01 AI SRE evaluation, without the theatre

Can your agent find
the real failure?

Investigate a production incident, write the RCA, and watch a transparent evaluator measure root cause, evidence, remediation, and red-herring resistance.

Enter live incident ↘ NO LLM JUDGE Every point is inspectable.
04deterministic scenarios
05scoring dimensions
100%browser-side evaluation
0hidden judge prompts

02 Live investigation chamber

Follow the evidence.
Then prove your diagnosis.

Choose an incident. Inspect the alert, metrics, logs, traces, and runbook. Write an RCA—or load an example—and run the exact scoring logic used by the Python package.

IFG-LOADING
SEV—
STATUSINVESTIGATING
SERVICE

Loading incident bundle…

Preparing deterministic evidence.

USER IMPACT—
EVIDENCE ARRAY— ITEMS
LIVE FIXTURE
CANDIDATE RCAWRITE OR LOAD A REPORT
0 WORDS

No report scored yet.

OVERALL — AWAITING RCA
Root cause 42%—
Evidence 34%—
Remediation 14%—
Service ID 10%—
Red-herring resistance—
MISSING EVIDENCE
Score a report to inspect gaps.
RED HERRINGS TRIGGERED
Score a report to inspect penalties.
Reveal the ground truth only after investigating ↘
ACTUAL FAILURE

—

03 The scoring engine

Not a vibe check.
A measurable contract.

01

Generate

Every scenario creates repeatable alerts, metrics, logs, traces, runbooks, noise, and hidden truth from a fixed seed.

02

Investigate

The candidate receives the same evidence an operator would inspect. Misleading but plausible clues are included on purpose.

03

Score

Terms and evidence signals map to explicit weights. Blaming a red herring removes points. No opaque evaluator decides the outcome.

04

Improve

Missing signals and remediation terms turn a single score into a useful explanation of what the RCA failed to prove.