Agentic Engineering Handbook

The definitive OpenAI, Anthropic, Google, MCP, Harness, Evals, and Production Agent Systems learning roadmap.

If this repository helps you, consider giving it a ⭐

Why This Repository?

The AI industry has entered the Agentic Era. Building production-grade AI systems now requires mastering agents, tool use, MCP, memory, long-running workflows, coding agents, agent harnesses, evals, and safety — but the knowledge is scattered across OpenAI blogs, Anthropic engineering posts, SDK docs, cookbooks, and research papers.

This repository consolidates 161 curated resources into one structured learning roadmap.

The goal: Become a world-class Agentic Engineer.

How To Use This Handbook

Pick the path that matches your starting point:

• New to agents: follow the Learning Roadmap from Phase 0 to Phase 6. Treat each Read First, Then Read, and Build Exercise as a checklist.
• Already building LLM apps: start at Phase 2 or Phase 3, then fill gaps in agent loop, tool calling, evals, and production engineering.
• Trying to build projects: use the phase-level Build Exercise prompts, then branch into Applied Practice Tracks for coding agents, security, code review, or SRE.
• Looking for references: jump to the Full Reading Table. Read P0 first, use P1 for implementation detail, and keep P2 as optional background.

Learning Roadmap

Phase 0 — Agent Loop From Scratch

If you treat Claude Code as a coding CLI, many capabilities can feel like magic: it reads files, runs commands, edits code, delegates work, and stays oriented during complex tasks.

From an engineering perspective, the core is much simpler:

model + tools + one loop.

Understanding that loop makes the rest of the system easier to reason about:

• When the agent should plan first, and when it should act immediately
• Why an explicit todo list reduces drift in longer tasks
• Why subagents improve exploration while protecting the main context
• How skills, MCP, and hooks each add capability around the same core loop

These pages are based on the upstream English Markdown tutorials from shareAI-lab/mini-claude-code, with added Study Notes and inline source code for this handbook.

Supporting files are included in the same folder: requirements.txt, .env.example, v0_bash_agent_mini.py, and skills/.

Phase 1 — Agent Foundations

Build shared vocabulary for workflow vs agent, tool loop, handoff, guardrails.

Key Mental Models

Should I build an agent? (4-question checklist from Barry Zhang's talk)

Think like the agent. Most failures come from designing with a human perspective. Put yourself inside the agent's context window: you only see ~10K–20K tokens (system prompt + tool descriptions + recent observations). Ask: does the agent have enough information to act correctly at each step?

→ Source: How We Build Effective Agents

Read First

Then Read

Build Exercise

Build a customer service/ticket triage agent: router → specialist → evaluator, with all outputs constrained by structured schemas.

Phase 2 — MCP & Tool Ecosystem

Understand MCP server/client, remote vs local, tool loading, approval, connector boundaries.

Read First

Then Read

Build Exercise

Build a read-only repo/docs MCP server, then create an eval to verify the agent correctly cites documentation.

Phase 3 — Context, Memory & Skills

Learn to control context window, short/long-term memory, skills/plugins, CLAUDE.md/AGENTS.md.

Read First

Then Read

Build Exercise

Implement the same task as a Skill/Plugin, then measure accuracy and token cost across three variants: no skill, long prompt, and skill-based.

Phase 4 — Harness & Long-Running Agents

Master agent runtime: event stream, thread, tool execution, state, sandbox, approval, recovery.

Read First