Understand agents. Build systems. Ship solutions.
First-principles explainers on AI agents, architectures, and automation concepts. Deep dives written for Google Search.
A workflow lets code decide what happens next, and an agent lets the model decide. This post gives one test that separates a chatbot, a workflow, and an agent, then counts the paths to show why that decides how you test them, with one invoice task built both ways and code you can run.
AI agent vs LLM: the LLM is the policy inside the agent loop, picking each action by probability. See why 148 of 1,000 simulated runs skipped a check.
An agent loop is a language model called over and over, with ordinary software carrying out its actions and feeding back the results. This post builds the loop from first principles: the equation, the harness that must decide when to stop, why tokens read grow with the square of the steps, a traced run, and about 30 lines of Python you can run offline.