AI AgentContinuous Improvement.
A practical framework for observing, evaluating, improving, and connecting production AI agents to business value.
Is it working?
Reliability / Operations
Is it good?
Quality / Evaluation
Is it creating value?
Outcomes / Economics
Evidence for one does not automatically answer the others.
From production signals
to controlled improvement.
Where failure taxonomy, versioned changes and regression evaluation became part of the improvement loop.
Diagnose → Prioritize → Hypothesize → Intervene → Validate → Release → Production
The goal isn't to make the LLM deterministic. It's to make the improvement process more measurable, controlled and repeatable.
A signal is not a diagnosis.
Control the system
around the model.
Can you trust the evaluator?
An evaluator can look reliable in aggregate while failing exactly where it matters.
90% overall ≠ 90% where it matters
Did the change actually improve the system?
Production failures become regression cases. Changes become hypotheses. Evidence determines the release.
53.3% → 86.7%
How far can the evidence support the claim?
Better model behavior does not automatically mean better operations or business outcomes.
Quality → Operations → Business → Financial
Does the controlled system still make economic sense?
The cost of evaluation, supervision and control belongs inside the business case for automation.
Control cost ∈ business case
Built from production.
Expanded through research.
The framework was subsequently applied in an enterprise production deployment, moving from a bounded system to higher-volume agent operations — and changing the problem from improving one model interaction to controlling an operating system around AI.
AACI v0.1 distinguishes what was observed, what was designed,
and what remains a hypothesis.
Running AI agents
in production?
If your team is struggling with evaluation, regressions, production quality, control, or understanding whether AI is actually creating value — let's talk.
no packages.
no résumé.
just the problem.