Every Agent Reliability & Production comparison and buyer's guide for building AI agents — 6 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
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.
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.
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.
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.
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.
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.