Managed AI operations is the work that keeps an AI workflow reliable after go-live: monitoring results, running evaluations against real cases, handling model changes, fixing what breaks and adding the next workflow. A named owner does it. You keep the code, the data, the documentation and the off switch, so it never becomes a black box.
#Key takeaways
- An AI workflow is not finished at go-live; inputs, models and business rules all keep changing.
- Monitoring and regular evaluations against real cases show when quality drifts before customers notice.
- A named owner on each side answers the question of who is responsible when an automation fails.
- Documentation and access in your name mean it still works when the builder leaves.
- You own the code, the data and the accounts, and you can switch it off.
Managed AI operations. The ongoing running of an AI workflow in production: monitoring, evaluations, model updates, fixes, improvements and new workflows, done by a named owner with the client keeping ownership and control.
#What happens after go-live, and who maintains the workflow?
The common story is familiar: it works until the builder leaves and nobody wants to touch it. A new invoice layout appears, a policy changes, or the model provider updates a version, and quality slips quietly. Without someone watching, the first sign is a complaint.
In Stennir's AI implementation work, managed AI operations follows go-live. A named person at Stennir owns the workflow's health, and a named person on your side owns the business rules and approves the output. Both are written down before go-live.
#What does managed AI operations actually include?
- monitoring: volumes, error rates, approval and rejection rates, and the cases people override.
- evaluations: a test set built from your real past cases, rerun after every change to prompts, rules or models.
- model changes: when a provider releases or retires a model, the new one is tested on your cases before it replaces the old one.
- fixes and improvements: rejected outputs are reviewed, and the rule or document behind them is corrected.
- next workflows: once the first one runs well, the next one is scoped from what the team has learned.
This matches how risk frameworks describe AI: the NIST AI risk management framework, january 2023 treats ongoing measurement and clear accountability as part of managing AI risk, and ISO/IEC 42001, december 2023 sets out a management system for AI rather than a one-off check.
#Who owns the code and the data after an AI company builds something?
You should, in writing. With Stennir the cloud accounts, model provider accounts, code repository and data are in your name from the start, and Stennir works inside them with access you can revoke. Hosting is set where your rules require. If the relationship ends, nothing has to be moved or bought back.
We are
Stennir runs your AI workflow after go-live as a named owner, inside accounts you own, with documentation and an off switch you control.
We aren't
Stennir is not a platform that keeps your workflow on its own servers, and not a builder that hands over code and disappears.
#How do you avoid inheriting a black box?
Ask for three things at handover. First, documentation a new engineer could follow: what the workflow does, the rules, the prompts, the test set and how to deploy a change. Second, logs of every action and approval, so any output can be traced. Third, a tested off switch, plus the manual process the team falls back to.
Weak controls end projects. Gartner, june 2025 expects over 40% of agentic AI projects to be cancelled by the end of 2027, partly because of inadequate risk controls. Operations is where those controls live.
#What should a COO ask before go-live?
Ask who is named on each side, what is monitored, how a model change is tested, where the documentation lives and how to switch it off. If any answer is vague, fix it before launch. Stennir's worked example on taking a prototype to production shows those pieces in place. See also AI for operations, take the free readiness assessment or book a 30-minute call.
#Common questions
What do you need from us during managed AI operations?
A named person who owns the business rules and approves the output, access to the systems the workflow touches, and a regular review where rejected and overridden cases are discussed. When a rule or policy changes on your side, that person tells Stennir so the workflow and the test set change with it.
What happens when the model provider changes or retires a model?
Stennir runs the new model against the evaluation set built from your real cases before switching. If results hold or improve, the change goes ahead with your approval. If not, the current model stays while prompts or rules are adjusted. Every change is logged so you can see what moved.
Can our own team take over later?
Yes. Because the code, accounts, documentation and test set are already yours, handing over to an internal engineer is a planned step rather than a rescue. Stennir can train the people who will run it through AI training so the knowledge does not leave with the builder.
Where does our data stay?
Where your rules require. Stennir sets up hosting in the region your regulator or policy needs, including where the model runs, not only where data is stored. Data stays in accounts you own, access is limited to what each step needs, and every action on it is logged.
