Agentic coding and persistent returns to expertise

Source: Anthropic · 2026-06-16

Building on prior work, we introduce a framework for studying interactive agentic coding based on a privacy-preserving analysis of ~400,000 Claude Code sessions from between October 2025 and April 2026. We evaluate the composition of tasks, human-AI collaboration, and success rates.

In a typical session, people make most of the planning decisions (what to do) and Claude makes most of the execution decisions (how to do it). The greater domain expertise a person brings to a session, the more work Claude does per instruction. On coding tasks, every major occupation succeeds––accomplishes what the person set out to do, with verifiable evidence like passing tests or committed work––at nearly the same rate as software engineers, on average.

The more domain expertise a person has, the more often the session ends in success—though the gap between intermediate and expert users is modest. Over the seven months we observe, the share of sessions spent debugging fell by nearly half, and usage shifted toward more end-to-end agentic use: deploying and running code, analyzing data, and writing non-code documents.

Over those seven months, the value of the typical task, which we estimate through a comparison to freelance job postings, rose in almost every kind of work—about 25% on average.

This report provides evidence on how Claude Code is used in practice, based on a privacy-preserving analysis of ~400,000 interactive sessions from ~235,000 people between October 2025 and April 2026. It builds on prior work focused on measures of autonomy in Claude Code sessions, and how Claude Code is changing work at Anthropic.3 Here, we introduce a framework for describing interactive AI coding-assistant usage: what kind of work is being done, who is doing it, and whether it succeeds. We focus on Claude Code usage through a command-line interface (CLI), Claude.ai, or the Claude Code desktop app.4 By tracking how agentic coding usage changes as models get more capable, we can better understand how these tools affect the labor market for coding professionals and knowledge workers.

We also see evidence that domain expertise, and not coding proficiency, amplifies effective use of the tool. In particular, domain experts succeed more often, and more easily recover from errors and misunderstandings. However, the gap between experts and intermediates is modest—suggesting that proficiency in a domain is enough to use the tool almost as effectively as those with deep mastery.

These findings give us an early read on possible transitions in the labor market. In our data, success is determined by how well a person understands the problem they are trying to solve, not whether they’re trained in coding. If these patterns hold across the economy, it suggests that while agentic coding tools may be absorbing some implementation-heavy work, they are also rewarding those with firm understanding of the problems they solve on the job. Coding agents are not substituting for domain expertise—the more understanding a worker brings to an agent, the more quality work the agent is able to do.

About 56% of sessions consist of writing (25%), fixing (26%), or testing and orchestrating code (5%). Operating software comprises 17%, while 14% of sessions are planning or exploring, and 13% produce analysis or prose (Figure 1).

On average, people make about 70% of the planning decisions but only 20% of the execution decisions (Figure 2). In practice, there is a clear division of labor in agentic coding––people decide what to build, and the agent decides how to build it.

We quantify how expertise relates to Claude’s output and activity per prompt. In typical novice sessions, each prompt sets off about five Claude actions and roughly 600 words of output, while expert sessions set off action chains more than twice as long (12 actions) carrying five times the output (3,200 words) (Figure 3). This gap between novice and expert sessions appears within every kind of work and every band of task value.

We were able to infer occupation in about 70% of sessions. Within this set, Computer and Mathematical Occupations, a category which encompasses most software-related jobs, is unsurprisingly the largest group. The next largest are Business and Financial Operations; Arts, Design, and Media; Management; and Life, Physical, and Social Sciences. The fastest-growing non-software occupation groups in our sample are management, sales, and legal occupations.

The composition of the work done with Claude Code changed substantially between October 2025 and April 2026. The clearest change is that the share of sessions spent fixing broken code fell from 33% to 19% (Figure 4). In its place, we saw a greater share of the work that surrounds code. Operating software grew from 14% to 21% of sessions. Writing and data analysis roughly doubled, from about 10% to 20% of sessions.

The tasks themselves also grew more valuable. We approximate each session's economic value by asking what the work would cost on a freelance marketplace, calibrated against a public dataset of real postings. By this measure, the estimated value of the average session rose by 27% between October and April. The rise holds across many kinds of work. Building, operating, and fixing-type tasks all grew more valuable by roughly a third or more (about 43%, 34%, and 32% respectively). These price estimates are coarse, so we use them primarily to compare tasks to one another over time, not as dollar values to be read literally.8 For details about the construction of the task estimator, see the Appendix.

Across all of our success measures, the more expertise a person exhibits in a session, the more likely it is that the session succeeds. A novice-rated session reaches our strictest measure, verified success, 15% of the time and at least partial success 77% of the time. A session rated intermediate or up reaches verified success 28-33% of the time and partial success 91-92% of the time (Figure 5).

In each measure, most of the gain comes from moving from novice to intermediate; between intermediate and expert, the slope decreases. In the Appendix, we give details about the regressions behind Figure 5.

A similar gradient appears in sessions that run into challenges along the way. We say a session hits trouble when the failure signal records verified evidence of failure. This could be an error, a failed test, multiple attempts to do the same thing, or the user expressing frustration or dissatisfaction. Among sessions that hit trouble, the share that are verified successes rises from 4% for novice-rated sessions to 15% for expert-rated ones, accounting for all the controls described above (Figure 5). Looking at the looser measures, we find that the share of at least partial success is 60% for novice and 80-81% for intermediate through expert sessions.

We also track the inverse relationship––expertise versus various measures of failure. Note that in this analysis, the sessions judged as failures are those that do not even partially succeed. We say a troubled session is abandoned if it is judged as failed and zero lines of code are written: 19% of sessions where the user appears to be a novice end abandoned, against 5-7% for everyone else. In other words, the least experienced users are more likely to give up when they are struggling to get the outcome they are after.

People in software-related occupations reach verified success in about 30% of their sessions overall, while users from other professions reach verified success about 26% of the time. Among sessions that produce code (i.e., sessions that add or modify at least one line of code), those numbers are 34% and 29% respectively (Figure 6). The gap between software-related occupations and other occupations narrows under our looser definition of success––with both groups reaching at least partial success in code-producing sessions 89% and 88% of the time, respectively. That five-point gap is small, and it has neither widened nor narrowed over seven months, even as the success rates in both groups increased. In code-producing sessions, every one of the ten largest occupations in our dataset lands within seven points of software engineers in terms of their success. Management occupations are highest on verified success, slightly above the software engineering occupations. Their higher verified success rates may reflect management skills that transfer to directing an agent.

The results in this report offer an emerging picture of how agentic coding amplifies some forms of knowledge and skills, while substituting for others. In sessions that produce code, every major occupation succeeds at rates within a few points of those in software-related occupations. It appears that coding agents are making a coding background less relevant to successful programming.

At the same time, successful sessions are more likely to exhibit domain expertise.