Pricing agents under enforcement spotlight\u00a0<\/p>\n
In March 2026, the CMA stated<\/a> that businesses are responsible for an AI agent\u2019s conduct in the same way as they are for an employee, including where the agent was designed or supplied by a third party. Businesses should therefore understand the law and how the technical system works, set clear boundaries, monitor outputs and intervene quickly if the agent risks distorting competition, for example through price coordination or exchanges of competitively sensitive information.\u00a0<\/p>\n Risk is highest where an AI agent can recommend or execute decisions affecting core parameters of competition: price, output, quality, choice, innovation, access, ranking, discounts or customer allocation. Enforcement to date has focused mainly on pricing and price recommendations, but competition law concerns can arise wherever an agent reduces uncertainty between competitors, deploys a foreclosure strategy or reinforces an existing dominant position.\u00a0<\/p>\n Key competition law risks and recent enforcement trends<\/p>\n In its recent blog on AI and collusion<\/a>, the CMA identified three main risks involving AI systems and pricing:\u00a0<\/p>\n \u2018Classic\u2019 collusion \u2013 businesses agree to collude and use software to implement or monitor a cartel. In the UK, the CMA fined<\/a> Trod GBP 163,371 and pursued director disqualification proceedings after finding that Trod and GB eye had agreed not to undercut each other on poster and frame sales on Amazon Marketplace. In the US, the DOJ brought<\/a> criminal enforcement action against David Topkins, who was fined USD 20,000 after allegedly agreeing with competitors to adopt pricing algorithms and instructing algorithm-based software to set prices in line with the conspiracy. In both cases, the illegal agreement was made by people, but automated pricing software helped to implement the conduct by monitoring and aligning prices.\u00a0<\/p>\n Hub-and-spoke collusion \u2013 businesses may use the same algorithm or data hub to exchange competitively sensitive information indirectly. This may involve delegating pricing decisions to the hub or receiving pricing or other conduct recommendations from it based on co-mingled data. In the US, rental-pricing software was alleged<\/a> to have enabled competing landlords to share non-public, competitively sensitive rental data through a common platform, which then generated rent recommendations and allegedly aligned pricing across competing properties. The case was settled with RealPage giving a number of undertakings on future conduct. The case illustrates that agentic or algorithmic pricing tools can create hub-and-spoke coordination concerns even where the tool is operated by a third-party software provider. Similarly, in May this year, the Danish competition authority issued<\/a> a formal warning to an AI accounting assistant that benchmarked users\u2019 fees. In its findings, the authority cited an example of the agent suggesting that carpenters in Copenhagen raise prices where they charged below the local average.\u00a0<\/p>\n Competition law risks in predictable-agent and autonomous AI systems<\/p>\n Businesses may use algorithms that react predictably to market events, potentially softening competition. Such algorithms may follow price leadership and punish deviations, achieving collusive outcomes without human communication or explicit agreement. Similarly, an AI system tasked with maximising profits may learn to reach coordinated outcomes that align or increase prices, even without any human intent to collude.\u00a0<\/p>\n As agentic AI increasingly makes autonomous pricing decisions, competition authorities are increasingly focused on whether it was foreseeable that AI systems could produce anti-competitive effects, such as the price collusion and information exchange discussed above. Singapore\u2019s Competition and Consumer Commission takes a compliance-by-design approach and has launched an AI Markets Toolkit<\/a>, which references the CMA\u2019s Foundation Model report, to help businesses check whether their models are, by design, compliant with Singaporean competition and consumer laws. The CMA does not have a similar tool, but it has issued helpful guidance and blogs to help businesses navigate compliance by design and monitoring.<\/p>\n What should businesses do? Compliance safeguards for the AI lifecycle from design to deployment\u00a0<\/p>\n