An agentic workflow in procurement is a buying process where AI agents handle some steps themselves, choosing what to do next based on the request in front of them, while fixed rules and human approvals handle the rest. It sits between a traditional automated workflow and a fully autonomous process.

Most procurement workflows today are rule-based: if the spend is over £50,000, send it to the CFO. Agentic workflows keep those rules and add agents for the steps that need judgement on the details of each request.

What’s the difference between an agentic workflow and an automated workflow?

An automated workflow follows rules set in advance. It does the same thing every time a request meets the same conditions. An agentic workflow adds agents that read the context of each request and decide how to handle it.

What does an agentic workflow look like in practice?

Take a request to bring in a new marketing contractor. The requester describes the work in plain language. An agent completes the request, checks if the supplier isn’t already on the books, suggests any existing or preferred suppliers, and fills the gaps from the contractor’s documents.

This request is then sent to Finance for budget approval and to Risk for assessment, in parallel. Down the line an agent checks the contract against the legal playbook and clears it as low risk, so it doesn’t need to sit in a separate legal queue waiting for review. Another prepares the approval recommendation for the budget holder, with the evidence attached. The budget holder approves, and the purchase order is created in the ERP.

People are involved to sense-check decisions and recommendations that are clearly evidenced. Agents handle the work that makes that possible.

Which procurement steps suit AI agents?

Agents suit steps that happen often, depend on information the system already holds, and are simple to course correct if the agent gets one wrong. Intake quality checks, duplicate supplier checks, form reviews, low-risk contract triage and collecting supplier security documents all fit in this bucket.

Keep people on strategic sourcing decisions, high-value approvals, new suppliers with high risk scores and anything regulators expect a named person to sign off. For most regulated businesses, full autonomy on high-risk workflows isn’t the place to start.

How do you add agents without losing control?

Start with the foundations. Agents need clean, structured information to work from. If intake still happens over email and supplier data sits in several systems, agents will speed up the mess rather than fix it. That’s why most teams begin at intake, where agents also build the record of requests and decisions that later agents rely on.

Then start with agents that recommend rather than act. Watch how often a person accepts the recommendation unchanged. Once that rate is high for a type of request, let the agent act alone on that type, within spend and risk limits.

Log every decision an agent makes against the agent, so the audit trail shows who or what did each step. Review the log regularly, and tighten or loosen limits based on what it shows.

How does Omnea build agentic workflows?

Omnea’s Agentic Operating System (aOS) ships with dozens of agents across intake, sourcing, third-party risk and supplier management. Teams build agentic workflows in the no-code Workflow Builder, connecting blocks for tasks, approvals, questionnaires and integrations, and add agents from the agent library to the steps where they help.

Each agent’s autonomy is set for its step. The Approval Agent, for example, recommends a decision by default, and teams can let it approve or reject routine, low-risk, high-volume tasks on its own when it’s confident. Every agent action comes with a rationale and the source data behind it, and is logged against the request separately from human actions.

Every intake, approval, rejection and policy decision feeds Omnea’s Context Engine, so agents draw on what earlier decisions taught them. Across its first few thousand live decisions, the Approval Agent went from matching the human reviewer 86% of the time to 95% or more. When an approver overrides it, an admin decides whether that correction applies more widely. Agents also help build the workflows: the Workflow Architect Agent and Approval Logic Builder Agent turn plain-language descriptions into workflow logic.

“We set out to find a partner who could take us further on automation and embedded AI. We ended up removing the legacy system entirely and integrating Omnea directly with our ERP. Everything runs in Omnea now, and we’re building agents the old system could never have supported.”

Gabriel Galdino, Procurement Team Lead, Adyen

FAQs

Is an agentic workflow the same as RPA?

No. Robotic process automation (RPA) copies the clicks a person makes in a system, following a script. It breaks when a screen or process changes. An agentic workflow uses AI agents that read the request and decide what to do, working inside the procurement system rather than on top of other systems’ screens.

Can you add AI agents to an existing procurement workflow?

Yes, if the workflow runs in a platform that supports agents. Most teams add agents one step at a time, starting with checks such as intake quality or duplicate suppliers. Each agent starts by recommending, and gets more autonomy once its recommendations prove reliable for that type of request.

Who is accountable when an AI agent makes a procurement decision?

The organisation stays accountable, so the person who set the agent’s limits owns the outcome, usually the procurement lead who configured the workflow. That’s why every agent decision should be logged against the agent, with the evidence it used. Auditors then see exactly which steps were automated and under which rules.