Managed AI Operations
Deployment is day one. Operation is the product.
Models change, data drifts, business requirements move. AutoMSP monitors, evaluates, maintains, and improves production AI systems as an ongoing service — so reliability doesn't depend on whoever built it staying around.
What ongoing operation includes
The work that starts after launch.
Nightly operating cycle
Once per day your workspace runs a full cycle: agents execute scheduled work, an orchestrator reviews what moved and what stalled, and you receive a structured morning brief — what ran, what we learned, what needs your decision.
Monitoring with real numbers
Execution counts, failure rates, approval latency, agent-run volume, and estimated model cost per workflow — recorded from actual operations, never fabricated. Your audit establishes the baseline.
Evaluation before and after every change
Changes to prompts, models, or workflows pass evaluation suites with recorded pass rates against known cases. A regression is caught in evaluation — not by your customers.
Incident and failure handling
Failed executions are surfaced, not swallowed. Repeated failures across clients become engineering fixes pushed to the platform — one tenant's edge case hardens everyone's system.
Optimization against real usage
Prompts, retrieval settings, and model routing are tuned based on recorded outcomes and cost data — cheaper where quality holds, stronger where it doesn't.
Why it matters
Most AI systems fail quietly.
An unmonitored automation doesn’t announce its decay. Retrieval quality slips as documents change. A provider adjusts a model and outputs shift. Exceptions pile up in a queue nobody watches until someone asks why the numbers look wrong.
Managed operations exists to catch that. Recorded runs, evaluation gates on every change, and a daily operating rhythm mean degradation is measured and corrected — not discovered at quarter end.
FAQ
Operating questions, answered.
- What exactly do I receive each day?
- A structured brief covering what ran overnight, what completed or failed, what was learned, and what requires your decision — including any items waiting in your approvals queue. Nothing consequential ships without your review regardless.
- How quickly are failures addressed?
- Failures are visible immediately in your workspace with retry and root-cause context. Committed response-time SLAs belong to managed engagements and are documented in the agreement rather than implied on a website.
- What if we want to change or expand a system later?
- That's the point of managed operations. New use cases are prioritized through the same discover-design-deploy path, building on existing integrations and infrastructure rather than starting over.