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AI Agents & Builders

Design, build, evaluate and ship agents that act on your behalf without wrecking anything.

You can already call a model. This course is about systems that plan, use tools and run for many steps: how agents work, how to specify and guard them, how coding agents and multi-agent frameworks fit in, how to evaluate them properly, and how to get them into production with the security and observability they need.

Recommended before this course: AI Power User

What you will be able to do

  • Describe the agent loop and choose the right level of autonomy for a task
  • Write a one-page agent specification with tools, memory, budgets and stop conditions
  • Work productively with coding agents such as Claude Code, Codex and Cursor, including repository conventions
  • Choose between single-agent, orchestrator-worker and framework-based designs, and justify the choice
  • Build an evaluation set and regression suite so changes are measured, not guessed
  • Harden and ship an agent: injection defences, least privilege, tracing, cost per task and human-in-the-loop design

Course outline

1

What an agent is

3 lessons · quiz

The loop that turns a model into an agent, the autonomy dial, and the four parts every agent is made of.

  • The agent loop: plan, act, observe 10 min
  • Levels of autonomy and where humans stay in the loop 9 min
  • Anatomy of an agent: model, tools, memory, guardrails 9 min
  • Module quiz 5 questions
2

Designing agent tasks

5 lessons · quiz

Specify before you build: a definition of done the agent can check, tools it can actually use, memory that does not rot, and limits that stop it.

  • Task decomposition and success criteria 10 min
  • Writing tool descriptions the model can use 9 min
  • Memory: short-term, long-term, and files as memory 10 min
  • Guardrails, budgets and stop conditions 10 min
  • Exercise: specify an agent on one page exercise · 30 min
  • Module quiz 6 questions
3

Coding agents in practice

4 lessons · quiz

The tools professionals use daily, a working rhythm that keeps you in control, and the repository files that make agents behave.

  • Claude Code, Codex and Cursor: what they are 9 min
  • Working with a coding agent: brief, plan, review, test 10 min
  • Repository conventions: CLAUDE.md and AGENTS.md 9 min
  • Exercise: ship a small change with a coding agent exercise · 40 min
  • Module quiz 5 questions
4

Multi-agent systems

3 lessons · quiz

When one agent is not enough: orchestrators and workers, sub-agents and hand-offs, running in parallel, and picking a framework without regret.

  • Orchestrator and workers 10 min
  • Sub-agents, hand-offs and parallelism 10 min
  • Frameworks: Anthropic Agent SDK, OpenAI Agents SDK, LangGraph, CrewAI: how to choose 11 min
  • Module quiz 5 questions
5

Evaluation

4 lessons · quiz

Replace 'it seems to work' with numbers: build a test set, grade with rubrics and model judges, and run it on every change.

  • Why vibes fail: building a test set 10 min
  • LLM-as-judge and rubric scoring 10 min
  • Regression testing prompts and agents 10 min
  • Exercise: a 20-case eval set exercise · 45 min
  • Module quiz 5 questions
6

Model strategy

3 lessons · quiz

Which lever to pull (prompting, retrieval, fine-tuning), what open-weight models are for and how to run them, and how to route work between models on cost and latency.

  • Prompting vs RAG vs fine-tuning: the decision 10 min
  • Open-weight models: Llama, Qwen, gpt-oss, Mistral, and running them (Ollama, Groq, vLLM) 11 min
  • Cost, latency and routing between models 10 min
  • Module quiz 5 questions
7

Security for agents

3 lessons · quiz

Injection and exfiltration when the model can act, the sandbox and permissions that contain it, and the audit trail that proves what happened.

  • Prompt injection and data exfiltration in agentic systems 11 min
  • Sandboxing, least privilege and secrets 10 min
  • Auditing and monitoring agent actions 9 min
  • Module quiz 5 questions
8

Shipping to production

4 lessons · quiz

See what the agent is doing, keep the right humans at the right points, roll out with a safety net, and keep measuring after launch.

  • Observability: traces, tokens and cost per task 10 min
  • Human-in-the-loop design 9 min
  • Rollout, feedback loops and continuous evaluation 10 min
  • Capstone: production readiness review exercise · 60 min
  • Module quiz 5 questions