Define acceptable outcomes
Build representative test sets, explicit scoring criteria, regression checks, and thresholds connected to the business workflow.
The production AI playbook
A prototype proves possibility. Production demands evidence.
Use this framework to evaluate an AI agent, RAG platform, or intelligent workflow before customers and operations depend on it.
The central question is not whether the model can produce a good answer. It is whether the complete system can produce acceptable outcomes repeatedly, safely, and at a known cost.
5 layersEvaluation, reliability, security, cost, oversight
1 systemModel behavior and software behavior tested together
AlwaysEvidence before confidence
The five production gates
What must be true before the system earns trust.
These gates apply differently by use case, but none can be replaced by a successful demo or a handful of manually selected examples.
Make failure visible
Trace inputs, retrieval, tool calls, model outputs, latency, cost, retries, and the final action taken by the system.
Constrain data and authority
Enforce identity, least privilege, data boundaries, prompt-injection defenses, tool restrictions, and audit trails.
Know the cost of a useful outcome
Measure total workflow cost, not only token price. Include retries, retrieval, tools, infrastructure, review, and failure.
Keep humans where judgment matters
Define approval points, escalation paths, reversible actions, and safe fallbacks for uncertainty or system degradation.
Treat prompts as one component
The production system also includes data, retrieval, tools, services, queues, permissions, tests, monitoring, and people.
A practical review sequence
Move from confidence by impression to confidence by evidence.
Map the complete workflow
Document every input, decision, retrieval step, tool call, side effect, human handoff, and failure path.
Create the evaluation baseline
Capture representative cases, expected behavior, unacceptable behavior, and the current system’s measurable performance.
Add operating controls
Instrument the system, restrict authority, introduce fallbacks, set budgets, and make risky actions reviewable.
Release progressively
Start with limited scope, compare production evidence against thresholds, and expand only when the system earns it.
Start with clarity
Want this framework applied to your system?
Bring your architecture, workflow, or prototype. We will identify the most important production gap and the evidence needed to close it.