AUTONOMOUS WORKFORCE
Managed AI agents, run by the people who built them. We watch the evals, route to cheaper models that still clear the bar, repair drift, absorb the changes your systems throw at us, and add agents each quarter. You get the output and a monthly report.
What AgentOps means in practice
A managed AI workforce is an operating model: somebody is accountable for keeping your agents accurate, affordable and available, the way somebody is accountable for keeping your servers up. The deliverable is not a system. It is the system continuing to work.
This exists because AI systems decay in ways ordinary software does not. Model providers update and behavior shifts. Your data drifts away from what the agents were tuned on. A supplier changes an invoice layout, a system changes an API, a new category of exception appears that nobody wrote a rule for. None of these announce themselves. They show up as a slowly falling pass rate that nobody is watching.
The honest version of this service is that most of it is unglamorous monitoring, and the value is in catching things early. We would rather describe it that way than sell it as an AI center of excellence.
You probably need this if you recognize these
- You built something that worked, and nobody owns it now.
- Nobody can say whether quality is better or worse than three months ago.
- Your model bill moves and nobody can explain why.
- A provider deprecated a model and you found out from a failure.
- The person who understood the prompts has moved teams.
What running it actually involves
Eval suites, rerun continuously
Against real inputs, on every model change, so quality is measured rather than assumed.
Live monitoring
Latency, error rate, escalation rate, cost per task and pass rate, with alarms on each.
Model routing
Each task on the cheapest model that clears its bar, re-tested as new options appear.
Drift detection and repair
Input and output drift caught early, with a defined response rather than a rebuild.
Change absorption
When your systems, formats or rules change, we adapt the agents. That is included, not a variation order.
New agents each quarter
Candidates from the original backlog, re-scored against what has actually changed.
A monthly report
What ran, what it cost, what escalated, what we changed and what we recommend next.
How the month runs
Managed AI agents and AgentOps
Watch
Dashboards and alarms run continuously. Nobody waits for a monthly review to notice a problem.
Self-healed - retried with fallback tool. Human not required.
Test
Eval suites rerun on schedule and on every model or prompt change.
Tune
Routing, thresholds and retrieval are adjusted where the evidence supports it.
Repair
Drift, broken integrations and new exception categories are fixed inside the retainer.
Extend
One or more agents are added each quarter from the re-scored backlog.
Report
A monthly report covering volume, cost, quality, incidents, changes and recommendations.
Agents that need the most tending
Self-Healing Infra Agent
High autonomy and real consequences, so its boundaries and evals get the closest attention.
Analytics Digest Agent
Quietly degrades when upstream data changes, which is exactly what drift monitoring catches.
Pipeline Forecast Agent
Needs periodic re-baselining as your business changes shape.
What we watch
We instrument the agents, not your whole estate. What we watch is what we are accountable for.
- Model providers and gateways
- Observability and logging
- Alerting and on-call
- Your agents' target systems
- Cost and billing platforms
- Data warehouse
- Ticketing, for our escalations to you
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
What we may change without asking
Autonomy boundary
We may tune prompts, routing, thresholds and retrieval within the agreed quality and cost envelope. Changing an agent’s autonomy boundary always needs your sign-off.
Approval gates
Any change that widens what an agent may do alone, touches a new system, or alters an approval path goes to you first.
What stays human
Your business rules, your risk appetite, and the decision to add or retire an agent.
Logging
Every change we make is recorded with its reason and its measured effect, and appears in the monthly report.
How the engagement runs
Typical shapes from our engagement model (doc 04 §5 and §8), not a quote.
| Stage | Typical | What happens |
|---|---|---|
| Take-on | 1-2 weeks | We inventory what exists, run a baseline eval, and write down the quality and cost envelope we are agreeing to. |
| Stabilize | 2-4 weeks | Gaps in monitoring, evals and runbooks are closed before we take accountability. |
| Run | monthly, ongoing | Watch, test, tune, repair, extend, report. Priced against cost per task rather than per seat. |
| Hand back | whenever you choose | Runbooks, evals, dashboards and training. No exit fee and no hostage-taking. |
What we measure
- Pass rate
- Tracked per agent against its eval suite, reported monthly (Target)
- Cost per task
- Monitored and driven down by routing, not by cutting quality (Target)
- Time to detect
- How quickly a quality or cost regression is caught - the number this service lives on (Target)
- Your envelope
- The quality and cost boundary we agree at take-on and report against (Yours)
“ Until then these are design targets and measurement commitments, not results.
What this looks like in practice
Self-healing hive
Reference scenario · SaaS and infrastructure. Nightly incident volume absorbed before the on-call phone rings.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
Can you run agents you did not build?
Yes, and we take on other people's systems regularly. Take-on starts with an inventory and a baseline eval, because we will not put our name to a quality envelope we have not measured. Sometimes that baseline shows the system needs work before it can be operated sensibly, and we say so before signing rather than after.
How is this priced?
Against cost per task rather than per seat, because seats are the wrong unit for work nobody sits down to do. The shape is a monthly retainer covering monitoring, evals, routing, repairs and a quarterly agent addition. The pricing page sets out the three engagement shapes.
What if we want to bring it in-house later?
Then we hand it over: runbooks, eval suites, dashboards, routing configuration and training for your team. That is a deliverable, not a negotiation, and there is no exit fee. A managed service that depends on you being unable to leave is a bad service.
What is actually included when something breaks?
Drift, broken integrations, changed formats, provider deprecations and new exception categories are inside the retainer. Building a new agent, or extending one into a genuinely new process, is scoped separately. The line is written into the agreement at take-on so it is not argued about later.
Do you have access to our production systems?
Only what the agents need, with credentials scoped per agent and isolated from each other. Our people work through the same audited paths the agents do. Access is reviewed at take-on and revocable by you at any time, without our involvement.
How do we know you are actually improving it?
The monthly report shows pass rate, cost per task, escalation rate and every change we made with its measured effect. If a change made something worse, that is in the report too. It is deliberately the kind of document you could hand to a skeptical CFO.
Related services
Business Process Automation
Building the process this service then runs.
Marketing Agents
A department commonly run under this model.
AI Agent Development
Where most managed hives start.