Agentic Engineering Handbook

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

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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 175 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

Then Read

Build Exercise

Build a mini coding harness: plan file, shell tool, apply patch, test gate, event log, and resume capability.

Phase 5 — Coding & Workspace Agents

Compare Codex vs Claude Code product/SDK forms; learn multi-agent, IDE, workspace collaboration.

Read First

Then Read

Build Exercise

Run both OpenAI/Codex and Claude Code style workflows on the same repo: issue → plan → patch → tests → PR summary.

Phase 6 — Evals, Safety & Production

Build pre/post-launch eval loop, trace loop, safety boundaries, permissions, regression monitoring.

Read First

Then Read

Build Exercise

Build a smoke/macro eval suite for your agent: task success rate, tool misuse, prompt injection resistance, latency, cost, and human approval count.

Applied Practice Tracks

Use these tracks after the core roadmap when you want to practice agentic engineering in real engineering workflows.

Full Reading Table

Priority guide: P0 = must-read (architectural/conceptual), P1 = highly useful (implementation detail), P2 = optional context (background/releases).