Why agents drift after launch
The world an agent works in does not hold still. Your products change, your documentation updates, an API you depend on gets revised, and new kinds of requests show up that no one anticipated. Without upkeep, an agent that worked at launch slowly gets less reliable.
Maintenance is what keeps it aligned with reality. It is the difference between an agent that stays useful and one that quietly degrades until someone notices it is wrong.
- Model and provider updates that can shift behavior
- Changes to the tools and APIs the agent depends on
- New question types and edge cases from real users
- Updated content and data the agent needs to reflect
- Business rules that evolve and need to be pushed into the agent
Monitoring, evaluation, and tuning
We keep an agent healthy by watching how it behaves, measuring it against a test set of real cases, and tuning it when the numbers slip or new needs appear. That turns maintenance from firefighting into something proactive.
You can review conversations and outcomes, catch problems before customers do, and feed real examples back into the agent so it steadily improves rather than stagnates.
- Monitoring and alerts so failures surface fast, not weeks later
- An evaluation set of real cases to measure quality over time
- Regular review of real conversations to find gaps and errors
- Tuning prompts, tools, and content as your business changes
- Regression checks so a fix in one place does not break another
- Updating the agent's knowledge as your documentation changes
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Frequently asked questions
Can't we just launch it and leave it?
You can, but its reliability will drift as your data, tools, and use cases change. Even a great agent at launch needs upkeep to stay great. How much depends on the agent; a stable internal tool needs less attention than a customer-facing one handling constant new cases.
Do you offer ongoing maintenance, or is it a one-time build?
Both are options. Some clients want the agent built and handed off with monitoring in place so their team can run it. Others prefer we handle ongoing monitoring, evaluation, and tuning. We will recommend what fits your team's capacity and discuss it during the consultation.
How do we know if the agent is getting worse?
Through measurement, not gut feel. We set up an evaluation set of real cases and monitoring so quality is something you can actually see over time. If performance slips, whether from a model update or a changed API, you find out from the numbers, not from an angry customer.