Is your business ready for AI agents? A readiness checklist

What is an AI agent, in business terms?

An AI agent is software built on an AI model that can plan and carry out several steps of a task, not just answer a question. It might read an incoming supplier invoice, match it to the purchase order, draft a query for a mismatch and update a record. McKinsey describes agents as systems "capable of acting in the real world, planning and executing multiple steps in a workflow" (McKinsey, November 2025).

That ability to act is what makes agents useful, and also what makes readiness matter. An assistant that suggests a reply is low risk. An agent that sends the reply, changes a record or moves money needs the same controls you would expect around a new employee, and some extra ones.

Why is readiness the question to ask in 2026?

Interest is high, but results are early. In McKinsey's November 2025 survey, 62% of respondents said their organisations were at least experimenting with AI agents, but in any given business function no more than 10% said they were scaling them (McKinsey). Gartner has predicted that over 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls (Gartner, 25 June 2025).

Standards bodies are catching up. On 17 February 2026 the US National Institute of Standards and Technology (NIST) launched an AI Agent Standards Initiative focused on agents that are interoperable and secure (NIST). On 23 February 2026 COSO, the body behind the internal control framework most auditors use, published Achieving Effective Internal Control Over Generative AI, a practical guide to governing generative AI within that framework (COSO; COSO press release, 23 February 2026). The message from both is the same: agents need governance designed in, not added later.

What is on the AI agent readiness checklist?

• A named use case. One process, described in a sentence, with a clear start and end. "Help finance" is not a use case; "match supplier invoices to purchase orders and flag mismatches" is.
• A process that works for people. The steps, exceptions and hand-offs are known and written down. If staff disagree on how it works, fix that first. See which processes to automate first.
• Clean, reachable data. The records the agent needs are accurate, in systems it can read, and not scattered across inboxes and personal spreadsheets.
• Written rules and limits. What the agent may do on its own, what it must ask about, and what it must never do, including value limits.
• Least-privilege access. The agent gets its own account with only the permissions the task needs, never a person's login.
• Human checkpoints. A person approves actions that are costly, external or hard to undo. Our guide on which agent actions need human approval goes deeper.
• A log you can audit. Every action is recorded with what the agent saw, what it did and who approved it.
• An owner and a measure. One person is accountable for results, with a baseline taken before launch.
• A data decision. The business has decided, according to its own appetite for risk, which data the agent and its AI provider may see and where it may be processed.
• A way to stop it. Someone can pause the agent quickly and fall back to the manual process.

How do you score readiness?

Score each item 0 (missing), 1 (partly there) or 2 (in place). A use case with no zeros is a reasonable candidate for a small, supervised pilot. Zeros on the process, access or human checkpoint items mean the work to do first is not about AI at all. That is useful to know: fixing the process and controls usually pays off even if the agent never goes live.

Update, 27 October 2026: NIST published design tenets for agent identity management, stating that agents should have an identity, credential and access lifecycle distinct from human users, which "prevents user impersonation" and makes autonomous actions traceable (NIST, 27 October 2026). This supports the least-privilege item above.

What should the first pilot look like?

Pick a high-volume, low-risk task inside a process that already works. Keep a person reviewing every output for the first weeks, measure time saved and error rates against the baseline, and widen the agent's freedom only where the evidence supports it. COSO's guidance takes the same approach: match the level of human involvement to the risk of each use case.

Update, 9 April 2026: Mauritius launched its National AI Strategy and FAIR Guidelines, a non-binding framework that covers generative and agentic AI systems and calls for appropriate human oversight so that AI supports rather than replaces human judgement, especially in higher-risk contexts (Ministry of ICT; FAIR Guidelines).

Update, 25 September 2026: the Mauritius Electronic Transactions (Amendment) Act 2026 came into operation. It provides for the legal validity of contracts formed or performed through "automated message systems" without direct human intervention (Laws of Mauritius; Act No. 5 of 2026). Where an agent can commit the business, the limits and approvals in this checklist matter even more.

Linkgrow's AI agent workflows service applies this checklist to a chosen use case. For a quick first view of your gaps, try the gap finder.

Frequently asked questions

What is the difference between an AI assistant and an AI agent?

An assistant responds to a person, for example by drafting text. An agent can plan and carry out several steps on its own, such as reading records, updating systems or sending messages.

Do we need perfect data before using AI agents?

No, but the data for the chosen process must be accurate and reachable. Readiness is judged per use case, not for all company data.

Should an AI agent use an employee's login?

No. Give the agent its own account with only the permissions it needs, so its actions can be traced and limited separately from any person.

What is a good first AI agent use case?

A high-volume, low-risk task inside a process that already works well, with a person reviewing outputs at first and a clear measure of time and errors.