Module 6 — Evaluation, Quality and Observability
What this module is about
You've built the system: the ontology, the agents, the workflows, the tools, the context packages. Everything is designed, structured, and deployed. But here's the question that separates a prototype from a production system: how do you know it's working?
Not "does it feel like it's working" or "did the demo go well" — but genuinely, measurably, provably working. Working today, working tomorrow, working when the model gets updated, working when traffic doubles, working when a new edge case appears that nobody anticipated.
In traditional software, you have unit tests, integration tests, performance monitors, error logs, and dashboards. You know exactly when something breaks, where it broke, and how to fix it. In AI systems, the challenge is fundamentally harder: the system can "break" in ways that are invisible. It can quietly start giving worse answers without any error code, any crash, any red alert.
This module teaches you to build the measurement infrastructure that makes quality visible, degradation detectable, and improvement provable. If the previous modules built the airplane, this module builds the cockpit instruments — the gauges, the alarms, and the checklists that keep the flight safe.
Learning outcomes
By the end of this module, you will be able to:
- Design quality metrics that connect business outcomes to measurable technical signals — both objective (accuracy, latency, cost) and semantic (coherence, policy adherence, trust)
- Build evaluation suites with golden datasets, rubric-based scoring, and regression gates that catch problems before they reach users
- Implement operational monitoring with stage-level traces, decision event logging, alert thresholds, and incident triage processes
- Close the learning loop by turning production observations into evaluation improvements and system refinements
Module map
| # | Content | Type | Estimated time |
|---|---|---|---|
| 1 | Metric definition — connecting quality to business value | Lesson | 90 min |
| 2 | Eval suite construction — building repeatable quality checks | Lesson | 90 min |
| 3 | Operational monitoring — detecting and responding to problems | Lesson | 90 min |
| A | SSA quality suite | Application | 3 hours |
| B | Regression drill | Application | 2 hours |
| — | Assessment | Evaluation | 30 min |
What you will build
- A metric contract mapping business KPIs to quality indicators to technical signals, with thresholds and ownership
- An evaluation suite of 50+ test cases covering happy path, edge cases, and adversarial scenarios with rubric-based scoring
- An observability playbook with trace architecture, alert rules, incident runbook, and triage process
- A regression drill report demonstrating your ability to detect, diagnose, and mitigate quality degradation after a controlled change
Prerequisites
- Module 5 completed (you need context packages and structural prompts to evaluate)
- All prior module artifacts (ontology, agent architecture, workflows, context packages) — these are the system you will be measuring
Estimated time
10-12 hours total (lessons + applications + assessment)