AI Governance · Evolved from Model Evaluation
Model & Agent Evaluation Framework
Structured evaluation of model and agent behavior against curated eval sets covering success cases, safety cases, adversarial cases, and regression cases — with defined thresholds and rollback authority.
Executive Guidance
How to hold this framework as a leader
Model evaluation is the discipline that lets a leader make a defensible statement about model behavior. Without it, every claim about model quality is opinion. With it, the organization can make and evidence release, procurement, and audit decisions.
The executive commitment is threefold: evals are versioned, evals are owned by named people, and evals cover safety and adversarial cases, not just success cases. The absence of any one of these is the absence of governance.
When to Apply
- Before production release of any model-driven capability
- Continuously across model, prompt, or context changes
- In response to observed customer harm or regulator inquiry
AI-Era Notes
Evals are not a one-time test; they are a first-class product artifact with version control, ownership, and review cadence equivalent to code.
Key Trade-off
Rigorous but expensive; under-investment produces unmeasured regressions and audit exposure.
Operating Sequence
The order in which to install the framework
- 01Define the eval set for each model-driven capability: success, safety, adversarial, regression.
- 02Establish pass thresholds and rollback authority in advance of release.
- 03Run the eval set on every material change — model, prompt, data, or context.
- 04Version the eval set as a first-class artifact with review cadence.
- 05Publish eval outcomes to a durable log accessible to leadership.
Key Artifacts
The documents this framework produces
Eval Set
Curated inputs and expected behavior for a model-driven capability.
Owner: Product + AI/ML
Eval Runbook
How evals are executed, scored, and interpreted.
Owner: Engineering + AI/ML
Eval Outcome Log
Durable record of eval runs and their consequences.
Owner: Product Operations
Operating Checklist
What "good" looks like when installed
Coverage
- Success cases represent the top real-world usage patterns.
- Safety cases include harmful, sensitive, and out-of-scope inputs.
- Adversarial cases probe prompt injection and manipulation.
- Regression cases pin behavior that customers already depend on.
Governance
- Pass thresholds are pre-committed and numeric.
- Rollback authority is named and reachable.
- Evals are run on every model, prompt, or data change.
Common Antipatterns
- Evals maintained by a single individual without redundancy
- No adversarial or safety cases in the eval set
Boardroom Questions
- For each customer-facing model, can we produce the current eval report?
- Who has the authority to roll back an AI capability, and how quickly?
- What safety or adversarial case have we chosen not to cover, and why?