Deep dive: do coding agents speed teams up?

Do coding agents make teams faster: what's real

My view as of October 2026: agents make writing code faster, and whether a team ships faster depends on its tests, its review capacity and the size of its changes. The best causal evidence is from the autocomplete era; for 2026 agents inside companies there isn't any, and the numbers that circulate are mostly self-reported. What the 2026 research does show is where agent work fails, and it fails at review and CI.

What the trials say

• Autocomplete era: the largest field trial, across 4,867 developers at Microsoft, Accenture and a Fortune 100 company from 2022 to 2024, found about 26% more completed tasks with Copilot, with build success about unchanged.
• Experienced developers, early 2025: METR's July 2025 trial with 16 maintainers of large open-source projects found they were 19% slower with AI tools, while they felt 20% faster.
• 2026: METR changed its design in February 2026 after its follow-up became unreliable (developers wouldn't do tasks without AI, so the sample skewed). Its 2026 outputs are a self-report survey, where 349 technical workers put the value at a median 1.4-2 times, and a methods paper. DORA's 2025 report found AI linked to both higher throughput and higher instability and called AI "an amplifier" of the existing system; it had no 2026 AI report by October 9.

The longer version of this evidence, and how to roll the tools out, is in Becoming AI native.

What agent pull requests show

The best 2026 data comes from public GitHub, so it's open-source-heavy, and I'd expect company repos to differ.

• Merge rates: about 65% for agent pull requests against about 85% for human ones, adjusted for repo and task, flat across four quarters. The adjustment matters: raw and adjusted rates for the same agent differ by 15 points or more, so I wouldn't read the per-agent figures as a league table.
• How they fail: reviewer abandonment is the most common pattern, and failed agent pull requests are bigger, touch more files and fail CI more often. Another study found that more review discussion raises the odds of merging for human pull requests and lowers them for agent ones. A May 2026 preprint found only about a third of rejections reflected a clear agent mistake; another third came from the project's own workflow rules, and the rest had no visible reason.
• What lands: CI and build changes, workflows, dependency bumps, docs and typos merge 80% of the time or more. New functions and code that calls language models merge least.
• After the merge: merged agent pull requests look no more defect-prone than merged human ones, which suggests review is doing its job as a filter. That compares only what survived review.
• Who carries it: in a 2025 study of open-source projects after Copilot, core developers reviewed 6.5% more code and wrote 19% less of their own.

What companies report

The strongest company results come from the strongest pipelines. Stripe's in-house agents open more than 1,300 pull requests a week, every one human-reviewed, and only after CI passes. Spotify reports more than 1,500 merged agent pull requests and 60-90% time saved on migrations, on top of a system that already automated about half its pull requests before AI. Google's 2025 paper on migrations found 80% of the code in landed changes written by AI and about half the total time saved, review included, with review and rollout as the limit.

The labs' claims about their own code are bigger and say less. Google says 75% of its new code is AI-generated, Anthropic says more than 80% of the code merged in May 2026 was written by Claude, OpenAI says nearly all its employees use Codex, and Microsoft said in 2025 that AI wrote 20-30% of the code in its repos. None of them publishes a change failure rate or incident count next to the share. Amazon reportedly went the other way in March 2026, adding a human gate with senior sign-off, and critics quoted in the press said that gives back much of the speed.

What benchmarks can and can't tell you

Model capability is still climbing fast. On SWE-bench Pro's public set, the top score reportedly rose from 23% to 80% in eight months, and METR estimates the length of task an agent can finish doubles about every seven months. The benchmarks themselves are breaking down:

• SWE-bench Verified: OpenAI stopped reporting it in February 2026 after an audit of 138 of the hardest tasks found 59% had flawed tests or specs, and some models reproduced the original fixes from memory.
• SWE-bench Pro: OpenAI withdrew its recommendation in July 2026 after finding about 30% of the 731 public tasks broken, and Artificial Analysis had already dropped it in June.
• What they score: a model and its harness together, graded on whether hidden tests pass, which says nothing about cost, safety or whether a maintainer would merge the change.

So I'd build a replay set from our own closed issues, with tests the agent can't edit, and measure merges after human review and reverts within two to four weeks. Throughput only counts if stability holds, and usage figures (seats, tokens, share of code written by AI) become theatre once they turn into targets.

Questions to ask a vendor

1. Of the agent pull requests your customers opened last quarter, what share merged after human review, and what share was reverted within a month?
2. What can your agent reach while it works: network, secrets, which repos and branches, and production?
3. Where do the session logs live, for how long, and can we export them?
4. Which of your agent's actions can be undone, and how?
5. What do you charge when a task fails or loops, and what caps can an admin set?