The Quiet Race to Control the AI Agent Economy

The early phase of artificial intelligence was dominated by spectacle. Larger models, sharper outputs, and ever more convincing demonstrations created the impression of rapid and universal progress. That phase is now giving way to something quieter and more decisive. The central question is no longer how intelligent machines can become, but where, and under whose authority, that intelligence is deployed.

This shift explains the growing attention paid to AI agents. Not agents as science fiction imagined them, acting independently and without restraint, but agents as embedded systems that assist decision-making inside organisations. Their importance lies less in autonomy than in placement. An agent that operates inside a critical workflow can exert far more influence than a more capable system that exists outside it.

In technology, as in politics, power follows position.

The economics behind this shift are already visible in the data. Research from McKinsey shows that organisations embedding AI directly into existing workflows are more than twice as likely to report material EBIT impact than those relying on standalone AI tools, largely because adoption is habitual rather than experimental (see McKinsey Global Survey on AI). Gartner estimates that by 2026 more than 60% of enterprise AI deployments will be embedded inside core platforms such as CRMs and marketing automation systems, up from less than 20% in 2023, reflecting a broader shift away from point solutions toward integrated infrastructure (Gartner, Top Strategic Technology Trends). This helps explain why established platforms are moving decisively to integrate agent layers into their products. Klaviyo’s approach with K:AI, embedded inside the system where segmentation, messaging and revenue decisions already occur, reflects this consolidation dynamic rather than a feature race. The implication is clear. AI value is accruing not to the most advanced models, but to the systems already positioned at moments of decision.

Most organisations are not seeking radical automation. Regulation, brand risk and internal governance all impose limits on how much control can be ceded to machines. What firms want instead are systems that make existing teams more effective, reducing friction while preserving accountability. This favours AI that integrates quietly rather than disrupts noisily.

The consequence is that the most valuable agents are not arriving as standalone tools. They are emerging within platforms that already structure how work gets done. These platforms possess three scarce assets: long-term data, habitual usage, and trust. Intelligence without these inputs struggles to produce durable value. Intelligence combined with them compounds.

This is why the agent economy is likely to reward incumbency over novelty.

Consider Klaviyo, a company whose role in marketing long predates the current enthusiasm for artificial intelligence. Klaviyo sits at a junction where customer data, messaging decisions and revenue outcomes converge. Over time, it has become an operational layer rather than a peripheral tool.

The introduction of its K:AI agent reflects this position. Rather than promising full autonomy, it augments decisions that marketers already make: how to segment audiences, when to communicate, how to frame messages. The agent does not replace judgement. It shapes it, repeatedly and at scale, inside a system that already commands attention.

This approach is not dramatic, but it is effective. Adoption follows familiarity, not ambition.

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Much discussion of AI agents focuses on whether machines will eventually act independently of humans. This misses the more immediate dynamic. Influence does not require autonomy. An agent that suggests options, ranks priorities, or frames decisions can alter outcomes even when humans retain final control. Over time, such systems establish defaults. Defaults harden into norms.

This is how infrastructure asserts power. Not through command, but through convenience.

Seen this way, the strategic value of agent layers becomes clearer. They do not need to be perfect. They need only to be present at the moment decisions are made.

The implications extend beyond marketing. In finance, logistics, healthcare and law, similar patterns are emerging. Intelligence is becoming abundant and interchangeable. Context remains scarce and defensible. Firms that control context determine how intelligence is used, and to whose benefit.

History offers familiar parallels. Operating systems mattered more than applications. Cloud platforms mattered more than the software built on them. In each case, value accrued to the layer that mediated access and standardised behaviour.

AI agents are likely to follow the same path.

Public debate often frames this transition in ethical or existential terms, asking whether machines will replace humans or undermine agency. Those questions may become relevant in time. For now, they obscure the simpler reality. The decisive struggle is over control, not consciousness.

The winners of the agent economy will not necessarily be those who invent the most advanced systems. They will be those who decide where those systems operate, how they are constrained, and which decisions they quietly influence.

That race is already underway, and it is less open than it appears.