# AI Evaluation & Observability — Alector Lab Engineering Specification

Canonical URL: https://alectorlab.com/services/ai-evaluation-observability
Raw Markdown: https://alectorlab.com/services/ai-evaluation-observability/raw
Last Updated: 2026-09-10
Provider: Alector Lab – Enabling Technologies Ltd (https://alectorlab.com)
Contact: alectorlab@gmail.com

## Summary
Make AI quality measurable before launch and observable as models, data and workflows change.

## Engineering Overview
Evaluation defines whether an AI workflow is good enough for its intended use. Observability shows what it does after release. Together they connect offline tests, production traces and incident review to practical release decisions.

### Illustrative Scope
For a knowledge assistant, separate retrieval failures from unsupported answers and access-control errors. A release candidate is compared with a baseline using versioned tasks. Human review checks ambiguous cases and calibrates automated scoring; a single average must not hide serious failure classes.

## Target Audience
- Teams moving AI into production
- Products with costly or regulated errors
- Engineering teams changing models or prompts

## Supported Workflows
- Task-level quality evaluation
- Hallucination and retrieval testing
- Agent trace review
- Latency, cost and drift monitoring

## Required Inputs
- Representative tasks and expected outcomes
- Risk and severity definitions
- Production traces where available
- Release and escalation policies

## Engineering Deliverables & Outputs
- Versioned evaluation dataset
- Automated regression suite
- Quality and operations dashboard
- Release gates and incident playbook

## Delivery Stages
1. Define the error taxonomy
2. Build representative test sets
3. Instrument traces and metrics
4. Set release thresholds
5. Review drift and regressions

## Acceptance Criteria
Deliver a maintained test set, documented scoring rules and a repeatable release report. Define alert ownership and review intervals. Test data must be access-controlled and representative; passing a benchmark does not certify a system safe for every future input.

## Operating Boundaries
- **Integrations:** Connect versioned task fixtures, application traces and release tooling. Redact sensitive records and document components that cannot be observed.
- **Deployment:** Keep evaluation data within its permitted boundary. Separate offline release checks from production telemetry and specify retention and alert ownership.
- **Human Review & Governance:** Calibrate automated scorers against human judgements. Report errors by severity and give an accountable owner control over release exceptions.

## Operating Limitations & Safety Boundaries
- A single aggregate score can hide critical failures
- Evaluation sets need ongoing maintenance
- Automated judges require calibration against human review

## Common Technical Question
**Q: Can you evaluate a system built by another team?**
A: Yes, subject to sufficient access to its intended tasks, outputs and operating constraints. A black-box assessment can test behavior; deeper diagnosis usually needs traces and component visibility. The scope should state what could not be observed.