engineering. Build agents from first principles.

Start this phase on GitHub

Prerequisites: Phase 11 LLM Engineering and Phase 13 Tools and Protocols.

First lesson: The Agent Loop

Two focused routes are available when you do not need the full phase:

  • Agent-Assisted Engineering covers task framing, repository evidence, harnesses, isolation, verification, review, and durable feedback.
  • Product Judgment and Delivery covers outcomes, workflow discovery, assumptions, slices, specifications, metrics, staged release, and feedback ownership.

Run this command from the repository root:

bash
python3 phases/14-agent-engineering/01-the-agent-loop/code/main.py

Keep the command, exit code, thought-action-observation trace, final answer, turn count, and the exact stop condition that ended the run.

Next action: Change the scripted task or turn budget, predict the effect, then continue to ReWOO and Plan-and-Execute.

Browse the full Phase 14 lesson list or the cross-phase roadmap.

Continuer étape par étape.

Code et livrables 1
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Sources et limites

Les repères de lecture sont déclaratifs. Les résultats de tests sont enregistrés séparément ; ils valent pour les cas exécutés.

AI Engineering from Scratch · 3f9715c · MIT

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