Who it's for: builders who want to go past using an agent to assembling one.
The idea in one line: an agent is a model plus a harness. The model reasons; the harness is everything you build around it so it can act, prompting, context, tools, memory, retrieval, the loop, and verification. This path takes you from using pre-built harnesses to assembling and evaluating your own.
Start with the Harness Engineering 101 guide for the full theory (what a harness is, the maturity ladder, the agency-control tradeoff, context rot, verification loops, the lethal trifecta), then use this path to go deeper.
Level 🟡→🔴 · Source ⭐ LevelUp Labs original / 🌐 External
- Agentic AI Crash Course, part 1 and part 2 ⭐ 📖: what an agent is, and the 4 types of agentic systems.
- Building Effective Agents 🌐 📖 by Anthropic: the canonical read on when to use a simple workflow versus a full agent, and the common patterns.
Learn the leading harnesses before you build one.
- Claude Code in Action 🌐 🎥 by Anthropic Academy: agentic coding, explore-plan-code-commit.
- Learn Cursor 🌐 🎥 by Cursor.
- OpenAI Codex Essentials 🌐 🎥 by freeCodeCamp.
- Claude Code Best Practices 🌐 📖 and the Claude Code docs 🌐 📖 by Anthropic.
The pieces of a harness, one at a time. All from the Agentic AI Crash Course.
- Tools and function calling → part 3 ⭐
- Retrieval as a tool → part 4 ⭐ and Agentic RAG 101 ⭐
- Planning and reasoning models → part 6 ⭐
- Memory → part 7 ⭐
- MCP → part 5 ⭐ and Anthropic's Introduction to MCP 🌐 🎥
- Multi-agent and sub-agents → part 8 ⭐
A harness is only as good as your ability to tell it is working, and to catch it when it breaks.
- AI Evals for Everyone ⭐ 📖 (certified): model vs product evals, reference datasets, monitoring.
- Securing Agentic AI Systems ⭐ 📖: what breaks once the agent can act.
- Agentic AI Crash Course, part 9 and part 10 ⭐: real-world systems and where this is heading.
You can now: use an agent harness fluently, assemble your own from tools, memory, context, and a loop, and evaluate it. Keep going with the AI Agents topic, the Agent Builder path, and the living Agentic Search and Retrieval research table.