The reliability operating system for AI.
Software got CI, observability and SRE. AI is getting agents that move money, read medical records and call other agents — with almost none of that discipline. SENTINEL-X is building it.
The end state
What's real today, what's next, and where we're heading.
Shipped means you can use it in the product now. Everything else is labelled honestly.
Shipped
Working in the product todayAI Onramp
Connect 10 kinds of AI system with a live handshake, stakes, guardrails and the frameworks in scope.
Test Studio
Profile-driven test generation across 104 dimensions, with import, export and coverage views.
Five AI roles
Judge panels, comparer, rewriter, adaptive attacker and generator — each model chosen per run and recorded.
Release gate & evidence pack
Trust score, severity blocking, guardrail checks, human sign-off and an auditor-ready export.
CI gate & API
Fail the build when the gate fails. JUnit, CSV and JSON exports. Bearer-token API.
Schedules & posture
Hourly to weekly re-tests, fleet risk posture and a searchable conversation archive.
Next
In designSignals ingestion
OpenTelemetry GenAI spans streamed in, so groundedness and tool arguments are proven from real traffic.
Trace replay
Re-run any failed conversation against a new model or prompt and diff the outcome step by step.
Drift intelligence
Spot when behaviour moves between runs before a threshold is ever crossed.
Horizon
Where SENTINEL-X is goingAutonomous QE agents
Agents that watch your AI fleet, decide what to test next, and open the fix with the evidence attached.
Reliability graph
Every system, model, tool, dataset and regulation linked, so one change shows its blast radius instantly.
Self-healing recommendations
From a failing test to a suggested prompt, guardrail or retrieval fix — verified by re-running the suite.
Six ideas the platform is built on.
AI systems are composable
One assistant can be a chatbot, a retriever, a tool user and an agent at once. Testing follows the composition.
Testing is capability-driven
Add memory, and memory-poisoning tests appear. Remove tools, and tool tests disappear.
Visibility sets what can be proven
Outside-in, Signals or Full trace — the platform is explicit about the difference.
Coverage is always transparent
What was tested, what was not, and why. No silent gaps.
Quality contracts define expectations
Guardrails are thresholds you agree up front, not adjectives in a slide.
Evidence beats assurance
A release decision should point at runs, traces and sign-offs — not opinions.
Shape what gets built next.
If you run AI in production and these problems sound familiar, I would like to hear what you need next.