Roadmap¶

Published¶

The book is available on Amazon. Seven chapters covering the full lifecycle of building production agent systems.

This repository is the code companion -- working implementations, tests, diagrams, and evaluation evidence.

Foundations (pre-read)¶

Four hands-on sections published as a free pre-read for readers new to LLMs and agentic AI:

• How LLMs Actually Work -- APIs, tokens, context windows, hallucination
• From API Calls to Tool Use -- Function calling, schema validation, tool execution
• Your First Agent, No Framework -- Complete agent in 100 lines, end-to-end
• The Same Agent, With a Framework -- ADK and LangChain side-by-side with eval comparison

Shipped in this repo¶

• Working code for every concept: tool registry, context pipeline, agent loop, workflow implementation, bounded agent, state management, multi-agent orchestration, approval gates, escalation engine, audit logging, eval harness, tracer, reliability hardening, cost profiler, security hardening
• 2 end-to-end projects: Document Intelligence Agent and Incident Runbook Agent
• Eval harness with gold dataset, rubric, scored comparison script, and failure buckets
• 52+ passing tests across unit and integration suites
• 22 architecture-grade diagrams (hand-crafted SVGs)
• Infrastructure: pyproject.toml, Makefile, .env.example

Part IV: Advanced Patterns (bonus chapters)¶

New chapters extending the book's operating patterns into advanced territory:

• Chapter 12: Memory Management -- Session memory, long-term learning, multi-agent coordination, and memory security
• More chapters planned

What might come next¶

• Additional code examples for advanced topics
• More end-to-end projects
• Community contributions: real-world case studies, additional eval datasets

Content ships when it meets the quality bar. No timelines promised.