This document captures a consolidated, practical understanding of AI evaluations (evals) based on our discussions and the reference material you shared. It is intended as a mental model + system design reference for building, running, and operationalizing evals in modern, agentic AI systems.
AI evals are structured mechanisms for measuring, validating, and improving the behavior of AI systems—especially LLM‑powered applications and agents—against explicit goals.
At their core, evals answer:
- Did the system do the right thing?
- How well did it do it?
- Is it improving or regressing over time?
Evals are not just tests. They are feedback instruments that sit inside a continuous learning loop.
Key characteristics:
- Quantitative and/or qualitative
- Automated or human‑in‑the‑loop
- Offline (pre‑deploy) and online (post‑deploy)
- Model‑centric and system‑centric
The aspiration of evals is high‑agency AI systems:
Systems that can learn, self‑correct, and improve autonomously while remaining safe, grounded, and aligned.
Evals enable:
- Trust — confidence in outputs, safety, and compliance
- Velocity — faster iteration without fear of regressions
- Autonomy — agents that adapt their behavior based on feedback
- Accountability — explicit signals tied to product goals
- Comparability — informed choices between models, prompts, tools, and architectures
Long‑term aspiration:
- Evals become a control plane, not a reporting artifact
- Systems dynamically select models, tools, or strategies based on eval outcomes
- Human review is focused on edge cases, not routine validation
Evals are not a single component—they span the lifecycle.
Used during:
- Prompt engineering
- Model selection
- RAG tuning
- Agent design
Examples:
- Benchmarking different prompts
- Testing retrieval quality on golden datasets
- Safety and policy validation
Evals increasingly behave like unit tests + integration tests for AI:
- Triggered on PRs
- Run in pipelines
- Gate deployments
Signals:
- Pass/fail thresholds
- Regression detection
- Composite quality scores
Post‑deployment evals monitor real behavior:
- Live traffic sampling
- Shadow evals
- Drift detection
- User feedback loops
This is where evals transition from quality assurance → operational intelligence.
| Phase | Purpose |
|---|---|
| Design | Validate assumptions and UX intent |
| Development | Iterate prompts, tools, and flows |
| Pre‑Deploy | Catch regressions, safety issues |
| Deploy | Confidence gating |
| Post‑Deploy | Drift detection, learning loops |
| Continuous | Autonomous optimization |
Evals should be:
- Cheap and frequent early
- Representative and precise later
- Test prompts / conversations
- Context (documents, tools, memory)
- Expected outputs or scoring criteria
- Run model / agent
- Capture outputs, traces, tool calls
- Automatic metrics (scores, labels)
- Model‑based judges
- Human review (as needed)
- Per‑metric scores
- Weighted composite scores
- Pass/fail thresholds
- Block deployment
- Select alternate model
- Trigger self‑correction
- Update prompts, skills, or policies
Measures how well the response is formed and understood.
- Fluency
- Coherence
- Similarity
- Relevance
- Response Completeness
Measures whether responses are based on provided sources.
- Groundedness
- GroundednessPro
- Retrieval
- Ungrounded Attributes
Critical for RAG and enterprise systems.
Measures whether the system achieved the user’s goal.
- Intent Resolution
- Task Success / ECI
- F1 / Exact Match (where applicable)
Measures harmful or disallowed content.
- Violence
- Sexual
- Self‑Harm
- Hate & Unfairness
- Indirect Attacks
- Protected Material
- Code Vulnerability
These often operate as hard gates.
Often used for summarization, translation, or generation comparison.
- BLEU
- ROUGE
- METEOR
- GLEU
Useful but not sufficient alone.
Single metrics are rarely enough.
Modern systems use:
- Weighted metric bundles
- Context‑specific thresholds
- Scenario‑based scoring
Example:
- Retrieval quality weighted higher for RAG
- Safety metrics always blocking
- Fluency de‑prioritized for internal tools
Composite scores are typically external to individual evaluators and live in an orchestration or policy layer.
Agent evals expand beyond text:
- Tool selection correctness
- Planning quality
- Step ordering
- Error recovery
- Self‑reflection quality
Key insight:
You don’t just eval outputs — you eval decisions.
This enables:
- Self‑correction loops
- Strategy switching
- Skill refinement
Humans are still essential for:
- Defining intent
- Labeling gold datasets
- Reviewing edge cases
- Training evaluators
But the trajectory is clear:
Humans move from graders → designers of grading systems.
- Evals are first‑class citizens, not afterthoughts
- Separate signal generation from decision policy
- Optimize for iteration speed early
- Treat eval datasets as versioned assets
- Expect metrics to evolve
Primary documents referenced:
-
Anthropic — Demystifying Evals for AI Agents https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents
-
Azure AI Evaluators (internal + public concepts)
-
Agent Skills Specification https://agentskills.io/specification
-
VS Code Copilot Agent Skills https://code.visualstudio.com/docs/copilot/customization/agent-skills
-
Azure Deploy / Ralph Architecture (user‑provided) https://github.com/spboyer/azure-deploy/blob/main/docs/ralph_architecture.md
Think of evals as:
The nervous system of AI products
They sense, score, and signal—so systems can act with speed, safety, and intent.
If you want, next we can:
- Turn this into a one‑page executive view
- Map evals to a reference architecture
- Design an eval control plane for agents
- Create a metric → decision matrix