Every Agent Reliability & Production comparison and buyer's guide for building AI agents — 7 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
A $0.14 model doesn't fail by throwing an error. It fails by getting slightly worse at every step until, forty turns in, it returns a confident wrong answer that passes your shallow check. Here are the four ways it happens and the four cheap guards that catch each one.
5 min
Durable execution saves the agent's work when the server dies. It does nothing for the user whose phone dropped Wi-Fi mid-answer — that's a different resume problem, on the other side of the wire, and the new stateless MCP spec quietly made it harder.
4 min
Whole-task routing picks a model before the work starts. Agents need something harder: to notice, mid-trajectory, that they're now out of their depth — and three 2026 benchmarks say they can't be trusted to notice it themselves.
5 min
Exponential backoff and durable checkpoints handle the errors that throw. They do nothing for the tool call that succeeds with the wrong answer — and that's the one that kills agents in production.
5 min
The failure isn't that the agent forgets the goal. It's that, step by step, a louder goal replaces it — and the fix is a ratio, not a bigger memory.
5 min
You can't A/B test an agent the way you A/B test a button. The unit of variance is a trajectory, not a click — so the gate has to be offline, and "shadow mode" means something different than it does for a model.
4 min
A model that solves a task 61% of the time can be reliable only 25% of the time. The gap between those two numbers is where production agents go to die.
5 min