JetBrains Research #
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[Agentic AI](/research/category/agentic-ai/)
[AI](/research/category/ai/)
“100% of my code is written by [insert whichever AI coding agent is popular right now]!” You’ve probably heard this claim many times this year.
We wanted to find out how many developers have actually fully outsourced code writing to agents and how the share of agent-generated code differs across regions, tech stacks, and seniority levels.
Luckily, our large-scale, globally representative Developer Ecosystem Survey 2026 gave us the perfect opportunity to uncover the trends. In May–July 2026, we asked over 15,000 professional developers worldwide:
“What percentage of the code that you produced last month for work was …?
- Fully generated by AI agents
- Written by you with some AI assistance
- Fully written by you without any AI assistance”
The answer options were: 0%, 1%–20%, 21%–40%, …, 81%–99%, 100%, and I don’t know.
Here is what we found:
Expectedly, manual coding is disappearing quickly, but not everybody has made the leap to a fully agentic development workflow yet.
On average, professional developers report that:
~47% of their code is fully written by agents.- ~38% is written with some AI assistance.
- ~27% is written fully manually.
However, adoption varies wildly across the board.** Over half of all developers now write less than 20% of their code manually**, and one in five writes literally zero code without AI help. At the same time, the group that relies almost entirely on coding agents (over 80% agent-generated code) remains a minority of around 22%. Most developers are sitting somewhere in the middle.
Agentic coding by professional experience #
Interestingly, senior developers are among the first to hand coding over to agents. About a quarter of senior developers generate the vast majority of their code (over 80%) using agents, compared to a smaller fraction of juniors who tend to lean more toward AI-assisted workflows rather than fully agentic coding.
That said, not all seniors are agentic-first yet. Adoption varies widely within this group. See the charts below.
Agentic coding by most-used AI coding tools #
About 32% of developers who report Claude Code as their most-used AI coding tool generate over 80% of their code with agents.
Interestingly, among developers who use Codex, this share is notably higher at 42%. The share of developers who don’t write code without AI assistance at all is 37% among Codex users, which is tangibly higher than among users of other AI coding tools.
Our interpretation is that while Claude Code is increasingly becoming the mainstream AI coding tool (already the de facto standard, with 39% adoption at work), its audience no longer consists predominantly of advanced users of agents. Codex, on the other hand, is catching up in terms of awareness and adoption, and its user base might have more advanced users seeking better value for money (Codex has historically offered higher quotas).
Cursor users are similar to Claude Code users – on average, 58% of their code is agent-generated, and for 28% of its users, coding agents generate over 80% of their code.
Agentic coding by main programming language #
There is a clear split in agentic coding adoption by tech stack**. Developers with Go, JavaScript, and TypeScript as their main programming languages report the highest shares of agent-generated code**, averaging 54%–55%.
On the other end of the spectrum, C and C++ developers remain the least agentic, maintaining a much higher proportion of manually written code – 38% on average.
Java and Python developers sit in the middle with 48%–51%.
Agentic coding by regions #
The geographic differences are remarkable. Developers across East Asia, particularly in China, Japan, and South Korea, are leading the charge in agentic coding: About twice as many developers there (32%–35%) generate the vast majority of their code (over 80%) with agents, compared with around 16% of developers in Europe and the UK.
Segments of developers by AI usage #
We were curious whether developers could be grouped into homogeneous segments based on how they write code today. Three distinct profiles emerged:
See the methodology notes for more details on how we did this.
We deliberately use the word “coders” here to highlight that this is about the code generation process, not development as a whole.
Agentic coders (~31% of developers) mostly write code with agents nowadays:
- On average, 84% of their code is fully agent-generated.
- ~15% is written with some AI assistance.
- ~6% is written manually without AI at all.
Despite being the locomotives of agentic coding, only 46%–** 57% of heavy users of Claude Code and Codex are agentic coders.**
AI-assisted coders (~47% of developers) haven’t gone full agentic yet, but already write a large portion of their code with agents. They still prefer an AI-assisted development workflow and don’t hesitate to write some code manually if needed:
- On average, 40% of their code is fully agent-generated.
- ~60% is written with AI assistance.
- ~20% is written manually.
Manual coders (~23% of developers) write most of their code manually, though they use AI sometimes, mostly in an AI-assisted rather than fully agentic manner:
- On average, ~10% of their code is agent-generated.
- ~25% is AI-assisted.
- ~75% is manually written.
Whether you are all-in on agentic workflows or prefer hands-on coding, the shift is clear: Purely manual coding is quickly becoming a thing of the past.
In our previous blog post, based on the same survey data, we explored trends in AI coding agent adoption across the industry and the main players in this market.
We plan to share more materials on agentic development from the Developer Ecosystem Survey 2026 with the community soon.
Stay tuned and subscribe to JetBrains Research blog updates below!
Methodology notes #
Unrealistic responses – where the sum of the lower bounds of selected answers exceeded 150% or the sum of the upper bounds of selected answers fell below 80% – were not used in this analysis. This filter was applied on top of the regular data-cleaning filters used for Developer Ecosystem Survey data.
We used the midpoint of each answer bucket (0%, 10.5%, 30.5%, 50.5%, 70.5%, 90%, 100%) to **calculate averages. **The averages across the three categories of how code is written within the same group (e.g. seniors) could exceed 100% because of the bucketed nature of the answers, and respondents’ self-reports may not always be fully accurate.
We employed hierarchical cluster analysis with the Ward method and Euclidean distance on unstandardized bucket midpoints (e.g. 0%, 10.5%, 30.5%)** to clusterize developers into homogeneous segments** based on how they write code.
In this report, “professional developers” refers to respondents who reported being involved in coding or programming in any of the following job roles:
- Developer / Programmer / Software Engineer
- AI / ML Engineer
- DevOps Engineer / Infrastructure Developer
- Architect
- Data Scientist / Data Engineer / Data Analyst
- QA Engineer
Roughly 90% of the sample falls into the Developer / Programmer / Software Engineer job category.
The Developer Ecosystem Survey is localized into eight languages: English, Spanish, Chinese, Japanese, Korean, German, French, and Portuguese. We apply quotas on the required number of responses by region to help achieve accurate global representation. The quotas are proportionate to the number of developers in each region, based on estimates by our Data Science team. The detailed methodology of these estimates is described here.
The Developer Ecosystem Survey has been statistically reweighted to better represent the global developer population by region, employment status, programming language, and familiarity with JetBrains products (to avoid data skewed toward an excessively JetBrains-familiar audience). You can read about the weighting methodology for the Developer Ecosystem Survey here.