Microsoft Build 2026

An Eleven-Line Agent That Works Half the Time

Original speaker(s): Jim Bennett, Microsoft MVP, Foundry and Developer Tools · Microsoft MVP

Verified sourceSession date not verifiedpresentation18:28EN2 min read

A system that succeeds on identical input half the time cannot be debugged by inspection at all — you need the distribution rather than the trace, which is why most published comparisons of agent architectures are measuring something other than what they claim.

Jim Bennett puts an eleven-line agent on screen and then shows what it does when you run it repeatedly: pass, pass, pass, fail, fail, fail (12:20).

It works about half the time. Nothing about the code says so, and nothing about a single successful run would have revealed it.

Why this is the hardest bug class in agent work

Conventional software fails deterministically enough to reason about. Given the same input, the same thing happens, so you reproduce it, narrow it, fix it.

An agent that succeeds half the time on identical input breaks that method entirely. Every test run is a sample rather than an observation. A fix appears to work because you ran it three times and got three passes; the change may have done nothing. Absent measurement across many runs, engineering judgement about this class of failure is unreliable in a way that feels like competence.

Which is the actual argument for observability here. It is not about dashboards. It is that a non-deterministic system cannot be debugged by inspection at all — you need the distribution, not the trace.

The demonstration that lands

Having established the failure rate, Bennett changes the model and reruns. Success rate rises to around 90 per cent (14:30), with no change to the application whatsoever (14:37).

The number matters less than what it establishes about the method. He knew the change worked because he could measure a rate before and after. Without that instrumentation the same swap would have been indistinguishable from luck, and the team would have kept the new model or reverted it on the basis of whichever runs they happened to observe.

This also inverts a common assumption about where agent quality comes from. A great deal of effort goes into prompt engineering and orchestration structure, on the reasoning that the application is what you control. Here the largest single improvement came from a component swap that took seconds — and the only reason anyone can say that is the instrumentation.

The uncomfortable implication

If model choice can move a success rate from fifty to ninety per cent with no application change, then most published comparisons of agent architectures are measuring something other than what they claim.

Two teams reporting different results for the same pattern may differ in prompting, orchestration and design — or they may differ in which model they happened to use, with everything else being noise around that. Almost no writeup in this space reports success rates across repeated runs, which means almost none of it can distinguish the two.

An eighteen-minute session cannot fix that. But it demonstrates the minimum required to make any claim about agent reliability: run it many times, count, and compare distributions. Everything short of that is anecdote with a screenshot.

Key numbers

~50% → ~90%
agent success rate before and after a model swap with no application change 14:30

Talk chapters

Key takeaways

  1. 01

    An eleven-line agent run repeatedly produces a visible pattern of passes and failures — it works about half the time, and the code gives no indication. 12:20

  2. 02

    Changing the model raised the success rate to around ninety per cent with no change to the application whatsoever. 14:37

  3. 03

    The method is the point: he can attribute the improvement because he measured a rate before and after rather than observing a few runs. 14:30

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