Agent sprawl looks like sophistication. It's a symptom of missing memory.
A large fleet of narrow AI agents usually isn't sophistication. It's evidence that no shared, governed memory exists, so every team built its own private workaround instead. Give every tool one governed memory and most organisations need far fewer agents, not more.
Ask a company how advanced its AI programme is, and lately the answer arrives as a number: forty specialised agents, a hundred, "an agent for every workflow." It's offered as evidence of sophistication. Look closer and it usually reads the opposite way: proof that the one problem worth solving, a shared and governed memory, was never solved, so a fleet of workarounds had to stand in for it.
Every agent is a vehicle for a slice of context
Build a narrow agent and you sidestep the hard version of the memory problem. Scope it tightly enough, the returns policy, the leave-request rules, one product line, and you can hand-feed it that sliver directly, in its prompt or its own private store. That's manageable, which is exactly why it multiplies. The moment a second team needs AI for a second slice of the business, they don't extend the first agent's context. They build a second vehicle, sized to their slice, because there's no shared place to plug into. Each agent isn't a specialist by design so much as a specialist by necessity: the only way to keep the problem small enough to solve without a real memory underneath it.
What the fleet actually costs
Multiply that pattern across a few dozen teams and the organisation ends up holding the same knowledge in dozens of separate, unsynchronised places. The leave policy changes; someone updates the HR agent. The rostering agent, built eighteen months ago by a different team from a document that has since been superseded, keeps answering from the old one. Nobody decided that on purpose. It's what happens when a fact about the business lives in a hundred private silos instead of one shared, governed one. A bigger fleet isn't a more capable fleet. It's more places for the business to quietly disagree with itself, until someone asks two different agents the same question and gets two confident answers back.
The honest deadline is not the regulator's
Two dates now sit on the calendar, and neither is about model capability. The EU AI Act's high-risk obligations were pushed from August 2026 out to December 2027, a real reprieve, and a temptation to file governance under "next year's problem." In the same window, Gartner predicts more than 40 percent of agentic AI projects will be cancelled before that year is out. Read next to everything above, that number stops looking like a verdict on weak models. It reads like a verdict on sprawl: fleets of independently built agents drifting out of agreement with the business and with each other, until nobody left in the room can say which one to trust. That's the deadline that actually matters, and it doesn't move just because Brussels moved one.
Fewer agents, not less ambition
The honest fix isn't to build fewer things. It's to stop handing every new agent its own private memory to maintain. Give every tool the same governed memory to draw from, one place where the business's current knowledge lives, where what's true gets decided once and every connected agent inherits it the next time it asks, and specialisation stops requiring duplication. An agent can still be narrow and built for one job without being an island that has to be separately taught, separately updated, and separately trusted. Under a shared memory, you need fewer agents, not because the ambition shrank, but because most of what a new agent used to exist for, carrying its own slice of the business around, is no longer its job.
A fleet of agents was never the achievement. A fleet that agrees with itself is.
Loriq builds SAGE, the governed memory engine. Talk to us.