Agent Monitoring & Optimization

What agents do is measured, on quality, cost and escalations, and the agents are improved accordingly. An agent without monitoring is an experiment, not an operating asset.

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A man sits alone at a desk in an open office at night reading a single wide monitor, an orange mug beside the keyboard.

An unoccupied desk by a window with two monitors and a laptop still awake, a grey chair pushed aside and an orange mug beside the keyboard.

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What we look at

From a pilot to an operation you can rely on.

Every agent has metrics

Completed cases, quality of results, escalation rate and cost per case. The numbers show whether an agent meets its business case, not gut feeling.

Escalations are the most important signal

Where agents regularly hand over to people, there is either a gap case or a system problem. Both are evaluated and fixed, specifically rather than broadly.

Changes are safeguarded

New models, changed instructions or extended rights only go live after a regression check. An agent that worked yesterday should still work tomorrow.

What you receive

Four things that make agents dependable.

Quality & cost metrics

Per agent: completed cases, quality of results and cost per case, set against the business case.

Escalation analysis

Handovers to people evaluated systematically: close the gap cases, fix the system problems.

Continuous optimisation

Instructions, knowledge and tools of the agents improved on the basis of measurement.

Safeguarded changes

New models and prompts only go live after a regression check.

FAQ

Frequently asked questions about Agent Monitoring & Optimization

How do you measure the quality of an agent?

Through defined test cases and samples in operation: was the case handled correctly, would a person have decided differently, was the escalation justified. The criteria come from the use case, not from the model.

Why do agents need their own monitoring?

Because their behaviour changes without anyone touching code: new model versions, a different data environment, new case types. Classic system monitoring sees none of that, while agent monitoring measures results.

What happens if an agent gets worse?

The measurement shows it early, as quality falls or escalations rise. Then it is improved specifically, through instructions, knowledge and tools, and the change is safeguarded by a regression check before it goes live.

Do we see the numbers ourselves?

Yes. Metrics per agent sit on the dashboard, not in the engine room. Owners see performance, cost and trends, which is the basis for deciding which agents to expand.

Does this connect to governance?

Closely: monitoring supplies the evidence AI Governance & Compliance requires, including logged actions, checked changes and documented escalations.

Case Studies

What comes after the plan.

See all Case Studies

Ricoh

+365 %

More conversions through CRM and content.

Dovista

68 %

Of all enquiries automated.

Other Automate services

Three more services in Automate

If Automate is not the right starting point for you, explore the other phases of the Agentic Growth Stack as well.

Explore Plan Services

Explore Build Services

Explore Operate Services

AI Agents & Copilots

Agents that take on tasks instead of merely answering: qualifying leads, preparing quotes, resolving tickets.

AI Workflow Automation

End-to-end processes across system boundaries, with AI at the points where fixed rules are not enough.

AI Governance & Compliance

Guardrails for using AI: approvals, logging and traceability, built in from the start.

first step

The first step towards an agent-native company.

In the Enterprise Discovery Workshop we develop a clear target picture and a business case that holds up, in a matter of days. Fixed price, no open-ended day rates.

Smiling man in a dark blue suit in a bright office.

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We get back to you personally.

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