Most agents that fail in production do not fail because the model is too small. They fail because nobody defined what the agent is allowed to do, what it should refuse, and how it recovers when a step goes wrong.
A bigger model hides this for a while. It guesses well enough that the gaps stay invisible in a demo. Then real users arrive with real edge cases, and the guessing shows.
Structure is the boring part that makes the impressive part reliable. Clear tools, clear boundaries, clear fallbacks.
What structure actually means
- A tight set of tools the agent can call, each with a clear contract.
- Explicit stop conditions, so the agent knows when to hand back to a human.
- A memory model that is deliberate, not accidental.
- Observability, so you can see why the agent did what it did.
Where to spend your time
Spend it on the scaffolding. Define the job narrowly, wire the tools carefully, and log everything. The model you already have is almost always good enough once the structure around it is right.
A capable model on a weak structure is a confident system that is wrong in ways you cannot see.