This course is a structured path through the Agent Factory wiki and examples: from definitions and loops to frameworks, production concerns, and advanced interoperability and self-improvement. It is designed for builders who learn best by reading, exercising, and shipping small vertical slices.
You should be comfortable with basic LLM usage (chat APIs, tokens, temperature) and general programming (read code, run scripts, use git). Prior exposure to HTTP APIs and JSON helps for tool and protocol modules. You do not need to be a researcher; the emphasis is practical systems.
Work modules in order within each block unless a module’s prerequisites note otherwise. Blocks 1–3 establish vocabulary and architecture; Blocks 4–5 map those ideas to real frameworks and operations; Block 6 extends to protocols, harness engineering, and a capstone.
Estimated time: about 11–13 hours of focused study for all 23 modules (excluding capstone implementation time, which varies by scope).
What agents are, architectures, the agent loop, and system prompts. Establishes shared language for the rest of the course.
Tools, memory and context, planning and reasoning, error handling and recovery. These modules mirror what most production agents spend engineering time on.
Design patterns, multi-agent patterns, explicit anti-patterns, and state management. Use this block to critique designs before you commit to code.
Framework selection, LangGraph, OpenAI Agents SDK, and Anthropic-oriented patterns. Compare trade-offs rather than treating any stack as universal.
Evaluation and testing, safety and guardrails, observability and debugging, deployment and scaling. Treat this block as the minimum bar before user-facing or high-impact automation.
Protocols and interoperability (MCP, A2A), self-improvement and harness engineering, and the production capstone. Completing Block 6 means you can situate agents in an ecosystem, not only a single repository.
| Module | Title | Duration |
|---|---|---|
| 01 | What Are Agents | 30 min |
| 02 | Agent Architectures | 40 min |
| 03 | The Agent Loop | 40 min |
| 04 | System Prompts for Agents | 30 min |
| 05 | Tool Design and Integration | 45 min |
| 06 | Memory and Context Engineering | 45 min |
| 07 | Planning and Reasoning | 40 min |
| 08 | Error Handling and Recovery | 30 min |
| 09 | Agent Design Patterns | 45 min |
| 10 | Multi-Agent Patterns | 45 min |
| 11 | Anti-Patterns | 35 min |
| 12 | State Management | 35 min |
| 13 | Framework Selection | 30 min |
| 14 | Building with LangGraph | 45 min |
| 15 | Building with OpenAI Agents SDK | 45 min |
| 16 | Building with Anthropic | 45 min |
| 17 | Agent Evaluation and Testing | 40 min |
| 18 | Safety and Guardrails | 40 min |
| 19 | Observability and Debugging | 35 min |
| 20 | Deployment and Scaling | 35 min |
| 21 | Protocols and Interoperability | 40 min |
| 22 | Self-Improvement and Harness Engineering | 45 min |
| 23 | Capstone: Build a Production Agent | 60 min |
Each module points to concept and research articles under ../wiki/. Use the wiki when you need depth, citations, or alternative phrasing of the same idea. The ../wiki/examples/ tree contains good and bad exemplars; read bad examples to sharpen design reviews.
Start from ../wiki/INDEX.md if you prefer browsing by topic instead of linear modules.
Treat AGENT_SPEC.md as the repository’s cross-cutting quality bar: clarity of purpose, tooling discipline, safety, observability, and operational readiness. Use it when reviewing your own capstone or a teammate’s agent PR.
Twelve modules (03, 05, 06, 08, 11, 12, 17, 18, 19, 20, 22, 23) include "Empirical note" sections drawn from a Karpathy-style autoresearch loop across 20 agents (~100 iterations). These sections connect each module’s topic to ranked, quantified findings about what actually raises agent quality scores. For the full ranked breakdown, see the Factory Showcase LEARNINGS.md or the distillate in the agent-factory README.
Agents and skills (procedures loaded by hosts) share engineering concerns: scoping, discovery, verification, and safety. The companion Skill Factory project lives at ../../skill-factory/ relative to this folder (sibling of agent-factory under the same parent workspace). Use it when you want parallel depth on authoring reusable SKILL.md assets and validation loops.
Agent Factory course README — aligns with modules 01–23 and the project wiki.