A study involving 340 developers across 12 teams reveals that 71% of organisations do not achieve a return on investment from coding agents, despite speed improvements of 20-30% in successful cases. The cost of reverse engineering has plummeted.
71% of organisations that have implemented coding agents in 2026 do not report a significant return on investment (ROI), according to a study involving 340 developers from 12 engineering teams. This figure contrasts with the productivity promises surrounding these tools, but the report reveals a bimodal pattern: those who integrate the agents systematically do achieve tangible gains, while the majority fail to capture value due to a lack of proper processes.
Speed and Reduction of Boilerplate: Metrics That Improve
Teams that implemented coding agents in a structured manner report speed improvements of 20-30% in development. The time spent writing boilerplate code, that repetitive and mechanical part, was reduced by 73%: it dropped from 30% to 8% of the developer's total time. Test coverage also improved, rising 19 percentage points to a median of 81%.
The time to reach the first pull request for new features was reduced by 44%, from 3.2 to 1.8 days. And the code review time decreased by 30%, from about 2 hours per PR to 1.4 hours. The bug rate in production improved by 18% compared to the baseline. These figures, on paper, would justify the investment, but the reality is more complex.
Three Levels of Agents and the Rise of Reverse Engineering
The ecosystem of coding agents has been divided into three clear categories in 2026. Level 1 consists of editor assistants like GitHub Copilot and Cursor, which represent 90% of daily coding work. Level 2 includes conversational assistants like Claude Code or Copilot Chat, used for refactoring and debugging. Level 3 comprises autonomous agents like Devin or GitHub Copilot Workspace, capable of writing code, running tests, and opening pull requests autonomously for well-specified tasks.
Simultaneously, a specialised category of reverse engineering agents has emerged that automate the analysis of binaries and legacy code without original source. Tools like Agentic Reverse Engineering (presented at REcon 2026), LibGhidra + GhidraSQL, or auto-re-agent enable reasoning at the system level using program graphs. Simon Willison notes that as the cost of writing code has drastically fallen, the psychological burden of long-term maintenance decreases: developers can experiment with automations without the anxiety of who will maintain it in six months.
The Bottleneck Shifts to Design and Validation
For founders and CTOs of startups, the change is profound. The barrier to automating devices, integrating legacy APIs, or modernising legacy systems is no longer the development cost, but the quality of design and human oversight. The ROI of automation projects has permanently changed, but only for those who know how to integrate these tools into their processes.
The study warns that adoption follows a bimodal pattern: organisations with systematic processes see gains of 20-30% in speed, while the remaining 71% fail to capture value due to inadequate integration. The bottleneck shifts from development to design and validation, according to Willison. For readers interested in implementing coding agents, the key lies in not focusing solely on the tool, but in redesigning the workflow and human oversight.
In practice, a developer can now reduce boilerplate writing time from 3 hours to less than 1 hour per day, freeing up capacity for higher-value tasks. But without a clear adoption strategy, those gains dissipate. The study recommends starting with level 1 assistants, measuring the impact over two months, and only then scaling to autonomous agents.
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