/01
Scenario Studio
Turn a real operational problem into a typed, replayable evaluation scenario. Define agents, roles, dependencies, and success criteria in plain language, then version the result for every team and benchmark.
Evaluation and diagnostic intelligence
Make AI
systems
provable.
Test multi-agent AI systems before deployment. Prove the constraints that govern their work. Find the root cause when they fail in production.
For teams shipping agents in regulated, high-consequence environments
truvyx / evaluation.runPROVEN
evaluate.pytruvyx-sdk
evidence-chain.log
|The complete platform
One evidence loop for the full agent lifecycle.
From scenario authoring to signed production evidence, Truvyx gives engineering, safety, and compliance teams one place to understand what an AI system should do, what it did, and why.
/02
Constraint Engine
Encode temporal, resource, regulatory, safety, and fairness rules as formal constraints. Truvyx checks whether the scenario is solvable before an agent spends a run on an impossible task.
/03
Verifier Factory
Generate sandboxed verifier scripts that score feasibility, completeness, and optimality. Run the same evidence-based checks on every deployment instead of relying on a single model judge.
/04
Decomposition Designer
Design how work should be divided across agents, then compare the intended graph with what the system actually did. Find missing handoffs, redundant work, and unsafe information flow.
/05
Benchmark Registry
Maintain a version-controlled library of private and public scenarios. Compare systems against the same constraints with reproducible submissions and transparent leaderboards.
/06
Agentic RCA Engine
When a run fails, trace the causal chain to the smallest actionable fault. Classify it across Truvyx’s nine-type taxonomy, replay a counterfactual fix, and keep the finding connected to the original evidence.
From intent to proof
The platform is the writeup.
Every step keeps the reasoning visible: the operational context, the hard rules, the evaluator’s verdict, the causal chain, and the evidence a reviewer can actually inspect.
certainty labels
◆Mathematically verified
◆Rule-based
◆AI-assessed, clearly disclosed
01
Author
Turn a sentence into a machine-checkable scenario.
Describe the job in plain language. Scenario Studio expands it into typed agents, roles, dependencies, and success criteria you can version, fork, and replay.
02
Prove
Catch impossible tasks before an agent ever runs.
The Constraint Engine encodes temporal, resource, and regulatory rules, then a solver proves feasibility up front. No more burning a run on a task that was never satisfiable.
03
Diagnose
Know exactly why a run failed, not just that it did.
The RCA Engine traces the causal chain to a root fault, classifies it into one of nine fault types, and replays counterfactuals to confirm the smallest fix.
04
Defend
Guard production with continuous regression monitoring.
Wire monitoring into every deploy. Truvyx watches scores and emerging fault types, then alerts Slack, email, or PagerDuty when a regression appears.
Built for engineers
Wire evidence into CI/CD, SDKs, and the tools you already use.
Submit a run from CI, poll for a verdict, investigate a failure in your IDE or Slack, and gate your deploy on structured evidence. Truvyx works with any MCP client and supports hosted, hybrid, and on-premise deployments.
npm install @truvyx/evalresult.overallScoreMCP compatible
deploy-gate.ts
|Where proof matters
Where “probably correct” isn’t good enough.
Bring your own operational or regulatory rules. The Constraint Engine is designed for systems where a small error can become a material risk.
Healthcare
Clinical scheduling, resource allocation, and patient-safety constraints.
Financial services
Credit underwriting, fraud detection, and regulatory compliance.
Government
Audit selection, benefits eligibility, and legally defensible decisions.
Insurance
Claims settlement, underwriting risk, and regulatory solvency.
Logistics
Multi-hub routing, capacity planning, and cross-border compliance.
IT operations
Incident response, capacity planning, and SLA-bound decisions.
Why Truvyx
Proof, not just probability.
Most AI evaluation tools score outputs using another AI model. Truvyx uses that approach where judgment is genuinely required, then goes further for the rules that matter most: mathematical proof, causal diagnosis, and signed evidence.
Typical evalTruvyx
Probabilistic scoreProof where possible
Passed test casesConstraint satisfiability
Failure dashboardCausal root cause
Evaluation reportSigned audit trail
Questions teams ask
Straight answers, inspectable evidence.
How is Truvyx different from LLM-as-judge tools?
LLM-as-judge is useful for judgment calls, but it is probabilistic. Truvyx adds formal SMT-based verification for guardrails that need to be mathematically provable, and labels every finding by its actual certainty.
Does Truvyx only work before deployment?
No. It evaluates agents before deployment, investigates production behavior through root cause analysis, and monitors live systems for silent drift and recurring failure patterns.
What does formally verified mean?
For a defined constraint, a solver checks the full modeled input space rather than a handful of examples. The result is a proof about that rule, not a claim that common cases happened to pass.
Can regulated teams keep their data private?
Yes. Truvyx supports hosted, hybrid, and on-premise patterns, with private benchmarks and cryptographically signed audit records for examiner-ready evidence.
Your agents will make mistakes. Find them before they cost you.
Start free, bring your own agents, and get a root-cause report on your first failing run.
No credit card required.