Whenever I see a new agent project kick off, I can almost always predict the first architecture decision: pick one monolithic model, wire it to some tools, and then tune prompts until something works. I have been there myself. It feels clean. It keeps procurement simple. It gives teams one benchmark to watch. It also breaks down as soon as you start to see any real traffic. Production agents don’t fail because the model is “bad.” They fail because the operating environment is messy: requests change shape, latency budgets conflict, tools flake out, costs spike, policy constraints shift and failure modes compound. A single-model architecture makes all of those problems focus on one point of failure. In practice, that becomes an availability risk, a cost risk and a governance risk over time.

Full opinion : Relying on one giant AI model for everything is a trap; it’s too expensive and slow for simple tasks and too risky for the hard stuff when things go wrong.