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Solution · AI Operations

Who maintains your AI agents once they're in production?

The agent got built, the demo went well and today it runs on its own. Until it drifts, a model changes, an integration goes down or the cost spikes — and nobody's job is keeping it working. That's the gap. You close it with a function, not a support ticket.

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The problem

The agent already runs on its own. The problem is nobody owns keeping it running well.

  • The model you were using changed version and the answers aren't the demo's anymore, but nobody saw it until a customer complained.
  • An integration went down on a Sunday and the agent kept "working" — returning nothing — for hours.
  • Token cost doubled and nobody noticed until the monthly invoice.
  • Whoever built the agent is on another project now; when something breaks, there's no owner, there's a WhatsApp group.

Cost of staying the same

An unmaintained agent doesn't break on day one: it degrades quietly. Model drift, integrations that fail without warning and rising cost show up on no dashboard until they've already done damage. Building the agent was 20%; operating it well is the 80% that decides whether it was an investment or an expensive demo.

The solution

An AI Operations function that owns keeping your agents working — not a patch when they fail

  1. 1We instrument every agent: a log of each decision, quality metrics and alerts for drift, integration downtime and cost. What isn't measured doesn't get maintained.
  2. 2We set the operating routine: who watches, how often edge cases get reviewed, how model or prompt changes get tested before they ship, and how an incident escalates.
  3. 3We manage the lifecycle: model updates, prompt tuning, cost control and periodic evals so the agent doesn't degrade over time.
  4. 4We leave it with an owner and a dashboard: incidents caught by alert not by damage, response time, cost under control and stable quality. A function, not an on-call hero.

What changes

What you stop losing

  • Drift and outages get caught by alert, not by a customer complaint: the failure shows before it costs.

    Mechanism

  • Agent cost stops being a month-end surprise and becomes a metric that's watched and capped.

    Mechanism

  • What we measure: incidents caught by alert vs by damage, response time, cost per agent and quality stability against baseline.

    What we measure

Spec sheet

Work it removes
nobody owning that AI agents keep working well once in production
Typical setup
1–3 weeks to instrument; continuous maintenance in phases
Input
your AI agents in production, their integrations, their logs and their cost
Output
an AI Operations function running: instrumentation, drift/outage/cost alerts, model and prompt lifecycle, and an owner with a dashboard
Works with
Tu stack de agentes en producciónTu stack de observabilidad y logging
Can connect to
Implementa's AI Operations frameworkYour evals and cost control
What we measure
incidents caught by alert vs by damageresponse time to an incidentcost per agentquality stability against baseline
Good fit for
companies with one or more AI agents already in production who need someone to own that they keep working
Not a fit for
anyone without an agent in production yet; there the conversation is how to build the first one right, not how to maintain it

Frequently asked questions

While they're available, sure; the problem is they almost never are. The person who built the agent moves to the next project and maintenance lands in no man's land. And maintaining isn't "fix it when it breaks": it's watching for drift, testing model changes before shipping them and controlling cost — a function with a cadence, not a favor when someone has a minute.

More. Support reacts when something breaks; AI Operations exists so it breaks less and so you see it before it costs. It includes instrumentation, drift and cost alerts, model and prompt lifecycle, periodic evals and an owner with metrics. It's operating a capability, not answering tickets.

Both, depending on your maturity. We can operate the agents ourselves as a managed function, set up the routine for your team to run, or a mixed model while your team grows. What we never leave is the gap: there's always an owner with a dashboard, whether it's us or yours.

Want it running in your business?

You’ve pinned the problem. We ship the fix and leave it measured.

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Who maintains your AI agents once they're in production? · Implementa