AgentOps & MLOps Services
Keep AI agents dependable after launch: controlled releases, versioned prompts and models, cost limits, rollback and the operational discipline to run them like any other production system.
What Changes Once an Agent Is Live
A working pilot is not the same as a system your team can rely on every day.
Silent changes
Model updates, prompt edits and data changes can alter behaviour without anyone noticing.
Unclear ownership
When something goes wrong it is often unclear who is responsible for the agent, its data and its outputs.
Unpredictable cost
Usage-based pricing can grow quickly without limits, caching or routing in place.
What We Set Up
We put the operating basics in place so agents can be changed, checked and rolled back safely.
Versioned releases
Prompts, tools, model choices and configuration are versioned together, with a record of what changed and why.
Evaluation before release
Each change is run against a test set of real (anonymised) cases before it reaches users.
Rollback and kill switch
A tested way to return to the previous version, or pause the agent entirely, without a code deployment.
Cost and usage controls
Budgets, alerts, caching and model routing so spend stays within agreed limits.
How It Works
Review the current setup
We look at how the agent is built, deployed and changed today, and where the risks are.
Build the pipeline
We set up environments, versioning, evaluation and release steps that fit your existing tooling.
Define runbooks
We write down who does what when quality drops, costs spike or an incident occurs.
Hand over or operate
Your team runs it with our support, or we operate it with you under an agreed scope.
Where People Stay in Control
Release approval
No new version reaches users until a named owner signs off on the evaluation results.
Pause at any time
Operators can switch an agent off or back to manual handling in one step.
Change history
Every change to prompts, tools and models is recorded and reviewable.
What We Measure
Measures are agreed during the readiness diagnostic and reported against your baseline.
Release quality
Evaluation scores before and after each change, against an agreed test set.
Incidents and recovery
Number of incidents and time taken to detect and roll back.
Cost per task
Spend per completed task, tracked against the agreed budget.
Frequently Asked Questions
What is AgentOps?
AgentOps is the practice of deploying, versioning, evaluating and monitoring AI agents in production. It extends MLOps to cover prompts, tools and multi-step behaviour.
Do we need this for a small pilot?
A pilot needs a lighter version: versioning, a test set and a way to switch it off. The full setup matters once real users depend on the agent.
Will this work with our cloud and CI tools?
Yes. We build on the platforms and pipelines you already use rather than introducing a separate stack.
Related
AI Performance Monitoring
Quality, accuracy, cost and drift tracking for agents already in use.
MCP & Legacy System Integration
Connecting agents safely to the ERP, CRM and document systems you already run.
Governance & Trust Center
How we handle data, approvals, audit trails and access control on every build.
Start With a Free 30-Minute AI Opportunity Review
We will look at one workflow with you and tell you honestly whether it is ready to automate, and what to fix first.
Not ready to talk yet? Get the AI Readiness Checklist →