First ask whether you need an agent

If the workflow is a fixed transformation, use deterministic code. If it is a known decision tree, model the decision tree. Use an agent when the workflow needs flexible reasoning across inputs, tools, and context.

The most reliable AI systems often combine deterministic boundaries with model-powered interpretation inside those boundaries.

Add capabilities deliberately

Retrieval helps when answers depend on private documents or changing knowledge. Tool use helps when the workflow must call APIs or act on systems. Memory helps only when durable context improves future runs.

ts
Input -> classify intent -> retrieve context -> choose tool
  -> validate structured output -> human review if risky

Try the idea

Agent capability planner

Choose capabilities only when the workflow actually needs them.

Selected capability notes
  • Use when the answer must cite private docs, tickets, policies, or product knowledge.
  • Use before sending emails, changing records, charging money, or touching customer data.

Human review is a product feature

Approval steps should not feel like afterthoughts. If the agent drafts an email, edits a record, changes a workflow, or touches customer data, a human review step can prevent expensive cleanup.

Production notes for AI workflows

AI workflows need product boundaries as much as model prompts. Decide what the system can answer, which tools it can call, what data it can use, and when a human must approve the result.

Logs, structured outputs, evaluation fixtures, fallback states, and cost limits make the workflow operable after the first impressive demo.

Conclusion

The best agent architecture is not the one with the most autonomy. It is the one with the clearest boundaries.

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