AI agent development: a practical guide

An AI agent is a model that can decide which tools to use — look up an order, search documents, update a CRM — and loop until a task is done. That power is exactly why agents need more engineering than a chatbot.

Do you need an agent?

If the task is "answer questions from our documents", you need retrieval, not an agent. If the steps are always the same, you need a workflow with an AI step in it. Reach for an agent when the path genuinely varies — different requests need different tools in different orders.

The building blocks

• Tools. Small, well-described functions with strict inputs: get_order(id), issue_refund(order, amount). Good tool design matters more than prompt cleverness.
• Context. The policies, customer data and history the agent needs — fetched with the user's permissions.
• Memory. What it should remember between steps and between conversations, and what it must forget.
• Guardrails. Limits on amounts, actions and data; human approval for anything consequential.
• Observability. Every step traced: what it saw, what it decided, which tool it called, what came back.

Evaluate like you mean it

Build a test set of real tasks with expected outcomes, including tricky and adversarial ones. Measure task success, wrong actions, cost and latency. Re-run it on every change.

Roll out in stages

1. Shadow mode. The agent proposes, humans act. Compare its suggestions to what people did.
2. Approval mode. The agent acts after a one-click human approval.
3. Autonomy for low-risk actions. Only where accuracy is proven and mistakes are cheap to undo.

Common mistakes

• Giving the agent broad API keys instead of narrow tools.
• No test set, so nobody knows whether a change made it better.
• Skipping logging, then being unable to explain a bad action.
• Starting with the hardest, highest-risk workflow.

See an agent workflow in action on our AI development page, or talk to us about yours.