AI Agent Governance and Evaluation for Production Control

Governance is the operating design around the agent

Governance should not be a policy document that sits outside the implementation. It should be expressed in the way the system obtains context, calls tools, requests approval, records evidence, handles uncertainty, and changes over time.

An agent can produce a reasonable answer and still be unsafe or operationally incomplete. It may have used information the requester should not access, selected an action outside its authority, missed a required approval, or failed to confirm that a downstream write succeeded.

Arcta governs the full workflow outcome. Model quality matters, but it is one part of a system that also includes identity, process, tools, knowledge, people, and operating ownership.

The governance model

Ownership

Each workflow has an accountable business owner and a technical operating owner. They define the intended outcome, risk boundary, approval roles, acceptable performance, and response to incidents or sustained degradation.

Data and knowledge access

The agent receives only the context appropriate to its task, tenant, user, and role. Sources remain attributable, and sensitive material can be isolated. Access to propose or approve knowledge changes is separate from access to use that knowledge.

Action permissions

Tools expose narrow actions rather than unrestricted system access. The workflow distinguishes reading, drafting, recommending, requesting approval, and executing. High-consequence actions can require additional evidence or a named human decision.

Approval and escalation

Approval points are explicit workflow states. Reviewers receive the relevant evidence, proposed action, unresolved issues, and available decisions. Missing context, novelty, policy conflict, or uncertainty can trigger escalation to an accountable role.

Evidence and auditability

The system records the sources, workflow state, evaluations, approvals, and tool outcomes needed to understand what occurred. Evidence retention should be appropriate to the risk and privacy requirements of the workflow, not an indiscriminate capture of every internal token.

Change control

Models, knowledge, prompts, tools, and workflow logic can all change behavior. Each material change is versioned, evaluated, reviewed, and released according to its risk. Rollback and incident ownership are part of production readiness.

Evaluation at the workflow level

An agent should not be evaluated only on whether its response resembles an expected paragraph. The system needs to complete the right work, use acceptable evidence, obey authority boundaries, and handle failure correctly.

Crucible organizes evaluation across several dimensions:

• Outcome: Did the workflow reach the correct completion or escalation state?
• Evidence: Were the required and permitted sources used and retained?
• Decision: Were company rules, precedents, and exceptions applied appropriately?
• Action: Were tool calls valid, authorized, and confirmed?
• Control: Did required approvals and escalation conditions occur?
• Efficiency: How much human review, correction, and rework remained?
• Robustness: What happened when data, tools, or policies were missing or conflicting?

These dimensions connect technical evaluation to operational accountability.

Building the evaluation set

The initial set should include ordinary representative cases, known edge cases, previously observed failures, high-consequence decisions, missing-information scenarios, policy conflicts, and external-system errors. Where subjective judgment matters, accepted examples and reviewer reasoning can define what good work looks like.

Production operation expands the set. When a person corrects an outcome or an unexpected case escalates, Refinery captures the learning. If approved, the case becomes a precedent, rule, or regression evaluation in Canon.

This prevents the evaluation suite from remaining a static launch artifact. It grows with the company’s operating experience.

Observability and failure diagnosis

Monitoring should show more than whether the service is available. The operating team needs to see workflow completion, exception rates, review effort, common escalation reasons, failed tool actions, and changes in quality or business value.

When performance degrades, Arcta diagnoses the responsible layer. The issue may be unavailable knowledge, incorrect retrieval, ambiguous policy, model behavior, workflow logic, permission configuration, integration failure, or a changed underlying process.

Clear diagnosis prevents prompt changes from becoming the default response to every problem.

Graduated autonomy

Autonomy is assigned per workflow action, not declared for the agent as a whole. An agent can automatically gather permitted context and prepare work while requiring approval to commit a record or communicate externally. Another low-risk step may eventually operate without review after sustained evidence supports it.

Crucible provides the evaluation record for those decisions. The business owner can expand authority when quality and control are demonstrated, or reduce it when failures, risk, or process changes require a tighter boundary.

This is how governance enables useful production operation: it makes responsibility explicit enough that the organization can safely delegate more work over time.