"AI coding agents" gets searched by people who already know what autocomplete is and want more: something that reads a repository, plans a change, and works across files without hand-holding. Every roundup that answers the query ranks the same handful of tools on the same axis, how well they write code inside a session. That's a real measurement, and it's the one that gets most of the attention. It stops the moment the session ends, which is exactly where a software house's actual risk starts.
What teams mean by AI coding agents
The term now covers a specific job, not just "AI that helps you code." An agent reads more than the open file, it plans a change across a repository, edits multiple files in sequence, runs commands and tests, and reports back when it considers the task done. Claude Code runs that loop from a terminal, headless if needed. Cursor's agent mode runs it from inside an editor, with each edit visible as it happens. Both get called agents because both can be handed a task description and left to work, unlike a completion tool that only ever finishes the line you're already typing.
That distinction matters for what follows: an agent, by definition, does more unsupervised work than a completion tool did, which means there's more that happens between the request and the result that nobody watched in real time.
What current roundups measure
Almost every comparison on the market, on faros.ai, vellum.ai, zapier.com, augmentcode.com, measures capability: benchmark scores, how many files an agent touches correctly, how often its first attempt compiles, how fast it finishes a task. Adoption backs up why this is the obvious axis to measure. Coding tools are the largest category of departmental AI spend, close to $4B in 2025, with adoption above 65% among top-quartile engineering teams, according to Menlo Ventures' 2025 State of Generative AI in the Enterprise. At that scale, ranking agents on raw output makes sense as a first filter.
It also measures something less solid than it looks. A METR randomized controlled trial published in mid-2025 had 16 experienced open-source developers complete 246 real tasks in codebases they knew well, with and without AI tools. Before starting, they forecast that AI would cut their completion time by about 24%. The measured result went the other way: tasks took about 19% longer with AI allowed. The developers still walked away believing they'd been faster. If people who did the work and watched themselves do it can misjudge the outcome, a roundup scoring capability from the outside is measuring something that doesn't reliably match what happened.
What they do not measure
None of the roundups ask what's left once the task is marked done. That's not a gap in methodology, it's outside what the category is built to produce in the first place. The Stack Overflow Developer Survey 2025 shows the shape of the problem at scale: 69% of developers using coding agents report higher individual productivity, but only 17% report better team collaboration. Faster individual output and a team that trusts what it's holding are not the same outcome, and closing the first gap doesn't close the second.
The 2025 DORA report adds the other half: AI adoption correlates with more throughput, but also with more delivery instability, not less. A team that ships faster with an agent isn't automatically shipping something it can account for later. Capability rankings answer "how good is this agent at the task." They don't answer "what does the team have, afterward, to show a client or an auditor why a specific change happened the way it did."
A definition of remaining proof
Remaining proof is what a team can still produce after the agent, and the person who ran it, are no longer in the room to explain the change: the intent behind the task, the context the agent was authorized to touch, what it actually executed, and an artifact tying those three together, portable enough to survive a change of tool and specific enough to be checked, not taken on trust. Right now, no agent in the category produces that as output. Claude Code and Cursor both log their own reasoning, in their own format, in a place that doesn't travel if the team switches tools next quarter. The log explains the session to itself. It doesn't explain the session to a reviewer who wasn't there.
How to compare agents on evidence, not only on capability
Capability still matters, and the METR result is a reason to weight it carefully rather than to ignore it: a benchmark score or a roundup's ranking can diverge from what a team actually experiences, in either direction. The criterion that current comparisons skip is a separate question from "which agent is best," and it's the one that decides whether a software house can stand behind a delivery months later: for a given change, can someone point to what was requested, what the agent was allowed to touch, what it did, and proof that those three line up, independent of which agent happened to write the code that week.
That criterion doesn't replace how a team should choose an assistant for the job in front of it today, or the workflow differences a Claude Code versus Cursor comparison still has to settle. It sits above both choices. Detent Bench measures agents on exactly this line, from request to production-ready, rather than on capability alone.