Agent Deployment Readiness Assessment
15 questions to answer before your AI agent goes live.
Deploying an AI agent to production is a governance decision, not just a technical one. This assessment helps engineering, security, and compliance teams systematically evaluate whether an agent is ready — covering identity, testing, monitoring, and rollback before the first request is served.
Does the agent have a unique, registered identity?
The agent should be registered in your AI inventory with a distinct identity, not sharing credentials with other agents or using a generic service account.
Are permissions scoped to the minimum required?
Review every tool, API, and data source the agent can access. Remove anything not strictly needed for the agent's intended task.
Is there an owner accountable for this agent?
A named individual or team should be responsible for the agent's behavior, compliance, and lifecycle — not just the team that built it.
Has the agent passed an evaluation pipeline?
The agent should be tested against a representative dataset covering normal operations, edge cases, adversarial inputs, and compliance-sensitive scenarios.
Has adversarial testing been performed?
Red teaming or prompt fuzzing should have probed the agent for injection vulnerabilities, policy bypasses, and unexpected behaviors.
Are regression tests in place for future changes?
A baseline of passing test cases exists so that model updates, prompt changes, or tool modifications can be validated before redeployment.
Are governance policies bound to this agent?
The agent has specific policy rules defining what it can and cannot do — enforced at runtime, not just documented.
Has the agent been classified by risk level?
The agent's risk classification is documented and determines the governance controls, monitoring intensity, and approval requirements that apply.
Has the required approval workflow been completed?
All required sign-offs — security review, compliance check, business approval — are documented and recorded before deployment.
Is tracing and logging configured?
Every agent action — tool calls, model interactions, policy evaluations — is captured in traces and logs that feed into your monitoring stack.
Are alerts configured for anomalies and violations?
Thresholds are set for error rates, token usage spikes, policy violations, and behavioral anomalies — with alerts routed to the right team.
Is cost monitoring in place?
Token consumption and API costs are tracked per-agent with budgets and alerts to prevent runaway spend.
Can the agent be rolled back to a previous version?
The deployment pipeline supports fast rollback to the last known-good version of the agent's model, prompt, tools, and configuration.
Is there a kill switch accessible to operations?
The agent can be immediately suspended without redeploying. The kill switch is documented, accessible, and has been tested.
Does an incident response playbook cover this agent?
The team knows who to contact, how to contain the agent, how to preserve evidence, and how to communicate if something goes wrong.
See how Prefactor manages agent deployment governance
Prefactor gives enterprises runtime governance, observability, and control over every AI agent in production.
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