Key takeaways
- An agent combines model decisions with tools, state and a control loop.
- Give tools the narrowest permissions needed.
- Measure task success, not just convincing explanations.
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The essential agent loop
A basic agent receives a task, decides on an action, invokes an allowed tool, observes the result and decides what to do next. It may stop when the task is done or when a time, budget or iteration limit is reached.
A model generating a plan is not sufficient. A useful agent needs reliable tool contracts, state handling, error recovery and verifiable completion conditions.
Tools, retrieval and state
Tools expose specific capabilities such as search, database queries, code execution or ticket updates. Retrieval-augmented generation (RAG) can provide relevant documents without retraining the core model.
Record which sources informed a step. Treat tool output and retrieved web pages as untrusted input, not as instructions with authority over the system.
- Use allowlisted, typed tools with input validation.
- Keep credentials server-side and use least-privilege permissions.
- Set clear tool-call ceilings and retry policies.
- Require explicit human approval for high-impact writes or purchases.
Agent evaluation
Build repeatable tasks with expected outcomes and inspect traces. Track success rate, false actions, latency and cost. Test adversarial or misleading tool responses to measure prompt-injection resilience.
For many tasks, a workflow with deterministic steps is cheaper and more reliable than an open-ended agent. Choose autonomy only where it delivers demonstrated value.
Sources & further reading
Primary sources and learning resources. Verify specific product and research claims directly.
