mlsanityData to 14 Oct 2026

Plans They Abandon, Reports They Author: The Narrative Layer of Autonomous Agents

arXiv 2609.12205 ↗First posted 10 Sep 2026cs.HCcited by 3

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

When a coding agent finishes a task, the developer reviews a summary the agent wrote about itself, not a display someone designed. We ask how much of the agent's work that summary carries, and whether it drifts toward the plan the agent stated when execution departed from it. Across 5,851 real developer sessions and 355,942 tool calls, a self-report referred to about one action in eleven, and a reader working from the report alone recovered roughly a fifth of the action log. Neither figure depended on whether the session later needed human correction. Reports did not generally resemble the stated plan more than the executed one, but they did so increasingly as execution diverged from the plan. We hand-validate both measurement steps that use a language model, report the one that failed alongside the one that passed, and draw conclusions only from measures that survived.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 26 of its 38 references that are in the map. First posted on arXiv 10 Sep 2026.

Builds on

26 of 38 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents3 Jun
  2. 2026How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions28 May
  3. 2026SWE-chat: Coding Agent Interactions From Real Users in the Wild22 Apr
  4. 2026"I'm Not Reading All of That": Understanding Software Engineers' Level of Cognitive Engagement with Agentic Coding Assistants15 Mar
  5. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 21

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  6. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  7. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  8. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  9. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  10. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  11. 2023How is ChatGPT's behavior changing over time?cited by 270
  12. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 87
9 more
  1. 2024Visibility into AI Agentscited by 79
  2. 2024The illusion of artificial inclusioncited by 34
  3. 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
  4. 2026Comparing Human Oversight Strategies for Computer-Use Agentscited by 6
  5. 2026LEDGER: Claim-to-Evidence Trace Graphs for Auditing LLM Agentscited by 5
  6. 2026Overseeing Agents Without Constant Oversight: Challenges and Opportunitiescited by 3
  7. 2026TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agentscited by 3
  8. 2026Artifact-centered Claim-aware Observability for Autonomous Scientific Agentscited by 3
  9. 2026HANSEL: Extracting Breadcrumbs from Web Agent Trajectories for Interactive Verificationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Confident and Wrong: Silent Semantic Failures in Coding Agents
  2. 2026Do AI Coding Agents Log Like Humans? An Empirical Study
  3. 2026Coding Agents Don't Know When to Act
  4. 2026From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
  5. 2026Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
  6. 2026Beyond Resolution Rates: Behavioral Drivers of Coding Agent Success and Failure
  7. 2026The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?
  8. 2026Who Holds the Pen? Let Specifications, Not Agents, Sign Off