Your AI automation works in testing and breaks in production
AI automations usually break in production for one of three reasons: the real data is messier than the test data, the workflow has no way to handle a step failing, and nobody can see what the agent actually did. None of those are model problems. They are engineering problems, and they are fixed with error handling, logging, and a data layer that is allowed to be imperfect.
Why the demo worked and the system does not
A demo runs once, on data someone picked, with a person watching. A system runs a hundred times a day on whatever arrives, including the record with a missing phone number, the customer whose name has an apostrophe in it, and the API that returns a 502 at four in the afternoon.
The gap between those two things is not intelligence. It is the unglamorous engineering that surrounds the model: retries, idempotency, validation, queues, and somewhere to look when it goes wrong. Most AI automation that fails in production fails because that layer was never built.
What a working agent looks like
It has a defined job with a boundary around it. It has permission to touch specific systems and no others. Every action it takes is logged in a way a human can read afterwards. When it cannot do something, it fails loudly rather than guessing.
It also has a person who owns it. An agent nobody maintains degrades quietly as the systems around it change, and the first sign of trouble is usually a customer noticing before you do.
What this looks like as a build
- Agents that act against your real systems, with scoped permissions and an audit trail
- Retry, queue and rate-limit handling for third-party APIs that fail intermittently
- Validation on the way in, so bad records get flagged instead of silently corrupting the run
- Monitoring and alerting, so you find out before your customer does
This is not a separate service with its own price
There is one thing to buy here: a build seat, from $7,500 per month. This page describes one of the things people point a seat at. You set the order, and the seat works one item at a time until each is done.
Questions about this kind of work
- Can you fix an AI workflow someone else built?
- Yes, and it is a common first item. I read what is there, tell you what is salvageable and what is not, and rebuild the parts that are worth keeping. I will not pretend a rebuild is a patch if it is really a rebuild.
- Do I need to move to a different AI platform?
- Usually not. The platform is rarely the problem. The missing engineering around it is the problem, and that can normally be added to what you already have.
- How long does something like this take?
- A single agent with a clear job is often live inside the first two weeks. A workflow that touches several systems takes longer, and gets broken into items that each go live on their own rather than landing all at once.
Start with a call, not a contract
Thirty minutes. You describe what broke, what it is costing you, and what you have already tried. I tell you whether a seat is the right answer, and I will tell you when it is not.
Or reach me directly: +1 385-208-1781 scott@aetenum.com
If you are comparing quotes on price, I am not your cheapest option and I will not pretend otherwise.