Bot Velocity Engineering
Evaluation as Infrastructure
Most AI systems treat evaluation as a one-time experiment. Enterprise systems require continuous verification.
Why Evaluation Cannot Be Manual
In production environments:
- Prompts evolve
- Models change versions
- Tool integrations shift
- Data distributions drift
Manual spot-checking fails under scale.
Evaluation must be automated and governed.
Dataset-Driven Execution
A production-grade evaluation model looks like:
Baseline Dataset ↓ Dispatch N Executions ↓ Collect Outputs ↓ Score & Compare ↓ Gate Promotion
Every evaluation run operates on real execution artifacts.
Business Impact
Without evaluation infrastructure:
- Silent regressions damage customer trust
- Flaky behavior erodes confidence
- Compliance violations go unnoticed
With evaluation governance:
- Rollouts become controlled
- Model updates become measurable
- Engineering teams gain auditability
Regression Detection
Regression is not just accuracy loss.
It includes:
- Policy violations
- Latency spikes
- Token overconsumption
- Increased tool call depth
Enterprise AI systems must track all of these dimensions.
CI Gating for AI
Traditional CI blocks code with failing tests.
AI CI must block:
- Scoring below threshold
- Policy violations
- Excessive flakiness
This creates a structured deployment lifecycle.
Executive Summary
Evaluation is not QA. It is a control mechanism.
Embedding evaluation into orchestration transforms AI from experimentation into governed infrastructure.