A descriptive model of agent-documentation interaction is derived as a two-lobed cycle rather than a linear journey, and it is shown that two widely assumed properties of"agent-friendly"documentation - actionability and verifiability - lack consistent behavioural support.
Abstract
Technical documentation is written for human developers, but an increasing share of software changes is now authored by autonomous coding agents. Which documents they consult, when, and what follows remain unknown. We conduct a behaviour-grounded study of agent-documentation interaction across two public datasets: 557 agentic coding sessions from SWE-chat, yielding 94,813 development events including 3,033 documentation interactions; and 33,097 agentic pull requests from AIDev, with 690,260 classified file-level change records. Four findings challenge current documentation practice. First, agents'documentation work is dominated by agent-facing artefacts: instruction files and working notes account for 60.5% of all documentation interactions, versus 10.6% for classical technical documentation and 1.3% for API references. Second, the link between consultation and code editing is unresolved: the adjacent transition probability is 0.002 and the unadjusted three-event lift 1.05, whereas a stage-adjusted model places it above unity (OR 1.33 [1.09, 1.62]); documentation creation is elevated unadjusted (lift 1.67) but its adjusted interval includes unity. Third, no explicit documentation-based validation sequence was observed, and consultation is associated with less immediate testing (lift 0.23, cluster CI 0.08-0.45; adjusted OR 0.39 [0.25, 0.60]). Fourth, consultation is self-initiated (70.2%) far more often than failure-driven (7.5%), and documentation trails code: among multi-commit pull requests changing both, code is touched first 4.7x more often. From these traces we derive a descriptive model of agent-documentation interaction as a two-lobed cycle rather than a linear journey, and show that two widely assumed properties of"agent-friendly"documentation - actionability and verifiability - lack consistent behavioural support. We release our pipeline, coding scheme, and event-level data.
Large-scale human–agent collaboration is investigated by comparing 40,214 pull requests across 2,807 GitHub repositories, including 33,596 agent-authored PRs from five autonomous coding agents and 6,618 human-authored PRs, revealing a socio-technical trade-off.
A. Njoku, Zohreh Sharafi, F. Khomh· AIware· 2 citations
This position paper uses systems thinking to trace how agent-mediated information seeking disrupts the balancing loops that currently contribute to documentation quality and how this disruption would create reinforcing loops that degrade documentation and code quality over time.
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This note argues that a skill is a software artefact and that its construction should follow software-engineering principles, with qualifications: single responsibility, separation of interface from implementation, low coupling, and economy in a shared token budget, together with behavioural evaluation in place of dete...
Overall, repository-preserved Agent Plans under these tool-specific directories appear to be a narrow but informative artifact for studying task intent and execution guidance in human-agent workflows.
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This work provides a bounded failure classification and empirical case study for white-box code auditing, and varies the surrounding context and classify where failures first become visible: lost requirements, editing drift, failed checking, or non-agent evaluator/runtime failures.
The results suggest that agentic-first reports benefit most from information that narrows the agent's search and repair space, and an ablation study removing selected information types confirms that agents benefit less from information traditionally useful to humans, and more from sentences that expose a repair directi...
Vincenzo Luigi Bruno, Alessandro Giagnorio, Daniele Bifolco et al.· 0 citations
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