Your team is rushing to build context-sharing for AI agents. The problem they’re solving isn’t an AI problem. It’s the same context-sharing problem your company has been working around for twenty years - and the cost of working around it just stopped being tolerable.

Maggie Appleton’s research on agent-assisted development names the bottleneck cleanly. Every solution on the table - coordination workspaces, context-sharing layers, alignment dashboards - is being built so agents can do their work. Substitute “new senior hire” for “agent” in any of those proposals. The problem is identical. The infrastructure your team is rushing to build for agents is the same infrastructure your company has worked around with humans for years - because the workaround was good enough. New engineers ramped slowly. Senior leaders carried context in their heads. “They’ll figure it out” was an acceptable answer because the cost of context loss was a quarter of slow output, not a wrong product shipped in two days. Agents change the math.

The companies that build this for agents will have built it for people too. The infrastructure doesn’t know whether the receiver is human or a model. The organizations that ship the agent-context layer will discover they’ve also shipped a human-context layer - faster onboarding, retained institutional knowledge, decisions that don’t have to be reconstructed each time someone new joins the room.

The decision you’re making about agent infrastructure is more loaded than it looks. It’s not an AI investment. It’s the move the regime change has made non-optional - the move toward making how the firm works legible to itself - with a forcing function attached. The companies that recognize what they’re actually building will use the AI urgency to fund the broader change. The ones that don’t will end up with better agent productivity inside the same firm that can’t see the next PMF.