Agentic AI
Agentic AI describes systems, often called AI agents, that plan and execute multi-step tasks toward a goal rather than returning one response to one prompt. A procurement agent might send an RFQ, chase the four suppliers who have not responded, log returned quotes, and assemble a comparison, choosing each next step itself. The defining feature is initiative inside explicit boundaries.
Examples
Quote chasing: An agent issues an RFQ to nine suppliers, sends tailored reminders on days 3 and 6 to non-responders, and answers two clarifying questions from a pre-approved FAQ. Eight of nine quotes arrive by day 8; the buyer touched none of it.
Price monitoring: An agent compares contracted extrusion prices to the aluminum index monthly. When the index drops 6% and holds for two quarters with no price movement, it opens a renegotiation task with the math attached.
Boundary in action: The same agent may release against a blanket PO within 5% of contract price on its own. A supplier's request for 9% lands in a buyer's queue within the hour instead of being quietly accepted.
Definition
The contrast with generative AI is the loop. A generative model answers when asked and stops. An agent observes state (three quotes in, six outstanding), picks an action (send reminders, rephrase the question for the supplier who misread it), executes, and checks the result, repeating until the goal is met or a rule says stop.
Agents earn their keep on high-volume, low-stakes loops nobody staffs properly: chasing quote responses, updating records after events close, monitoring contracted prices against indices, assembling status reports. Pointed at an entire sourcing event end to end, the same machinery becomes autonomous sourcing; pointed at bilateral dealmaking, autonomous negotiation.
Guardrails are not optional, because errors compound across steps: a system that is 95% reliable per step succeeds about 60% of the time across ten steps. Working deployments scope agents tightly, log every action, cap what they can commit (spend limits, approved recipients), and put human checkpoints in front of anything a supplier would read as a commitment. LightSource takes this approach: agents handle quote chasing and record updates while awards and supplier commitments stay with the buyer.
Frequently asked questions
What is agentic AI in simple terms?
Agentic AI describes systems, often called AI agents, that plan and execute multi-step tasks toward a goal rather than returning one response to one prompt. A procurement agent might send an RFQ, chase the suppliers who have not responded, log returned quotes, and assemble a comparison, choosing each next step itself. The defining feature is initiative inside explicit boundaries.
What is the difference between agentic AI and generative AI?
The difference between agentic AI and generative AI is the loop. A generative model answers when asked and stops, while an agent observes state, such as three quotes in and six outstanding, picks an action, executes it, and checks the result, repeating until the goal is met or a rule says stop.
Where does agentic AI work best in procurement?
Agents earn their keep on high-volume, low-stakes loops that nobody staffs properly: chasing quote responses, updating records after events close, monitoring contracted prices against indices, and assembling status reports. Pointed at an entire sourcing event end to end, the same machinery becomes autonomous sourcing, and pointed at bilateral dealmaking it becomes autonomous negotiation.
What guardrails does agentic AI need?
Guardrails matter because errors compound across steps: a system that is 95% reliable per step succeeds only about 60% of the time across ten steps. Working deployments scope agents tightly, log every action, cap what they can commit through spend limits and approved recipients, and put human checkpoints in front of anything a supplier would read as a commitment.
Can agentic AI make supplier commitments on its own?
In well-governed deployments, awards and supplier commitments stay with the buyer. An agent may act inside a defined envelope, for example releasing against a blanket purchase order within 5% of contract price, while anything outside the envelope escalates to a human, such as a supplier's request for a 9% increase landing in a buyer's queue within the hour.
Related Terms
Autonomous negotiation
Human-in-the-loop (HITL)
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