AI Standards

Production AI should be observable, controlled, and owned.

These standards guide how Axionvex Tech designs, evaluates, deploys, and operates AI-enabled workflows. The exact controls vary by business consequence, data sensitivity, and user need.

Workflow suitability

· Is the work repetitive enough to justify systemization?
· Are inputs available and lawful to use?
· Can quality be evaluated?
· Is the expected outcome measurable?
· Are exception paths known?
· Can a person review uncertain or high-impact decisions?
· Would deterministic automation be more appropriate?

Model strategy

· Avoid unnecessary vendor lock-in
· Use task-appropriate models
· Route by quality, latency, cost, and data constraints
· Keep model configuration versioned
· Define fallback behavior
· Test changes before broad release

Context and retrieval

· Use approved sources
· Track source identity and freshness
· Apply role and tenant boundaries
· Limit context to what the task requires
· Cite sources where the user needs verification
· Define behavior when evidence is missing

Tool access

· Least privilege
· Explicit action allowlists
· Validated inputs and outputs
· Idempotency where applicable
· Rate-limit handling
· Secrets management
· Audit logging
· Human confirmation for high-impact actions

Human approval

· Financial action
· Customer commitment
· Legal or compliance judgment
· Sensitive communication
· Account or permission change
· Irreversible system update
· Low-confidence exception

Evaluation

· Expected behavior
· Failure taxonomy
· Representative cases
· Edge cases
· Release thresholds
· Regression schedule
· Human review method

Observability

· What input triggered the workflow?
· What context was used?
· What model and configuration ran?
· What tools were called?
· What rule or person approved the action?
· What failed or required retry?
· What did the workflow cost?
· What was the final outcome?

Security and privacy

· Data minimization
· Encryption in transit and at rest
· Role and tenant isolation
· Retention rules
· Secrets management
· Vendor data-use review
· Incident logging
· Access review
· Environment separation

Cost and latency

· Establish cost per completed workflow
· Track cost by model and stage
· Use caching where appropriate
· Route simple tasks to appropriate models
· Set timeouts and fallbacks
· Avoid repeated context transfer
· Review expensive failure loops

Ownership

· Technical owner
· Business owner
· Runbook
· Architecture record
· Integration inventory
· Model and prompt version history
· Incident process
· Change approval process
· Support agreement

Need to evaluate an existing AI workflow?

Request an AI Operations Review