Continuous assurance for AI agents.
Operational trust, proven at runtime.
TrustEvals converts live agent behavior into measurable controls, drift signals, and export-ready audit evidence aligned to AIUC‑1, NIST AI RMF, ISO/IEC 42001, and related governance frameworks.
Built for production
Ingest traces → evaluate trajectories → trend drift → export evidence
Designed for audit readiness
Evidence freshness + change tracking across every control
Framework-aligned
AIUC‑1, NIST AI RMF, ISO/IEC 42001, EU AI Act and related standards
For security & GRC
CISO, GRC lead, Compliance
- Prove controls are working today, not last quarter
- Reduce audit scramble with continuous evidence
- Defensible story when models, prompts, and tools change
For AI & product teams
CAIO, Head of AI, Platform, Product
- Catch regressions before customers do
- Map PRs to control impact automatically
- Ship agents that pass enterprise security reviews faster
Screenshot compliance breaks in probabilistic systems.
Traditional controls are static. AI agents aren't. A prompt update, model swap, new tool, or RAG corpus change can silently break safety, privacy, reliability, and authorization behavior — without changing any “checkbox.”
- Controls drift as agent behavior changes
- Evidence goes stale the moment code or config changes
- Quarterly testing isn't continuous — risk can appear between audits
- Audits become panic mode because proof isn't ready
A control plane for AI agents — built for drift.
Three modules that work together to give you continuous visibility, audit-ready evidence, and shift-left control enforcement.
See the platformConvert runtime behavior into control signals
Track reliability, safety, tool-call behavior, and data exposure as time-series control signals. Alert when control health drifts.
- Turn traces and evals into Control Signals (time-series)
- Track reliability, safety, tool-call behavior, and data exposure
- Alert when control health drifts
Keep audit evidence continuously current
Store evidence as versioned, reproducible artifacts — not ad-hoc screenshots. Evidence includes run provenance, timestamps, source pointers, and freshness rules. One-click Audit Pack export.
- Versioned, reproducible evidence artifacts
- Run provenance, timestamps, source pointers, freshness rules
- One-click Audit Pack export
Stop control regressions before merge
Scans agent code and configs for control-breaking changes, maps findings to controls, and auto-invalidates stale evidence to trigger re-tests.
- Scans agent code and configs for control-breaking changes
- Maps findings to controls (tool auth, PII filters, retention, logging)
- Auto-invalidates stale evidence and triggers re-tests
From agent runtime → control proof.
Capture runtime traces
Ingest telemetry from production and staging agent flows.
Run layered evaluations
Apply deterministic checks first, then model-based analysis where needed.
Track control health
Monitor drift, thresholds, and trend breakpoints continuously.
Export defensible evidence
Produce audit-ready packs instantly, with provenance and freshness state.
What you can monitor.
Tool authorization & validation
Verify agents only invoke tools they're permitted to use with valid parameters.
Unsafe tool calls & rate limits
Detect dangerous or excessive tool invocations before they cause harm.
PII Leakage & Log Redaction
Ensure sensitive data is never exposed in outputs or logs.
Data isolation / tenant boundaries
Confirm agents respect multi-tenant data boundaries at runtime.
Groundedness & Citation Coverage
Measure whether agent responses are grounded in retrieved evidence.
Harmful output filtering
Catch toxic, biased, or policy-violating outputs automatically.
Regression detection
Identify behavioral regressions on critical workflows after any change.
Evidence freshness
Track what audit evidence is current and what needs re-evaluation.
Works with your stack.
Any trace-emitting framework
OpenAI
Anthropic
Bring your own model
GitHub
GitLab
Slack
Jira
Common Questions.
Answers to common questions about us, our approach, and how we can help.
TrustEvals provides both: a production-ready monitoring toolkit plus solution engineering support when teams need help integrating or tuning their setup.
Agent reliability problems are highly context-specific. Applied research helps us ground evaluations in your real production behavior instead of relying on generic benchmarks.
Yes. We can support the full loop: instrumenting traces, defining scorers, diagnosing failures, and iterating on prompt, policy, and workflow updates.
Absolutely. Teams often begin with a single high-impact monitor or scorer and expand incrementally as reliability requirements and traffic grow.
TrustEvals is best for teams shipping autonomous or semi-autonomous agents in production where behavior quality, safety, and operational confidence matter.
Stay audit-ready as your agents evolve.
Implement continuous control monitoring and evidence workflows tailored to your stack.