AI agents are stuck in their terrible twos, and the companies racing to productize them are learning what every parent already knows: development doesn't happen on a schedule.
The Signal
MIT Technology Review is drawing parallels between raising children and deploying agentic AI, and the comparison is more useful than it sounds. Just like anxious parents obsessing over developmental milestones, companies building AI agents are discovering that capability emergence doesn't follow a predictable timeline. An agent might excel at data analysis but completely fail at basic task prioritization. Another might handle customer service brilliantly until it encounters an edge case and melts down spectacularly.
The real insight here is about expectations versus reality. We're treating AI agents like they should mature faster than they actually do, deploying them in production before they've learned to reliably "walk." The pressure to ship is intense. Every company sees the agent economy forming and wants a seat at the table. But rushed deployment creates brittle systems that fail in ways that erode trust faster than successful tasks build it.
What makes this moment different from previous AI hypes: these agents are actually doing real work, just inconsistently. They're not vaporware. They're temperamental toddlers with occasionally brilliant moments. The companies that win will be the ones comfortable with messy, nonlinear progress rather than those forcing linear roadmaps onto fundamentally unpredictable development curves.
The Implication
If you're building with or betting on AI agents, stop expecting smooth capability curves. Build systems that assume inconsistency and create guardrails that let agents safely fail and learn. The winners in the agent economy won't be the ones who shipped first. They'll be the ones who had the patience to let their agents actually develop before scaling them into critical workflows.
Source: MIT Technology Review AI