AI Performance Monitoring
See how your AI agents are really performing: accuracy, escalations, cost and drift, reported against the baseline you agreed at the start, with alerts when something needs a person to look.
Why Monitoring Matters for Agents
Agent quality can change over time even when nobody touches the code.
Drift
Inputs, documents and business rules change, and answers that were right last quarter can quietly become wrong.
Hidden failure
An agent can return confident, well-formatted output that is incorrect unless someone is checking.
No shared view
Without clear reporting, business owners cannot tell whether the agent is still worth running.
What We Monitor
We set up monitoring that tracks both technical health and business outcomes.
Quality sampling
Regular review of a sample of outputs by your team, scored against clear criteria.
Automated checks
Rules and evaluation tests that flag missing sources, policy breaches or unusual answers.
Cost and latency
Spend, response time and usage by workflow, with alerts on agreed thresholds.
Business dashboards
Plain reporting for owners: volumes, escalations, time saved and open issues.
How It Works
Agree the baseline
We record how the workflow performs today so improvements can be measured honestly.
Instrument the agent
We add logging and tracing to each step, with personal data handled according to your policy.
Set alerts and reviews
Thresholds trigger alerts, and a regular review looks at samples and trends.
Act on findings
Issues feed back into fixes, prompt changes or process changes, through a controlled release.
Where People Stay in Control
Human review of samples
People regularly check real outputs, not just metrics.
Alert routing
Alerts go to a named owner with context, not to a shared inbox.
Decision to pause
Owners can pause an agent when quality falls below the agreed level.
What We Measure
Measures are agreed during the readiness diagnostic and reported against your baseline.
Accuracy against baseline
Output quality compared with the starting point, using the same criteria.
Escalation rate
How often items are handed to people, and why.
Cost and response time
Spend per task and time to complete, over time.
Frequently Asked Questions
Can you monitor agents we built ourselves?
Yes. We can add monitoring to existing agents as long as we can access logs or add instrumentation.
Does monitoring expose sensitive data?
We design logging to your data policy, including redaction and retention limits, and keep access restricted.
How often should outputs be reviewed?
It depends on risk and volume. Higher-risk workflows get more frequent and larger samples; we agree this with you.
Related
AgentOps & MLOps
Running agents in production: deployment, versioning, rollback and cost control.
AI Workflow & Readiness Diagnostic
A short, structured review of one workflow: data, systems, risks and whether it is worth automating.
Executive Reporting Agent
An illustrative use case: reporting drafted by an agent and reviewed before release.
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 →