Evaluation Framework
Quality gates are infrastructure, not afterthoughts.
Go beyond observability to pre-deployment quality gates.
Problem Statement
Most automation platforms give you observability—logs, traces, dashboards. But observability tells you what happened after failure. Evaluation tells you what will fail before deployment.
LangSmith gives you observability. Bot Velocity gives you quality gates.
Each dimension scores independently and produces actionable insights.
Did it produce the right answer?
Evaluation runs in CI/CD with status endpoints and gates.
Built-in evaluation infrastructure compared to reactive monitoring and manual testing approaches.
Evaluation Built-In. Not Bolted On.
Most automation and agent platforms focus on execution and observability. They help teams run workflows and monitor results after deployment.
Bot Velocity takes a different approach.
Evaluation is embedded directly into orchestration — not layered on afterward.
RPA platforms specialize in deterministic automation. While execution is reliable, quality validation is typically external:
Automation executes — but governance is not inherent.
Agent frameworks prioritize flexibility and multi-step reasoning. They enable experimentation and rapid iteration.
However:
These systems are powerful — but production governance depends on additional engineering effort.
Observability platforms provide trace visibility and performance monitoring.
They answer: “What happened?”
They do not answer: “Should this be deployed?”
Monitoring is reactive. Evaluation must be proactive.
Many teams build internal evaluation pipelines:
This increases operational complexity and long-term maintenance burden.
Bot Velocity unifies:
Evaluation is part of the control plane.
Deployment decisions are based on structured quality signals — not post-release diagnostics.
Observability reacts to failure.
Evaluation prevents instability from reaching production.
Bot Velocity treats evaluation as infrastructure.
Create datasets, run evaluations, and enforce CI gates.
Define structured evaluation datasets that include representative inputs and expected outcomes. Organize them by folder or domain to mirror production workflows.
Run the selected workflow version against the dataset. Each execution is measured across defined quality dimensions, including accuracy, policy adherence, performance, and stability.
Analyze results through the Orchestrator interface. Review overall pass scores, dimension-specific insights, regression indicators, and CI status to determine readiness for promotion.
Establish release criteria that define acceptable quality thresholds. Prevent regressions, restrict policy violations, and monitor output stability before allowing deployment.
Connect evaluation status to your CI/CD process. Automatically trigger evaluations on new releases and block deployment when quality gates are not satisfied.
Put quality gates in front of every deployment.
