Evaluation suites
Define repeatable cases and expected behaviour across agent workflows.
ELANROVE / AGENT RELIABILITY
Make autonomous AI
prove it is ready.
Evaluate, stress-test, trace and govern AI agents before they reach production — with evidence that shows what happened, why it happened and what changed.
The reliability problem
A successful demo does not prove production readiness.
Agents can call tools, access data, follow plans and make decisions. Reliability requires more than a clean happy-path run. It requires repeatable tests, traceable failures, regression evidence and control over approvals and external actions.
5 / 5Baseline external-agent validation in the controlled test run
1Governance bypass surfaced in adversarial testing
TRACEFailure evidence and evaluator feedback retained
V0.9Controlled-pilot platform baseline
The method
Evaluate the behaviour. Diagnose the failure. Prove the control.
01
Evaluate
Run defined tasks and scenarios against expected outcomes and evaluation criteria.
02
Stress-test
Exercise ordinary, adversarial, unreachable-integration and governance-sensitive conditions.
03
Diagnose
Trace failure detail, evaluator feedback and execution context instead of stopping at pass/fail.
04
Compare
Use regression testing to identify behaviour that changed between runs or versions.
05
Govern
Inspect approval behaviour, tool mappings, external-agent calls and audit evidence.
06
Prove
Produce evidence for a readiness discussion, not just another opaque score.
Evidence, not decoration
Reliability becomes useful when a failure can be explained.
The platform brings together evaluation results, failure explorer details, traces, feedback, approvals and regression evidence so a team can move from “something failed” to “what failed, where and why.”
Readiness snapshot
97
✓Baseline scenariosPass
!Approval bypass scenarioFail
✓Unreachable integrationClassified
Failure trace
01Agent received governed taskOK
02Planner selected external toolOK
03Approval expectedExpected
04Action executed without approvalFailure
05Governance finding recordedEvidence
Platform capabilities
A reliability control plane for serious agent testing.
The current platform baseline includes persistent knowledge, vector search, planning/evaluation, citations, approval controls, test suites, evaluations, traces, regression testing, Failure Explorer, tool registry, MCP/external-agent adapters and evidence-oriented reporting.
Failure Explorer
Open failures with evaluator feedback and detailed execution evidence.
Regression testing
Identify when a change improves one path but degrades another.
Approvals & audit
Track governance-sensitive actions and retained approval evidence.
External agents & tools
Validate tool mappings, MCP-style connections and external-agent behaviour.
Readiness evidence
Turn tests, risks and failures into a practical remediation discussion.
Commercial engagement
Start with a production-readiness assessment.
For organizations that are not ready to buy a platform, ELANROVE can begin with a focused assessment of an agent or AI workflow and provide evidence, risks and a remediation roadmap.
AI Agent Readiness Assessment
A scoped engagement to test reliability, governance, failure handling, regression risk and operational evidence before production.
Request an assessment- Test scope & readiness criteria
- Behaviour evaluation
- Failure and trace analysis
- Governance / approval checks
- Regression observations
- Risk register
- Remediation roadmap
ENTERPRISE AI
If an AI system can act, it should also be able to prove how it acted.
Bring a representative workflow, agent endpoint or evaluation problem. We can scope the evidence needed before production.
Discuss readiness