Agents — Analysis of JetBrains 2026 Developer Ecosystem Survey
The landscape of AI‑assisted software development is undergoing a critical paradigm shift. For years, AI coding tools were mostly known for inline code completion and short snippet generation embedded within Integrated Development Environments (IDEs). Today, AI coding agents are reshaping developer workflows. Instead of merely generating small blocks of code, modern AI agents can read entire code repositories, perform iterative modifications, run test suites, and finish end‑to‑end development tasks autonomously.
JetBrains Research recently released its 10th‑annual 2026 Developer Ecosystem Survey, collecting valid responses from more than 15 000 professional developers globally between May and July 2026. Respondents cover software engineers, DevOps specialists, ML engineers, system architects, QA engineers and other technical roles. The survey delivers quantifiable metrics about real‑world adoption rates, tool awareness, and behavioural changes within AI‑powered coding workflows. According to the collected statistics, up to 90 % of professional developers leverage AI coding agents at least on a weekly basis. Claude Code has achieved explosive growth in workplace adoption and overtaken GitHub Copilot as the most‑used AI coding tool in corporate settings. Meanwhile, open‑source agent projects and competing commercial solutions are rapidly gaining market traction, shifting industry competition from IDE‑embedded code completion to full‑cycle agent execution capabilities. When organisations manage multiple large‑model endpoints for agent workloads, an API gateway such as 4sapi can simplify authentication, routing and protocol adaptation across heterogeneous model backends.
Surging Workplace Adoption: Claude Code Takes the Leading Position
One of the most striking findings from JetBrains’ dataset is the rapid expansion of Claude Code usage. In January 2026, merely 18 % of surveyed professionals reported using Claude Code for work‑related programming tasks. By the May‑July 2026 survey wave, this figure jumped to 39 %, representing more than doubling of workplace penetration within half a year. In the United States market specifically, its workplace adoption reaches 47 %, meaning nearly half of local developers rely on this tool for daily coding work.
For comparison, GitHub Copilot had long dominated enterprise AI‑coding adoption. From mid‑2025 to early‑2026, its workplace usage stayed stable between 29 % and 31 %. During the same May‑July 2026 period, GitHub Copilot’s workplace adoption dropped to 21 %. Statistically, Claude Code’s workplace usage is now nearly twice that of GitHub Copilot.
It is worth distinguishing actual workplace adoption rate from public awareness rate. Both tools maintain comparable awareness levels at 79 % among all respondents. Awareness reflects whether developers have heard of a product, while workplace adoption captures real‑world usage on production assignments. The gap between these two metrics indicates that Claude Code is converting awareness into practical work‑environment usage much faster than its rival.
OpenAI Codex also demonstrates impressive momentum. In January 2026, only 3 % of developers used Codex in their jobs. Within six months, this number climbed to 16 %, marking roughly a five‑fold increase. Its awareness metric grew even more dramatically: from 27 % in January 2026 up to 65 % in mid‑2026. This trend illustrates OpenAI’s brand advantage translating into growing real‑world adoption for its agent‑centric coding product.
Cursor presents a contrasting trend. Its workplace adoption fell from 18 % in January 2026 down to 12 % in the May‑July round, even though general awareness rose from 69 % to 75 %. The Chinese developer community shows a similar pattern: self‑reported workplace usage dropped from 28 % to 16 % over the same timeframe. This change signals a meaningful industry transition. Previously, AI coding competition centred heavily around AI‑enhanced IDE editors. Developers expected the editor itself to deliver AI assistance via inline completion and dialogue panels. Increasingly, practitioners demand standalone agent capabilities that can traverse codebases, apply multi‑file edits, execute tests and keep iterating until tasks are complete. Pure IDE‑centric tools are losing ground to powerful agent workloads.
90 % of Professional Developers Are Using AI Coding Agents
Beyond individual tool market shares, the survey exposes a fundamental shift in developer daily workflows. Across the global sample, 90 % of professional developers use AI coding agents at least once every week, and 68 % run agent‑driven tasks on a daily basis. These figures confirm that AI agents are no longer experimental curiosities for early‑adopter hobbyists. They have become mainstream infrastructure embedded within standard software‑development routines.
JetBrains defines AI coding agents broadly. The scope covers both local‑running agents deployed on developer workstations and remote agent services hosted on cloud servers. The core behavioural change is clear: AI is evolving from “assisting to write partial code snippets” toward “executing complete development assignments”. Where developers once wrote most logic manually and used AI for supplementary suggestions, many engineers now hand over complex multi‑step tasks to agent systems. This transition reshapes job responsibilities: human engineers focus more on requirement definition, result review and architecture decision‑making, while agents handle implementation‑heavy repetitive work.
Open‑Source Coding Agents Join the Market Competition
Commercial offerings are not the only area of growth. Open‑source agent frameworks are gaining meaningful mind‑share and real‑world usage. OpenCode, an open‑source coding agent, records a 7 % workplace adoption rate in the May‑July survey, alongside 42 % overall awareness among respondents. Google Antigravity also shows fast progress: workplace adoption hit 6 %, with awareness expanding from 29 % in January 2026 to 47 % mid‑year. Regional variance is prominent; adoption in the India developer community reaches 15 % for this product.
JetBrains’ own built‑in AI assistant suite (JetBrains AI and Junie) holds approximately 9 % workplace adoption. Collectively these statistics demonstrate a fragmented but vibrant competitive landscape. No single solution monopolises the agent‑coding space. Developers evaluate multiple tools and select different agents according to task complexity, privacy constraints, cost budgets and self‑hosting requirements. Open‑source options give teams extra flexibility for on‑premises deployment and custom modification, which attracts enterprises with strict data‑security rules.
The New Competition Dimension: Agent Execution Capability
The survey data paints a clear picture: competition in AI coding has moved past simple code‑completion benchmarks. Product comparisons are no longer limited to how well a model generates short inline snippets. The decisive battlefield lies in agent capability — whether a system can reliably carry out full‑cycle software‑engineering assignments.
Historical AI‑coding evaluations prioritised token‑level code generation accuracy. Modern agent‑oriented workloads require additional abilities: repository context loading, multi‑file modification planning, error self‑correction, test invocation, and looped iteration until objectives are satisfied. Even high‑quality base LLM models may underperform in agent scenarios without specialised prompt engineering, tool‑call logic and state‑management layers.
High weekly and daily agent adoption rates (90 % and 68 % respectively) prove that developers are ready to delegate substantial work to agents. This creates new engineering challenges for platform builders. Teams running multiple agent backends often need unified routing, rate‑limiting, logging and credential management. Centralised API gateway tooling helps streamline operations across mixed proprietary and open‑source model deployments.
Looking forward, future market winners will not merely be models good at writing isolated code fragments. The winning solutions will be those agent systems that can reliably understand complex business requirements, navigate large real‑world code bases, recover from runtime mistakes, and deliver usable finished outputs with minimal human intervention. IDE integration remains important, yet it becomes one component of a larger agent‑driven workflow rather than the core differentiator.
Conclusion
JetBrains’ 2026 Developer Ecosystem Survey captures a decisive turning point for AI‑assisted software engineering. Claude Code’s rapid workplace‑adoption growth demonstrates that agent‑first coding products can swiftly displace long‑established code‑completion tools. OpenAI Codex and open‑source agent projects are expanding their footprint, while traditional AI‑IDE products face shifting user expectations.
With 90 % of professional developers using AI coding agents weekly, agent‑based workflows have firmly entered mainstream software‑development practice. Industry rivalry is shifting from inline code completion toward end‑to‑end task execution. Going forward, the competitive edge will belong to agent systems that combine solid foundational model quality with robust tool‑calling, self‑correction and repository‑aware reasoning. Engineering teams building agent‑powered stacks need to weigh commercial closed‑source solutions alongside configurable open‑source alternatives, balancing performance, cost, compliance and self‑hosting needs.
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