Build Production-Ready AI Agents with LangChain, LangGraph, RAG, MCP, and FastAPI. 505 pages. 119 hands-on labs. Zero fluff. Built from 14 enterprise training batches at Oracle, JPMorgan & Deloitte.
Every concept backed by working code. Every chapter has labs you can run today.
From zero to production-grade chains, agents, tools, and memory — with real-world enterprise patterns.
Build retrieval-augmented generation systems from scratch. Embeddings, vector stores, hybrid search, and enterprise RAG pipelines.
Stateful agent graphs, human-in-the-loop, multi-agent orchestration, and complex workflow management.
Integrate external tools and data sources via the emerging standard for AI agent interoperability.
Deploy agents as production APIs. Async endpoints, streaming responses, authentication, and scalability.
Monitor, trace, and evaluate your AI agents in production. Cost tracking, latency, and quality metrics.
Four progressive tracks: Foundations → Framework Mastery → Production Engineering → Enterprise & Safety.
📌 Foundations — Chapters 1–3
LLM superpowers & blind spots, the agent loop, 4 building blocks, 3 architectural patterns
AI coding agents, tool registry, context & token budgeting, safety & sandboxing
ReAct, Chain-of-Thought, planning loops, tool-use strategies, and iterative refinement
⚙️ Framework Mastery — Chapters 4–9
Message types, prompt templates, LCEL pipe operator, output parsers, streaming & batch
Embeddings, ChromaDB vector store, document loading & splitting, metadata filtering, citations
Multi-tool agents, conversation memory, MemorySaver, session management strategies
Graph building blocks, conditional routing, state reducers, checkpointing, human-in-the-loop
Complex graph topologies, retry/refinement cycles, parallel branches, production-grade state machines
Supervisor/worker pattern, LLM-powered routing, agent handoffs, peer-to-peer collaboration, task decomposition
🚀 Production Engineering — Chapters 10–12
Three pillars (metrics/logs/traces), OpenTelemetry, structured logging, distributed tracing
REST APIs, streaming responses, secrets management, structured logging, production checklist
Trace hierarchy, LangChain integration, feedback & evaluation, prompt management, cost tracking
🔐 Enterprise & Safety — Chapters 13–16
Three primitives, JSON-RPC wire protocol, multi-server config, security & RBAC, MCP ecosystem
OWASP Top 10 for LLMs, prompt injection defense, output validation, jailbreak defense, red team testing
Full production system: FastAPI + LangGraph + RAG + MCP + Langfuse + safety guardrails end-to-end
Evaluation gap, emerging patterns, CI/CD for AI, career paths, 90-day plan, 5 portfolio projects
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Rated 4.91/5.0 at Oracle · Delivered at JPMorgan, Deloitte, Bank of America