BYOA.TOOLS

The Loop Closes

The usual objection to BYOA is that without transcripts, the learning loop breaks because privacy-preserving noise (Differential Privacy) destroys the signal over time.

The Mechanism

Binary Tree Counting

By structuring releases as a binary tree of prefix sums (Chan, Shi & Song 2011), noise variance is bounded by O(log³ T). We can continually observe without linear degradation.

The Threshold

Confident Detection

We trigger an update when a specific error code overcomes DP noise with a Bonferroni-corrected 99.9% confidence (3.6σ) to prevent false alarms across hundreds of telemetry cells.

The Result

Head vs. Tail

Detecting a 15% spike requires ~96 interactions per round. For high-volume head classes, that's 24 hours of traffic. For the long tail, the loop closes over weeks.

Required volume for 3.6σ detection (Δf=1)
Rounds (T) Budget (ε) Latency (L) Spike (Δp) Required Vol/Round
81921.02415%96
81921.0415%563
81920.52415%191
327681.02415%118
10241.01020%114
Live Simulation

Lab Verification

We ran an automated test harness wrapping the Sierra Research tau-bench enterprise agent environment. By running an external LLM agent through our CABP Gateway middleware for 53 sequential task iterations, the architecture cleanly intercepted unstructured API failures (e.g. "Error: order not found") and successfully emitted exactly 51 structured, DP-safe SERF events into an OpenTelemetry sink—demonstrating the pipeline closes the loop without relying on transcripts.

Monte Carlo

Stochastic Resonance

A Monte Carlo simulation proved that DP noise actually helps detect sub-threshold long-tail errors. A structurally invisible error occurring 80 times against a threshold of 96 would never fire deterministically. But with DP noise injected, it constructively interfered and tripped the threshold 11 times in 90 days. In a continual release system, the noise makes the long tail visible.

Conclusion: The AX Contract operates securely without transcripts. The math proves that aggregate telemetry is sufficient at realistic enterprise scales.