Models drift. Prompts decay. Edge cases surface. Vendors release new tooling weekly. The retainer covers monitoring, prompt optimization, governance audits, model updates, and incident response — so your agents keep performing without adding overhead to your team.
Live accuracy, latency, cost, and confidence-distribution dashboards. Drift alerts. Sample audits on every workflow.
Continuous prompt tuning against real outputs. Versioned, A/B tested, rolled back when regressions appear.
When a vendor ships a better model, we test, compare, and migrate. You don't manage the AI vendor relationship — we do.
Quarterly review: who owns what, who's reviewing outputs, where policy gaps exist, where adoption has slipped.
When an agent does something wrong — and at some point it will — we have the runbook, the logs, and the on-call.
One page. What the agent did, what it cost, where it failed, what we changed, what we recommend next.
The deployment goes well. Accuracy looks good. Then a model gets deprecated, a vendor changes their API, an edge case slips through, or a team member leaves and the runbook isn't kept up. Six months later the agent is half-working and nobody owns it.
The retainer exists because that's the actual failure mode. Not the build — the run.
Typical accuracy degradation we observe across SMB workflows when agents are deployed and left alone for 90+ days. Not always linear — often a cliff at a model update.
Average time-to-fix for a drifted agent without a retainer in place. With T-02 / Run, we typically catch and correct within 48 hours.