Stanley's AI Systems Lab

AI Agents

This track is for builders who want agents that complete bounded work instead of impressive demos that fall apart in production.

Search intent

Readers searching for AI agent tutorials usually need a practical design path: when to use an agent, how to constrain it, what to log, and how to know if it worked.

Who this is for

Founders, product engineers, and operators building research, support, QA, or workflow assistants.

Outcome

Design an agent with a clear job, permission boundary, tool list, run log, stop condition, and review loop.

What you will build

A one-page agent spec with job, boundary, tools, memory, and stop rules.

A run-log template that records tool calls, observations, uncertainty, and final decisions.

A small eval set using real tasks instead of a one-off demo prompt.

A human approval path for risky or low-confidence actions.

Curriculum

Define the agent loop

Separate agents from prompts, chatbots, and deterministic workflows before choosing the architecture.

Choose the simplest useful pattern

Use a decision framework to pick chatbot, automation, or agent based on the job and failure mode.

Add boundaries and evals

Turn the agent into an inspectable product system with permissions, run logs, stop rules, and regression checks.

Proof payloads to create as you learn

The goal is not to read more AI takes. Build reusable artifacts that make the workflow inspectable, reviewable, and credible.

Agent spec worksheet with allowed and blocked actions.
JSON run-log example for a sales research or competitor monitoring agent.
Eval table comparing a normal automation against an agent on five real tasks.

Get the next AI Agents tutorial

Practical, proof-backed AI systems notes for founders and product engineers. No prompt-hack filler.