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AI Infrastructure

Is your business ready for AI agents? A readiness audit.

Before adding agents, check whether the process, data, permissions and failure policy are ready.

An agent cannot repair an undefined process. It can only hide the uncertainty for a while.

Process readiness

Can the team explain the workflow, inputs, permitted actions and successful output? If experienced staff handle every case differently, document the decision before asking software to reproduce it.

Data readiness

Identify the systems of record, missing fields, duplicated records and information the agent is allowed to access. Useful context must also be current, attributable and retrievable.

Action readiness

List every tool the agent may use and the permissions each action requires. Reading a CRM note and issuing a refund are different risk classes. Use least privilege and explicit approval gates.

Evaluation readiness

Collect realistic test cases, including ambiguous inputs and known failures. Define acceptable behaviour before choosing a model. Without an evaluation set, improvement is guesswork.

Operational readiness

Decide who receives alerts, reviews traces, handles exceptions and can disable the workflow. Agents are production systems; ownership after launch matters as much as the demonstration.

If these five areas are clear, an agent build may be sensible. If they are not, the readiness work is the project.