Key facts
- Main answer
- Start with the least autonomous design.
- Framework answer
- Choose after the agent spec exists.
- Safety answer
- Evaluate route, output, and stop reason.
- Free tools
- Checker, spec generator, framework chooser.
Questions
How do I turn my idea into an AI agent?
Walk the idea through the Decision Gate first. If the stamp says prompt, workflow, assisted, not-ready, chain, or human-assist, build that instead. If it stamps a scoped agent, write tools, permissions, stop conditions, and eval fixtures, then choose a framework. Autonomy comes last.
Do I need an agent or just a prompt?
Use a prompt when one model response completes the job. Use a workflow when the next action is knowable before the previous step returns. Use an agent only when tool observations change the next action, the contract is written, and side effects have an approval. The Decision Gate at /gate/ walks that order and stops at the first wrong shape.
When is an agent the wrong shape?
When one response is enough, when the route is known, when success is still unwritten, when there are no tools to observe, or when write tools have no approval. Those five stops are the Decision Gate. Passing them warrants a scoped loop, not a swarm.
What framework should I use?
Choose LangGraph for explicit stateful orchestration, CrewAI for role-based multi-agent collaboration, OpenAI Agents SDK for compact Python or TypeScript agent apps, Claude Agent SDK for self-hosted tool-rich agents, and custom orchestration when your needs are narrow.
What is the architecture of an AI agent?
Most useful agents combine instructions, tools, state, retrieval or memory, an action loop, policy gates, evaluation, and observability. The hard engineering is around tool boundaries and failure handling, not the prompt alone.
How much autonomy should I give an agent?
Start with read-only tools and human approval for side effects. Increase autonomy only after traces and evaluations show the agent handles normal cases, edge cases, and abuse cases within your risk tolerance.
How do I evaluate an agent?
Use task-based fixtures with expected outcomes, adversarial inputs, tool-call assertions, cost and latency budgets, trace review, and production monitoring. Do not rely on a demo transcript as evidence.
The useful next step
If you are still at the idea stage, write a PRD first. If you have a PRD, write the technical spec with tools, state, non-goals, failure cases, and done criteria. If you already have that spec, start with the smallest read-only agent loop and an evaluation suite. Autonomy can wait until the evidence earns it.
Sources used
- Agents SDKOpenAI Developer Docs. Accessed 2026-07-13.
Defines agents as applications that plan, call tools, collaborate, and keep state.
- Building Effective AgentsAnthropic Engineering. Accessed 2026-07-13.
A practical distinction between augmented LLMs, workflows, and autonomous agents.
- OpenAI Agents SDKOpenAI. Accessed 2026-07-13.
Documents the Python Agents SDK primitives: agents, handoffs, guardrails, tracing, sessions, tools, and sandbox agents.
- Agent SDK OverviewClaude Code Docs. Accessed 2026-07-13.
Documents Claude Agent SDK built-in tools, hooks, subagents, MCP, permissions, sessions, and production use cases.