Built with an SDK, understood without one: tools, memory, and the loop wired into one research agent over the awesome lists.
The three loop topologies (ReAct, plan-and-execute, orchestrator/subagent), why your agent goes in circles, and what Runner.run is actually doing for you.
Why agents forget, the three layers of memory you actually need, and how to wire a vector store into the loop without the framework hiding what's happening.
What an agent tool actually is, how OpenAI Agents SDK, Anthropic, and Google ADK each declare one, and why every tool is a blast-radius decision.
Inside the reason phase. How LLMs turn a goal into the next action, why chain-of-thought is just more tokens, and why your prompt is the agent's architecture.
Why an AI agent is more than a chatbot with extra steps, and the perceive, reason, act loop every framework eventually maps back to.
