Silent degradation
No stack trace. No alert. Agents drift, miss edge cases, and ship broken outputs — without surfacing a single error.
CI/CD for AI agents
The control plane for self-improving AI agents.
Every release makes your agents sharper — without you writing a single test. Relios catches silent drift, turns real failures into labeled evals, and replays every fix against production traffic. You ship. It improves from there. Automatically.
The problem
AI agents fail silently.
Traditional software fails loudly — stack traces, error logs, alerts. AI agents fail through distributional drift. User behavior shifts, APIs evolve, edge cases accumulate. Nothing signals degradation. Systems look healthy while performance quietly decays.
Visibility illusion
Dashboards show green. Users see breakage. The gap between observed health and real quality compounds every week.
Manual remediation
Fixing drift means engineers hand-writing evals, replaying traces, and babysitting deploys. It doesn't scale past one team.
How it works
You ship the release. Relios makes the next one better.
A closed loop that runs continuously — turning every production trace into training signal, every failure into a regression test, and every fix into a validated deploy. No human in the loop.
Observe Capture decision traces. Detect drift.
Learn Turn failures into structured evals.
Harden Replay, validate, deploy safely.
01 OBSERVE
Sees the failure before you do.
Capture every decision trace and pinpoint exactly where reasoning broke — prompt, retrieval, tool call, or policy. Drift gets surfaced, not swept under a dashboard.
Full-trace capture Causal attribution Drift detection
02 LEARN
Writes its own tests.
Every real failure becomes a labeled eval — automatically. Coverage grows with production traffic, not with your team. No engineer writes a single regression test.
Synthetic evals Pattern clustering Zero-touch coverage
03 HARDEN
Ships only what survives.
Candidate fixes get replayed against real historical traffic. Only regression-safe improvements go live. Bad deploys roll themselves back — before anyone files a ticket.
Shadow replay Regression gates Auto-rollback
Why Relios
Not surface-level. Research-grade.
Built on cutting-edge research in self-improving AI systems, Relios is the only system in its class that actually understands how your agent thinks. Instead of flagging a bad output, it localizes the failure to the step that caused it — and proposes a targeted fix.
PROMPT
Identifies drifted instructions.
When the problem is a prompt, Relios pinpoints which sentence, example, or constraint broke — and suggests the targeted rewrite.
TOOL EXECUTION
Catches bad tool calls at the source.
Parameters, retrieval, parsing, side-effects — every tool call is traced. Relios attributes failure to the specific call and the specific argument that caused it.
BRANCHES TAKEN
Models the agent's decision tree.
When the agent picks the wrong path, Relios knows. It traces the branch points in your agent's reasoning and flags the exact decision that went sideways.
Surface-level monitoring tells you something broke. Relios tells you which step broke — and exactly how to fix it.
Deployment
Two ways to start.
Relios works with the trace stack you already have — or ships with everything you need out of the box.
OPTION 1
Connect your observability.
Already running traces through LangSmith, Arize, Datadog, or your own pipeline? Relios plugs in. No migration, no rip-and-replace. Keep what you have — gain the closed loop.
LangSmith· Arize· Datadog· OTel· Your own
OPTION 2
Batteries included.
Start fresh. Relios ships as a single clean stack — trace capture, causal attribution, synthetic evals, replay, and deploy gates. One contract. No glue code.
Traces· Evals· Replay· Gates· Rollback
Get early access
Bring your agents from demo to dependable.
We're working closely with a small cohort of design partners. If you're running agents in production — or trying to — we'd love to talk.