Could autonomous pricing agents dampen competition without explicit collusion?

The CMA (UK Competition and Markets Authority), in a March 2026 publication on agentic AI and consumers, argues that agentic AI could intensify algorithmic pricing risks the regulator has already flagged for ordinary pricing algorithms. The CMA states that algorithmic pricing "can also increase the risk of more coordinated market outcomes, even in the absence of explicit communication between businesses, particularly where algorithms learn from and react to each other in concentrated markets." It then extends that finding explicitly to agents: "Where multiple businesses deploy autonomous agents that optimise pricing or commercial strategies, there is a risk that interaction between these systems could dampen competitive pressure."

The mechanism the CMA describes is tacit coordination through mutual adaptation rather than explicit agreement: pricing systems that learn from and react to each other's behavior in concentrated markets can converge on softer competition without any business ever communicating with a rival. Agentic AI, in the CMA's framing, raises the stakes on this existing concern because agents act with greater autonomy and across more of the commercial relationship — not just setting a price but potentially coordinating broader commercial strategy — which widens the surface on which interaction effects can emerge. The CMA notes it has addressed the topic elsewhere, in a blog post on AI and collusion, signaling this is an active area of regulatory attention rather than a one-off observation.

This argument sits alongside Can traditional governance tools actually control AI agents?, which makes a parallel point from a different direction: conventional tools built for principal-agent problems do not anticipate systems that interact with each other, not just with their own principal. It also complicates Can EU consumer law protect AI agents as algorithmic buyers? — where that argument holds that consumer law needs redesigning for algorithmic consumers, the CMA takes the opposite starting position for the UK, insisting existing consumer protection law already applies regardless of whether a decision is made by a person, an algorithm, or an agent, because "it is the effects on consumer decision‐making, not the novelty of the technology, that matter most."

The excerpt documents a risk and a regulatory posture, not a measured outcome: the CMA does not report evidence of actual collusion occurring through deployed commercial agents, and deployment of agentic AI is, by the CMA's own account, still "concentrated in domains where scope and oversight can be tightly managed" rather than fully autonomous and consumer-facing on pricing. The implication the CMA draws is anticipatory — that competition-law readiness should be in place before agent-to-agent pricing interaction becomes widespread, not that it has already caused measurable harm.