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The average professional reports 47 percent of last month's code fully agent-generated, 38 percent assisted and 27 percent manual. Only 22 percent live above the 80 percent line.
The Engineer · Build desk
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Add the three averages together and you get 112 percent of a month's output [16]. The survey put fully agent-generated, AI-assisted and fully manual code as three separate questions, answered in bands running 0 percent, 1-20 percent and upward to 100 percent [1][22]. Code a developer counts twice, once as agent output and again as assisted because they went back and edited it, lands in that 12 points of overshoot. So 47 percent is not a measured share of a repository. It is the midpoint of a self-reported band, and the bands do not tile.
The heavy end can be counted two ways, and JetBrains counted it both. Draw a line at 80 percent agent-generated code and 22 percent of respondents sit above it [5]. Cluster developers by behaviour instead and the agentic coder segment comes out at 31 percent, averaging 84 percent agent-written code [15]. That is a 9-point spread between two defensible readings of one dataset [19], which is roughly the size of most of the differences the release goes on to report.
Tool comparisons carry a selection problem that JetBrains names itself. Among developers whose most-used tool is Claude Code, 32 percent are above the 80 percent line; among Codex users it is 42 percent, and 37 percent of Codex users say they write no code at all without AI [7][8]. Cursor users average 58 percent agent-generated, 11 points above the overall figure [10][17]. JetBrains' own interpretation is that Codex has historically offered higher quotas and so draws advanced users looking for value, while Claude Code, at 39 percent adoption at work, has grown a mainstream audience [9][7]. On that reading the 10-point gap between Codex and Claude Code [18] is a fact about who quota pricing recruits, not about which agent writes more code.
The stack numbers behave more like a constraint than a preference. Go, JavaScript and TypeScript developers average 54 to 55 percent agent-generated code, seven to eight points above the overall average [11][20], while C and C++ developers still write 38 percent of theirs by hand [12]. Java and Python fall between [13]. Seniority runs against the usual assumption: about a quarter of senior developers are above the 80 percent line, ahead of juniors, who lean toward assistance rather than handing the work over [6].
The widest single split is geographic. Between 32 and 35 percent of developers in China, Japan and South Korea are above the 80 percent line, against about 16 percent in Europe and the UK [14]. So the fully agentic workflow is real, and it is roughly one respondent in five [5]. A plan that treats it as next quarter's baseline is extrapolating from the tail of a distribution whose own totals overrun by 12 points [16].
Ranked by verification strength, evidence, and original report placement.
JetBrains' Developer Ecosystem Survey 2026 asked over 15,000 professional developers worldwide, in May-July 2026, what percentage of the code they produced last month for work was fully generated by AI agents, written by them with some AI assistance, or fully written by them without any AI assistance. JetBrains describes the survey as large-scale and globally representative.
The answer options for each of the three categories were 0%, 1%-20%, 21%-40%, and so on through 81%-99%, 100%, and 'I don't know'.
About a quarter of senior developers generate over 80% of their code using agents, compared with a smaller fraction of juniors, who lean more toward AI-assisted workflows than fully agentic coding.
JetBrains identified three profiles, agentic coders, AI-assisted coders and manual coders; agentic coders are about 31% of developers, and on average 84% of their code is fully agent-generated with about 15% written with some assistance.
JetBrains framed the study around a claim it says developers have heard many times this year: that 100% of a given developer's code is written by whichever AI coding agent is currently popular.
Over half of all developers now write less than 20% of their code manually.
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Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Large single-vendor self-report with a visible accounting flaw
The underlying instrument is unusually large (over 15,000 professional developers, fielded May-July 2026) and the question wording, answer bands and segment definitions are partly published, which is more disclosure than most agentic-coding statistics carry. But every number in the cluster comes from one interested publisher, the measure is a developer's recollection of last month's code share captured in 20-point bands rather than any telemetry, the three headline averages sum to 112% of a month's output, the disclosed cleaning rule tolerates responses whose bounds sum between 80% and 150%, and the methodology notes are truncated in the published text. There are no per-cut sample sizes or intervals, so cross-tabs by tool, language and region cannot be checked for precision.
Broad shallow adoption, minority fully agentic
On self-reported data, agent involvement in professional code is close to universal rather than fringe: over half of developers write under 20% of their code manually, one in five write none unassisted, and the average fully agent-generated share is ~47%. Tool-level penetration is concrete too, with Claude Code at 39% workplace adoption. What is not broadly adopted is the fully agentic mode the popular narrative describes: only ~22% of developers sit above the 80% agent-generated line (~31% by behavioural segment), and even among heavy Claude Code and Codex users just 46%-57% qualify as agentic coders. Adoption is also uneven, concentrated in East Asia and in Go/JavaScript/TypeScript work and thinnest in C and C++.
Ambient '100% agent-written' talk overstates a 22% reality; the corrective is itself loosely measured
The dominant public claim the post targets - that a developer's code is now 100% written by whichever agent is fashionable - is clearly overstated relative to the data: heavy agentic use is a 22% minority by threshold, 31% by segment, and even self-described heavy users of the leading agents are only 46%-57% agentic coders. That is a real positive gap between narrative and measurement. The gap is not larger because the corrective evidence is itself soft: self-perceived shares in wide bands, averages that sum to 112%, and a vendor's unvalidated interpretation of why Codex users skew heavier. So the story deflates a specific overstatement without establishing a precise replacement figure.
Vendor research marketing on its own survey
The single source is the research blog of JetBrains, a commercial developer-tools vendor publishing its own annual survey; the post promotes a prior instalment, previews further releases and solicits blog subscriptions. JetBrains has a direct commercial interest in how the agentic-coding transition is characterised and in being the reference point for developer-ecosystem statistics, and it also volunteers interpretation of rival coding tools' user mixes. The incentive is mitigated by the fact that the headline finding cuts against the maximalist agent narrative rather than flattering it, and by partial methodology disclosure - but no independent publisher in the cluster checks any of it.
Directionally credible, numerically loose, single-publisher
Confidence is moderate: the direction of travel - agent-assisted work is now the norm while fully agentic work is a minority concentrated among seniors, Codex/Cursor users, Go/JS/TS stacks and East Asian developers - is supported by a large disclosed sample and is internally consistent across several independent cuts. Confidence in the specific percentages is materially lower because of the 112% overshoot, wide answer bands, self-report without telemetry, truncated methodology, absent per-cut sample sizes, and the absence of any second publisher or dataset in the cluster.
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1 article · August 26, 2026