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?
Start by writing the job, tools, data, success criteria, failures, and approval rules. Then build the smallest loop that can complete one valuable task and evaluate it on real examples before adding autonomy.
Do I need an agent or just a prompt?
Use a prompt when one model response is enough. Use a workflow when the sequence is predictable. Use an agent only when the system must choose tools, recover from partial failures, or adapt across multiple steps.
What framework should I use?
Choose LangGraph for explicit stateful orchestration, CrewAI for role-based multi-agent collaboration, OpenAI Agents SDK for lightweight Python-first OpenAI-native agents, Claude Agent SDK for codebase and local-tool 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 SDK OpenAI Developer Docs. Accessed 2026-07-06.
Defines agents as applications that plan, call tools, collaborate, and keep state.
- Building Effective Agents Anthropic Engineering. Accessed 2026-07-06.
A practical distinction between augmented LLMs, workflows, and autonomous agents.
- OpenAI Agents SDK OpenAI. Accessed 2026-07-06.
Documents the Python Agents SDK primitives: agents, handoffs, guardrails, tracing, sessions, tools, and sandbox agents.
- Agent SDK Overview Claude Code Docs. Accessed 2026-07-06.
Documents Claude Agent SDK built-in tools, hooks, subagents, MCP, permissions, sessions, and production use cases.