What founders should know before building an AI agent

AI agents — systems that can autonomously plan and execute multi-step tasks — represent one of the most significant developments in applied AI. They also represent one of the most significant sources of over-optimism in product planning right now.

The promise is real. Agents can automate workflows that were previously too complex to handle with traditional automation. But the implementation complexity is also real, and it is consistently underestimated by teams that haven't built them before.

A standard AI feature — a classifier, a summariser, a recommendation engine — operates on a single input and produces a single output. An agent operates across multiple steps, often with the ability to call external tools, make decisions about what to do next, and handle unexpected states.

This multi-step, tool-using architecture introduces failure modes that don't exist in simpler systems. Errors compound. Incorrect intermediate steps lead to incorrect final outputs. And because agents are harder to observe, problems can persist longer before they're detected.

The founders who build agents most successfully tend to start with a very specific, well-bounded use case rather than a general-purpose agent. They define exactly what the agent is supposed to do, what it should never do, and how a human will verify the output before it has consequences.

They also invest in evaluation infrastructure early — the ability to test agent behaviour systematically, rather than relying on manual spot-checking. Without this, it's very difficult to know whether changes to the agent are improvements or regressions.