Agent health check
We test your agents against real cases and review their guardrails, logging and cost, then tell you where the risks are.
- Accuracy on real cases
- Guardrail and policy gaps
- Logging and audit readiness
- Cost per case and savings
Launching an agent is easy.Running it well is the job.
AI agents don’t stay good on their own: models change, data drifts, policies move and costs creep. Data Semantics runs your agents in production, testing every change, watching accuracy and cost and keeping the audit trail, whoever built them.
Your agents
Copilot StudioMicrosoft Agent FrameworkSerina & ZeboCustom agentsAny modelWe run them
- Testevery change, on real cases
- Monitoraccuracy and outcomes
- Governguardrails and policies
- Optimizemodel choice and cost
People review a sample
You get
Weekly performance reportsAudit & regulator packsLower cost per case+ Any platformAgents we didn’t build, too
Agreed service levels
In short
Data Semantics runs AI agents in production: testing every change, measuring accuracy on real cases every week, spotting drift, checking guardrails and keeping the audit trail. It works for agents we built, you built or another partner built, on any platform and any model.
We operate agents on
- Copilot Studio
- Microsoft Agent Framework
- Azure OpenAI
- OpenAI
- Claude
- Power Platform
- and others on request
Why it matters now
Launch day is the best an unmanaged agent will ever be.
Model providers update models, document formats change, policies move and volumes grow. None of it announces itself, so an agent that was accurate at launch can quietly get worse, riskier or more expensive. Production agents need the same testing, monitoring and ownership as any other critical system.
Launched and leftRun by Data Semantics
Model updatesSwitched without warningTested against your case library first
AccuracyAssumedMeasured every week on real cases
PoliciesChanged in a memoGuardrails updated and checked
CostGrows with volumeCost per case tracked, routine work on smaller models
AuditLogs scattered or missingAudit and regulator packs ready
OwnershipNobody in particularA named team under agreed service levels
What we run for every agent
Six checks on every production agent. People in charge.
Automated evaluation and monitoring do the constant checking. Our engineers and analysts make the calls.
Evaluation
Your test library runs against every change, and agents are scored on real cases every week.
You get: no untested change goes live
Checked by: our engineers
Drift detection
Shifts in inputs, accuracy or behavior are spotted, such as new document formats or a changed model.
You get: problems found early
Checked by: our analysts
Policy checks
Agent actions are checked against current policies and limits, and anything close to the line is flagged.
You get: guardrails that stay current
Checked by: your policy owners
Cost tracking
Cost per case and model is tracked, with cheaper routes recommended that keep quality.
You get: lower cost per case
Checked by: engineers test before switching
Review sampling
The right sample of cases is picked for human review, and escalations routed to the right person.
You get: people check what matters
Checked by: our analysts and yours
Reporting
Audit trails, performance reports and regulator packs are compiled from the logs.
You get: evidence on demand
Checked by: reviewed before release
Talk to an expert
Already have agents in production?
Tell us what they do and where they run. We’ll show you how we would test, monitor and report on them.
How we start
From first look to agents you can trust in production.
For agents you have live today, or are about to launch.
- 01 / BASELINE
Measure today
Score current accuracy, outcomes, cost and guardrails on real cases.
- 02 / INSTRUMENT
Make it visible
Add logging, test libraries and dashboards to every agent.
- 03 / RUN
Operate
Monitor, review, tune and report under agreed service levels.
- 04 / IMPROVE
Get better
Raise automation as accuracy is proven, and lower cost per case.
Ways to work with us
Check your agents. Then let us run them.
Works for agents we built, you built or another partner built.
AI Agent Operations service
We run your agents in production and keep improving them under agreed service levels.
- Testing before every change
- Weekly accuracy and outcome reporting
- Guardrail and cost management
- Audit and regulator packs
Common questions
Frequently asked questions
About AgentOps
Why do AI agents need ongoing operations?
Because their environment keeps changing. Models are updated, input data and formats shift, policies change and costs grow with volume. Without testing and monitoring, accuracy and safety can quietly degrade.
What is the difference between AgentOps and MLOps?
MLOps manages machine-learning models: training, deployment and drift. AgentOps manages agents that act: the prompts, tools, guardrails, policies and approvals around a model, and the business outcomes they produce. Data Semantics focuses on AgentOps, including the model choice beneath each agent.
Can you run agents that you didn’t build?
Yes. Data Semantics starts with an agent health check, adds logging and test cases where they are missing, then operate the agents alongside any we built.
Quality and safety
How do you red-team AI agents?
Before release and after every change, Data Semantics runs red-team scenarios alongside real past cases: attempts to push the agent outside its policies, misleading or injected instructions in documents and messages, and edge cases with unclear data. Failures block the change until they are fixed.
What happens when a model provider updates its model?
Data Semantics runs the new model against your test library before switching, compares accuracy, behavior and cost, and only moves when it passes.
Can you help us satisfy auditors and regulators?
Yes. Every agent action is logged with the data and rule behind it, and we produce audit and regulator packs showing what agents did, how they were tested and who reviewed them.
Cost and platforms
Can you reduce what our agents cost to run?
Usually. Data Semantics routes routine work to smaller, cheaper models, cuts unnecessary steps and tracks cost per case, always tested against quality first.
Can you move our agents to open-weight models?
Often, yes. Data Semantics tests open-weight models such as Llama, Mistral, Qwen and DeepSeek against your case library and moves routine work to them where quality holds, to lower cost or to keep data in a private environment.
Getting started
What is an agent health check?
A fixed-price review in which Data Semantics tests your agents against real cases and reviews their guardrails, logging, audit readiness and cost per case, then sets out the risks and savings. It works for agents on any platform.
How is AI agent operations priced?
Data Semantics prices the agent health check as a fixed-price engagement. AI Agent Operations is then a monthly managed service, measured on the outcomes your agents exist to deliver.
Start the conversation
Is your AI agent still as good as the day it launched?
Our agent health check tests your agents on real cases and shows where accuracy, safety or cost is slipping.
