How to evaluate agentic AI vendors for procurement

To evaluate agentic AI vendors for procurement, ignore the "agentic" label and test four things a rebranded product cannot fake: production references for your specific workflow, cross-system action, governance artifacts that already exist, and maintenance ownership. Feature checklists and demo impressions fail here because the vendor pool itself is polluted: Gartner estimates that of the thousands of vendors claiming to sell agentic AI, only about 130 actually offer it, and calls the rebranding of chatbots, copilots, and RPA "agent washing." The four tests below work because they probe what the product does after the contract is signed, which is the part a relabeled chatbot cannot demo.

Production references and cross-system action expose rebranded automation

Start with references, and be specific about what counts. You want customers where the system owns decisions in production today for the workflow you are buying – invoice exceptions, supplier master data, req-to-PO conversion, whichever queue hurts. Pilots and design partnerships do not count. The question that cuts through: what percentage of this workflow's decisions does the system execute without a human clicking approve? A vendor that has delivered autonomous procurement in any real sense will have a number. A vendor selling a copilot will change the subject to time savings.

Then test cross-system action. Procurement exceptions live across the ERP, supplier portals, shared inboxes, and spreadsheets, and a real agent completes work across all of them. A rebranded product acts only inside its own suite and hands everything else back to a person, which in practice is how agentic AI differs from RPA. Ask the vendor to show one exception resolved end to end across two systems it does not own. If the demo cannot leave the vendor's own interface, neither will the product. A quick screen alongside both: a vendor priced per seat is planning for people to keep working the queue, while pricing tied to outcomes matches what you are buying.

Governance and maintenance show whether the vendor expects real autonomy

The stakes on governance are not small: the same Gartner press release predicts that over 40% of agentic AI projects will be canceled by the end of 2027, and inadequate risk controls is one of the reasons it cites. Ask to see three governance artifacts in the current product, today: decision logs at the level of individual actions, configurable autonomy thresholds, and an exportable audit trail. If any of these is "on the roadmap," the agent is not ready for your auditors, whatever the demo showed. A vendor whose system genuinely acts on its own has already been forced to build all three by its existing customers.

Finally, ask who owns maintenance. Exception patterns shift and ERP configurations drift, so an agent that is accurate in month one degrades quietly by month nine unless someone is watching. Ask who detects accuracy degradation, who retunes the system, on whose budget, and how fast. A real agentic vendor budgets for model drift as part of its own operating cost; if retuning arrives as a professional-services proposal, you have bought software plus an open-ended consulting relationship, and the economics that justified the purchase quietly erode. This last test also previews what agentic AI changes for the CPO: the vendor relationship becomes an operating partnership, and the contract should read like one.

These tests apply to every vendor in your pool, Fragment included. Fragment's agents work across the full source-to-pay lifecycle – PO creation and change orders, RFQ administration, supplier master data, invoice exceptions and three-way match, credits and deductions, GL coding, tax holds – reasoning across your existing ERP, portals, email, and spreadsheets, with nothing ripped out or replaced. If you are building a shortlist, Fragment's workflow coverage is a reasonable place to see how these criteria map to specific queues, and a demo is the right setting to put the same four questions to Fragment that you would put to anyone else.