Ingénierie des agents IA avec Claude et MCP

Ce qu'elle couvre

Ce bootcamp intensif apprend aux ingénieurs logiciels à concevoir, construire et évaluer des agents IA autonomes en utilisant l'API Claude d'Anthropic, le SDK Agent et le Model Context Protocol (MCP). Les participants implémentent des boucles de planification, l'utilisation d'outils multi-étapes, des architectures mémoire et des harnais d'évaluation de sécurité dans des scénarios réels. Le format alterne courtes séquences théoriques et longues sessions de laboratoire où les équipes livrent des prototypes d'agents fonctionnels. À l'issue de la formation, les ingénieurs peuvent architecturer, instrumenter et robustifier des agents autonomes pour un déploiement en production.

À l'issue, vous saurez

• Implement a multi-step ReAct agent loop with Claude that plans, calls tools, observes results, and self-corrects
• Register and consume MCP-compatible tool servers within an agent orchestration graph
• Design a hybrid memory system combining in-context state, a vector retrieval layer, and a structured episodic store
• Apply safety gates that interrupt agent execution when confidence drops below a threshold or policy constraints are violated
• Write an automated evaluation harness that scores agent trajectories against ground-truth task completions

Sujets abordés

• Claude API fundamentals: tool use, function calling, and structured outputs
• Agent SDK architecture: agent loops, state machines, and execution graphs
• Model Context Protocol (MCP): server setup, context injection, and tool registration
• Planning patterns: ReAct, Reflexion, and multi-agent orchestration
• Memory architectures: in-context, external vector stores, and episodic memory
• Safety gates: guardrails, constitutional AI checks, and human-in-the-loop triggers
• Evaluation frameworks: trajectory scoring, tool-call accuracy, and regression harnesses
• Observability and debugging: tracing agent runs, cost control, and latency profiling

Modalité

Delivered over 3–5 days, either on-site or live-remote via video conference. Each day follows a 30/70 theory-to-lab ratio. Participants need a laptop with Python 3.11+, an Anthropic API key, and access to a vector store (Pinecone or Qdrant trial accounts are sufficient). A shared GitHub repo provides starter code, evaluation scaffolding, and reference implementations. Remote cohorts use VS Code Live Share or GitHub Codespaces for pair-lab exercises. A private Slack channel remains open 30 days post-bootcamp for async Q&A.

Ce qui fait que ça marche

• Start every agent project with an evaluation harness before writing the first prompt — it forces task decomposition discipline
• Define a clear contract between the orchestrator and each tool (input schema, error codes, timeout) before integration
• Instrument every agent run with full trajectory traces from day one to enable fast debugging and cost optimisation
• Schedule a weekly red-team session where engineers deliberately try to break the agent's safety gates

Erreurs fréquentes

• Skipping trajectory evaluation: teams ship agents without automated scoring, leaving quality regressions undetected in production
• Infinite loops with no circuit-breaker: agents with unbounded planning loops exhaust token budgets or enter retry spirals
• Treating tool schemas as an afterthought: poorly typed tool descriptions cause Claude to misfire calls far more often than prompt quality issues
• Ignoring memory eviction strategy: storing everything in-context causes latency spikes and context-window overflow on longer tasks

Fournisseurs à considérer

• Anthropic Academywww.anthropic.com/academy →
• DeepLearning.AIwww.deeplearning.ai →
• DataScientestdatascientest.com →
• Jedha Bootcampwww.jedha.co →

Sources

• Anthropic Model Context Protocol documentation →
• Anthropic Claude Tool Use documentation →