An AI agent is a language model that can decide on a sequence of steps and act on them through tools: searching a knowledge base, reading a record from your CRM, drafting an email, updating a ticket. That last part is what separates an agent from a chatbot. A chatbot answers; an agent does.
That makes agents genuinely useful for operations work, and it also changes the risk. A wrong answer in a chat window is an inconvenience. A wrong action in your order system is an incident. The question for most businesses is not whether agents work, but which work they should be trusted with first.
What makes a good first workflow for an AI agent?
The best candidates share a few traits. They are high in volume, they follow a recognisable pattern most of the time, and a mistake is cheap to catch and undo. Good examples:
- Triage: reading incoming support tickets or emails, tagging them, pulling the customer record and routing them to the right queue.
- Data entry from documents: extracting fields from invoices, purchase orders or application forms into your system, with a person approving anything below a confidence threshold.
- Research and preparation: assembling the account history, open orders and recent conversations before a sales or support call.
- Back-office follow-ups: chasing missing documents, sending reminders and updating statuses once a reply arrives.
What should stay with people for now?
Anything irreversible, anything that moves money, and anything where the judgement is the job. Agents can prepare a refund, a contract change or a hiring decision; a person should approve it. This is not caution for its own sake. Language models can be steered by the text they read, a weakness known as prompt injection, which OWASP ranks first in its Top 10 for Large Language Model Applications. An agent that reads customer emails can be sent instructions inside one.
How to give an agent access without giving it the keys
- Least privilege: give the agent its own service account with only the permissions the workflow needs, never an administrator’s credentials.
- Read and write separately: most of an agent’s value comes from reading and drafting. Make write actions explicit tools with narrow inputs, such as update_ticket_status, rather than a general database connection.
- Approval gates: route high-impact actions to a person with the agent’s reasoning and the proposed change side by side.
- Limits: cap the number of actions per run and per hour, so a loop or a bad instruction cannot do damage at machine speed.
- Full logging: record every tool call, its inputs and its result. You will need this to debug, to audit and to improve the prompts.
Standards are making integration easier
Connecting an agent to your systems used to mean custom code for every model and every tool. The Model Context Protocol (MCP), an open standard introduced by Anthropic in late 2024 and since supported across tools from OpenAI, Google and Microsoft, gives agents a common way to discover and call tools. It does not remove the need for permissions and review, but it does mean the integration you build once can be reused as models change.
How to roll it out
Run the agent in shadow mode first: it proposes, people act, and you compare. Measure how often its proposal would have been accepted unchanged. Widen its authority one action at a time as that number earns it. Agents that start with full autonomy tend to end with none, after the first visible mistake.
