Pick for tool following.
Test whether the model follows schemas, handles retries, and asks for missing information before using tools.
Agent architecture
A Useful AI Agent Stack Is Mostly State, Tools, Logs, and Judgment.
An agent is not magic prompt glue. The reliable version has a narrow workflow, explicit tool permissions, durable state, retries, human approvals, evaluation, observability, and a fallback route when the model or tool fails.
Last updated May 21, 2026. Agent frameworks change quickly, but the safety and reliability checklist stays useful.
Blueprint
Keep the agent workflow narrow and auditable.
User taskPolicyPlanToolsQueueStateApprovalExecuteEvaluate
Best beginner scope: pick one repeatable task, one or two tools, one approval step, and one success metric. Avoid open-ended "do anything" agents until your logs and failure handling are boring.
Make contracts explicit.
Every tool needs input schema, permissions, rate limits, error messages, and audit logs.
Do not store everything in prompt text.
Track tasks, steps, decisions, tool outputs, approvals, and retries in a durable store.
Measure task success.
Use scenario tests, tool error rates, human review outcomes, latency, and cost instead of vibes.
Guardrails
Give the agent less freedom and better feedback.
Most useful agents are constrained operators. They know which tools they can call, when to ask for help, when to stop, and what evidence must appear before action.
Agent safety checklist
- Define allowed tools and blocked actions.
- Add human approval for irreversible or expensive steps.
- Set request, token, and tool-call limits.
- Log every model decision and tool response.
- Build fallback behavior for API, rate-limit, and tool errors.
- Run scenario evals before giving real user access.
People also ask
AI agent stack questions.
What is the simplest useful AI agent?
A narrow workflow that reads one input, calls one safe tool, asks for approval when needed, and writes one auditable output is often better than an open-ended autonomous agent.
Do agents need memory?
They need durable state more than vague memory. Store tasks, tool outputs, decisions, user preferences, and approvals in structured data.
Which framework should I use for agents?
Use a stateful framework when the workflow has branching, retries, approvals, and long-running jobs. For one-step tools, direct API calls may be enough.