𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗧𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁 𝗟𝗼𝗼𝗽

A demo agent is easy to build. A production agent is hard.

In a demo, the loop is simple: observe, decide, act, check, repeat. The model sees a result and moves to the next step.

In production, that same loop needs strict rules. A production agent is not just a model and some tools. It is a loop wrapped in structure to keep it honest.

The biggest trap is trusting tool responses.

Imagine an agent needs to cancel an order and issue a refund.

In a demo, this works. In production, this is a disaster.

A 200 OK often means the request was accepted, not that the task is done. The cancellation might still be pending in a queue. If the agent issues a refund before the cancellation is confirmed, you move money based on an assumption.

You must follow one rule: A tool response describes the request, not necessarily the world.

To build a safe production loop, you need these components:

• Tool Contracts: Every tool must define its input, output, failure modes, and an idempotency key. • Verification Gates: Do not just check if a tool returned a success code. Re-read the actual state of the world to confirm the change happened. • Working State: Keep facts separate from the chat history. This prevents the agent from hallucinating based on conversation text. • Approval Gates: Use humans to sign off on intent for high-stakes actions. • Stop Rules: Set limits on steps, cost, time, and silence to prevent runaway loops.

The loop structure does not change. The implementation becomes stricter.

For consequential actions, your "check" step must have two parts:

  1. Verify: Did the world actually change as expected?
  2. Commit: Now that it is confirmed, is it safe to move to the next step?

The safest agent is the most bounded one. Reliability comes from placing clear boundaries around the model, not from giving it more freedom.

Source: https://dev.to/gursharansingh/ai-agents-in-practice-part-6-building-the-production-agent-loop-2lfi

Optional learning community: https://t.me/GyaanSetuAi