Most AI in a business system does one thing when asked. Read this document. Draft this email. Score this lead.<\/p>\n
An agent<\/strong> is different. It is given a goal rather than a task, and it works out the steps itself.<\/p>\n “Chase the overdue invoices” instead of “send this reminder to this customer”.<\/p>\n To do that, it has to decide what to look at, decide what to do, do it, see what happened, and decide what next. Multiple steps, with judgement between them.<\/strong><\/p>\n That is a genuine advance. It is also why it needs more care in a system where money moves.<\/p>\n Concrete examples make the idea clearer.<\/p>\n Collections.<\/strong> Review overdue invoices, check each customer’s history and any open disputes, draft appropriately different messages, send them, log responses, and escalate the ones that need a person.<\/p>\n Purchasing.<\/strong> Notice low stock, check lead times and supplier reliability, prepare purchase orders, and flag the ones needing approval.<\/p>\n Support triage.<\/strong> Read incoming tickets, gather relevant context, resolve the routine ones, and route the rest with a summary attached.<\/p>\n Month-end preparation.<\/strong> Run the standard checks, chase missing documents, assemble the exceptions, and present a list for the accountant.<\/p>\n Each of these is several steps a person currently does in sequence.<\/p>\n FIGURE 1: WHAT AN AGENT DOES DIFFERENTLY<\/p>\n Given a goal<\/p>\n Decides the steps<\/p>\n Acts and observes<\/p>\n Escalates<\/p>\n Some of this works now. Much of it works in demonstrations and needs supervision in production.<\/p>\n Working reasonably today:<\/strong> multi-step tasks within a narrow, well-defined boundary, with review before anything commits.<\/p>\n Emerging:<\/strong> longer chains where the agent decides between several genuinely different courses of action.<\/p>\n Not there:<\/strong> anything you would leave unsupervised where the cost of being wrong is significant.<\/p>\n The gap is not intelligence. It is reliability under exceptions.<\/strong> An agent that handles ninety-five percent of cases well and does something odd on the other five percent is a problem in a system that touches money \u2014 because the five percent is where the money usually is.<\/p>\n Four reasons an agent in an ERP is a different proposition from an agent doing research.<\/p>\n Actions are hard to reverse.<\/strong> A posted journal entry needs a reversal. A sent email cannot be recalled. A confirmed order has been communicated to a supplier.<\/p>\n Errors compound quietly.<\/strong> A wrong step produces a wrong record, which the next step treats as fact. By the time anyone notices, several things are wrong.<\/p>\n Explanation is genuinely difficult.<\/strong> When an agent takes fifteen steps and the outcome is wrong, tracing why is hard. In a system an auditor may examine, that matters.<\/p>\n Permissions are inherited wholesale.<\/strong> An agent with the ability to post entries can post wrong ones. There is no partial capability.<\/p>\n FIGURE 2: WHY AN ERP IS NOT A SANDBOX<\/p>\n An agent doing research<\/p>\n An agent in your ERP<\/p>\n Two rules make agents useful today.<\/p>\n An agent with a defined scope and a limited set of permitted actions is far more reliable than one with broad latitude.<\/p>\n “Prepare collection emails for overdue invoices under a set value, do not send” is a task an agent handles well.<\/p>\n “Manage our receivables” is not, yet.<\/p>\n Let the agent do the work and stop before anything irreversible.<\/p>\n Draft the emails and present them. Prepare the purchase orders as drafts. Assemble the exception list.<\/p>\n A person confirms.<\/strong><\/p>\n This captures most of the time saving. The steps that take the effort are the gathering, checking and preparing \u2014 not the final click.<\/p>\n FIGURE 3: MAKING AGENTS SAFE ENOUGH TO USE<\/p>\n Narrow the scope<\/p>\n Stop before commit<\/p>\n Limit permissions<\/p>\n Log every step<\/p>\n Six, and vague answers tell you something.<\/p>\n That last one matters more than it sounds.<\/strong> An agent working through two hundred records needs a stop button that works immediately.<\/p>\n “Autonomous” as a selling point.<\/strong> In a business system, autonomy is a risk to be managed, not a feature to be maximised. Ask what it does without confirmation.<\/p>\n Demonstrations on clean data.<\/strong> Everything works on clean data. The question is what happens with a duplicate customer, a missing cost, or a half-completed record.<\/p>\n Claims without a stop mechanism.<\/strong> If nobody can explain how to interrupt it, it is not ready.<\/p>\n Anything that would post to your ledger unsupervised.<\/strong> Whatever the accuracy figures.<\/p>\n For most businesses, the sensible stance is:<\/p>\n Watch this properly.<\/strong> It is progressing quickly and the direction is real.<\/p>\n Use narrow, supervised versions now<\/strong> where they exist. Preparation and gathering, not commitment.<\/p>\n Do not restructure your processes around it yet.<\/strong> Build on what works today.<\/p>\n Keep your data clean.<\/strong> Everything that comes will work better on clean data, and worse on bad data. That work is worth doing regardless of what arrives.<\/p>\n An agent works out its own steps toward a goal, rather than doing one task. That is a genuine advance and it is arriving.<\/p>\n In a business system the difficulty is not capability. It is reliability on the exceptions<\/strong>, and the fact that actions cost money and are hard to reverse.<\/p>\n Narrow the scope. Stop before the commit. Limit the permissions. Log everything.<\/strong><\/p>\n Used that way agents are useful now. Used as marketing describes them, they are a risk you will not notice until the exception arrives.<\/p>\n Hearing a lot about AI agents and unsure what is real?<\/p>\n Get in touch. We will look at which of your multi-step processes could be prepared automatically \u2014 and which ones should keep a person on the commit.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":" What makes something an agent Most AI in a business system does one thing when asked. Read this document. Draft this email. Score this lead. An agent is different. It is given a goal rather than a task, and it works out the steps itself. “Chase the overdue invoices” instead of “send this reminder to […]<\/p>\n","protected":false},"author":3,"featured_media":9217,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[],"class_list":["post-9216","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"yoast_head":"\nWhat this could look like<\/h2>\n
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The honest state of it<\/h2>\n
Why business systems are harder than they look<\/h2>\n
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Where to use them now<\/h2>\n
Narrow the boundary<\/h3>\n
Keep the commit step<\/h3>\n
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Questions to ask any vendor<\/h2>\n
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What to be sceptical about<\/h2>\n
A realistic position<\/h2>\n
The short version<\/h2>\n