Reliability Audits

Reliability audits
for AI coding agents.

Stress-test your AI coding agent before you trust it in production.

Give us your AI coding agent. We run it through repeated real software-engineering tasks in isolated environments, independently verify the results, and show you where it succeeds, fails, times out, and violates boundaries.

THE RELIABILITY PROBLEM //

Single-run success
≠ reliability.

Most AI agent benchmarks evaluate whether a model can solve a problem once. If the agent completes the task on a single rollout, it is marked as capable.

In production, however, autonomous software engineering agents execute across dozens of interdependent steps where non-deterministic tool choices, subtle syntax variations, and environmental timing differences compound into unpredictable failures.

Single-run evaluations hide trajectory divergence, infinite loop traps, and unexpected regressions. True reliability requires multi-rollout isolation and independent verification against objective out-of-band invariants.

SYSTEM ARCHITECTURE //

Sanre puts an AI agent through controlled, repeated executions and independently verifies what happened.

Evaluating an agent requires testing the full operating system: model + harness + tool permissions + environment state + execution trajectories.

01INPUT STREAM
AGENT RUNTIME

Model context, prompt instructions, reasoning traces, and tool dispatch requests.

STAGE RUNTIMEACTIVE
02HERMETIC
CONTROLLED SANDBOX

Isolated disposable container with clean initial commit and scoped tool proxies.

STAGE SANDBOX
035× PARALLEL
REPEATED EXECUTIONS

5× independent rollouts under identical starting states to expose stochastic variance.

STAGE ROLLOUTS
04OUT-OF-BAND
INDEPENDENT ORACLE

Out-of-band test suites, AST diff checks, and integrity scans unreachable by the agent.

STAGE ORACLES
05TELEMETRY
RELIABILITY EVIDENCE

Complete failure mode taxonomy, execution trajectories, and structured audit telemetry.

STAGE EVIDENCE

EMPIRICAL EVIDENCE //

Observed reliability across frontier models.

Across four 25-rollout experiments, Sanre evaluated autonomous coding agents on the same 5-task benchmark suite, producing 100 total rollouts.

80.0%
Gemini 3.7 Flash

Highest observed pass rate across 5× repeated rollouts (EXP 007).

64.0%
Gemini 3.1 Pro

Stochastic variance surfaced on async race condition tasks (EXP 004).

52.0%
Gemini 3.8 Flash

11 timeouts observed across 25 evaluations (EXP 006).

44.0%
Gemini 3.1 Flash-Lite

0 timeouts · 9 test tampering violations · 75.0% valid capability (EXP 005).

EVALUATION INTAKE //

Put your agent
under test.

We run controlled, multi-run reliability audits across capability, integrity, and execution trajectory before production deployment.

Turnaround: 3–5 business days · Complete telemetry, traces & failure catalog delivered.