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

Obada Kraishan, Kulsawasd Jitkajornwanich

arXiv:2609.12205v1cs.HC

TL;DR

Coding-agent completion messages are written by the agents whose behavior they describe, leaving open how much work those self-reports capture. The paper compares plans and reports with executed tool logs across real sessions; reports captured a small share of actions, while planward drift grew with execution divergence.

  • Problem

    Because agents author their own completion accounts, the paper asks how much executed work reports capture and whether they resemble plans when execution diverges.

    Source: §1 · pp. 1–2

  • Method

    The study compares prospective plans and retrospective reports with tool logs, measuring omission, recoverability, and whether planward resemblance rises with plan-execution divergence.

    Source: §1 · p. 2

  • Results

    About one action in eleven was referenced and readers recovered roughly a fifth of the log, regardless of later human correction; reports drifted toward plans increasingly as execution diverged.

    Source: §1 · p. 2, §9 · pp. 11–12

  • Takeaways & Limitations

    Reports track execution when agents follow their plans, but increasingly resemble plans as execution departs from them.

    Source: §9 · p. 12

    The evidence covers coding-agent sessions from one capture tool, with 82.9% using a single scaffold; non-coding replication was not achieved.

    Source: §8 · p. 11

Abstract

from arXiv · show

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.

1 Introduction

Developers increasingly rely on agents’ own completion messages, but those accounts are generated by the same process whose behavior they describe. This study tests what reports capture and whether they drift toward stated plans when execution diverges.

1 Introduction

  • The study asks how much of an agent’s executed work appears in its self-report and whether reports drift toward stated plans as execution diverges.It analyzes 5,851 real developer sessions from the SWE-chat corpus [8].Source: §1 · pp. 1–2
  • Omission measures executed actions not referred to in the report, recoverability measures how much of the log a reader could reconstruct, and the repair index tests planward resemblance as divergence grows.A positive repair index means a report resembles the plan more than execution.Source: §1 · p. 2
  • Self-reports referenced about one action in eleven and let readers recover roughly a fifth of the action log, regardless of whether sessions later required human correction.Narrative repair was conditional: reports tracked execution when agents followed their plans and drifted toward plans when they did not.Source: §1 · p. 2
  • The analysis aligns plans, reports, and tool logs on a shared action vocabulary, hand-validates both model-dependent measurement steps, and reports the failed check alongside the passed one.Conclusions are drawn only from measures that survived validation.Source: §1 · p. 2

2 Related Work

Related work spans trust and transparency in automation, how people engage with agent traces, claims about agent behavior, and language models used for measurement. This paper measures self-report content in real sessions and analyzes prospective plans alongside retrospective reports.

2.1 Transparency information and its author

  • Trust research treats appropriate reliance as depending on accurate information, typically delivered through designed displays or explanations [9] [10].Generative agents author both the account and the behavior it describes, complicating that assumption [12, 13, 14, 15].Source: §2.1 · p. 2

2.2 Agent traces and how they are read

  • Studies find agent traces cumbersome to verify, declining engagement over task progress, and developer oversight relying on spot-checks rather than full review [3] [4] [5].For computer-use agents, oversight strategy shaped exposure to problematic actions more than correction ability [21].Source: §2.2 · p. 2

2.3 Checking what agents claim

  • Prior work proposes evidence graphs, observability, interactive extraction, and provenance records to make agent claims checkable [23] [24] [25] [26].A prior coding-agent analysis identified seven misalignment types, including progress reporting [22].Source: §2.3 · pp. 2–3
  • This paper complements checking tools by measuring self-reporting in the wild and analyzing prospective plans alongside retrospective reports.It connects both narrations to tool execution and to trust calibration and situated action accounts.Source: §2.3 · p. 3

2.4 Language models as measurement instruments

  • Language-model measurement faces prompt, version, and nondeterminism risks; both model-dependent steps were checked against hand coding, and analysis uses the step that passed.The prompts are fixed and released verbatim, but nondeterminism was not addressed [27] [28] [29] [32].Source: §2.4 · p. 3

3 The Narrative Layer

The study compares the text agents produce about their work with the record of what they did. Plans and reports are narrations; tool logs provide a separate record for comparison.

3 The Narrative Layer

  • Each session leaves a stated plan, an executed sequence of tool calls, and a final self-report describing actions and results.Plans may come from prose or structured task lists; logs include reads, edits, and commands.Source: §3 · p. 3
  • Unlike plans and reports, the scaffold records the log as calls occur, so both narrations can be checked against a record the agent did not compose.This asymmetry makes comparison informative.Source: §3 · p. 3
  • The paper calls the two written accounts together the narrative layer and measures how they relate to the work.Unlike the log, this layer is written for the reader.Source: §3 · p. 3

4 Method

The method builds three action sequences per session from coding-agent records, then compares narration with executed tool calls. Corpus, mapping, segmentation, and extraction choices define those comparisons.

4.1 Corpus

  • The corpus contains 5,851 SWE-chat sessions from real developers, spanning multiple scaffolds and model versions.All claims concern coding agents; candidate non-coding corpora lacked agent prose or were not processed.Source: §4.1 · p. 4
  • The 355,942 tool-use turns supply executed actions, while assistant turns carry narration and structured task-list calls provide machine-readable plans where available.Tool calls map deterministically from tool names and commands to action types.Source: §4.1 · p. 4

4.2 A shared vocabulary of action types

  • Plans, reports, and logs are mapped to six shared action types, with tool calls mapped by tool name and command and text units by lexical cues.The categories are READ, WRITE, EXECUTE, NETWORK, ORCHESTRATE, and OTHER.Source: §4.2 · p. 4

4.3 Segmenting the narration

  • Turns are labeled prospective or retrospective by position and plan language, with each session’s final assistant response treated as its self-report.This produced 30,126 prospective spans and 5,450 self-reports; thinking turns appeared in only 670 sessions (11.5%).Source: §4.3 · p. 4

4.4 Extracting plan units and claim units

  • A pinned language-model snapshot and fixed prompts extract intended actions from plans and typed assertions from reports; 27.4% of prospective spans yielded a plan unit.The prompts request atomic actions in order and classify claims as ACTION, NON-ACTION, or OUTCOME; five of 35,411 outputs were unparseable.Source: §4.4 · p. 4
  • Structured task-list plans and prose plans are pooled because their agreement is low: per-session F1 was 0.275 (SD = 0.228) across 535 sessions containing both.The sources capture different planning horizons, so using either alone would measure divergence against only one; plan units retain provenance.Source: §4.4 · pp. 4–5

4.5 Aligning claims to actions

The paper aligns report claims with logged actions, then measures how much execution reports omit, what a reader can recover, and whether reports track plans or execution. The measures use sequence overlap and report-length containment to compare these records.

4.5 Aligning claims to actions

  • 45.2% of claim verdicts were rule-decided and 52.8% were decided by a model adjudicator after rule-first matching failed.File paths and commands are matched lexically; NON-ACTION claims are supported when no log entry touches the named target.Source: §4.5 · p. 5

4.6 The three measures

  • Divergence compares plan and log sequences using longest-common-subsequence overlap, while abandonment and emergent rates count planned action types not executed and executed types not planned.Divergence is zero when every planned step appears in the log in planned order and one when none does.Source: §4.6 · p. 5
  • Omission is the share of log entries unmatched to a report claim, and recoverability scores a report-only reconstruction against the true log using F1 over action-type counts.Each claim matches at most one action; omission depends on matched action indices, not the number of claims.Source: §4.6 · p. 5, §4.5 · p. 5
  • Repair uses report-length containment to compare the report's implied action sequence against the plan and log, avoiding the built-in penalty of symmetric similarity for the shorter report.The repair index is positive when the report is better contained in the plan, zero when both are equally good hosts, and negative when it tracks execution more closely.Source: §4.6 · p. 5

4.7 Recoverability probe

The recoverability probe asks a model to reconstruct action types from a report alone and compares that sequence with the session log. Analyses use measure-specific samples, with clustered standard errors for repository-level dependence.

4.7 Recoverability probe

  • A model given only each report reconstructed action types, scored against the true log by F1 over type counts across 5,289 sessions.The prompt instructed the model not to pad its list with actions a competent agent would plausibly have taken.Source: §4.7 · p. 5

4.8 Analytic sample

  • The divergence and repair analyses include 2,646 sessions with a recovered stated plan, excluding sessions without narrated plans in a non-random way.That exclusion is identified as a limitation.Source: §4.8 · p. 5
  • Each measure uses sessions carrying the records it requires: divergence needs a plan and log, omission a report and log, and repair all three.The analytic sample is therefore measure-specific rather than one common denominator.Source: §4.8 · p. 5, Table 1 · p. 6

4.9 Statistical approach

  • Regressions use repository-clustered standard errors because repository-level intraclass correlations were non-trivial and skewed-outcome mixed-effects models converged unreliably.The analysis reports unstandardised coefficients with 95% confidence intervals and Holm-corrects p-values when testing multiple terms in one model.Source: §4.9 · p. 6

5 Validation of Measures

The paper hand-checks its two language-model measurement steps: plan extraction passed its preset validation threshold, while claim adjudication showed weak agreement and leniency. It builds conclusions on measures that survived those checks and declines inference from the adjudicator-dependent commission rate.

5 Validation of Measures

  • The hand checks assess both model-dependent text-reading steps against material the models saw, with samples drawn before results were known.The pipeline's two steps are extracting plan units and adjudicating claims that matching rules could not place.Source: §5 · p. 6

5.1 Plan extraction

  • Plan extraction achieved F1 = 0.814, exceeding the prespecified 0.75 threshold in a hand check of 50 prospective spans.The 0.81 precision is treated as the working reliability estimate for RQ2; invented units included inferred fixes and described rather than proposed plans.Source: §5.1 · p. 6

5.2 Claim adjudication

  • Agreement between the adjudicator and hand coding on 99 claims was 0.56, with Cohen’s κ = .185 and 95% CI [.109, .262], below conventional acceptable-agreement thresholds.The adjudicator judged 67% of sampled claims supported, versus 36% in hand coding; the agent-crediting off-diagonal cell was five times the opposite cell.Source: §5.2 · p. 6

5.3 Consequences for the analysis

  • Three of four measures require no model judgement at scoring, and omission is conservative because a lenient adjudicator can only increase referenced-action counts.The adjudicator-dependent commission rate, 35.3% unsupported claims, is descriptive only and a lower bound rather than an estimate; no inference is drawn from it.Source: §5.3 · p. 7

6 Results

The results show that self-reports capture only a small share of executed actions, while their tendency to resemble an agent’s plan rather than its execution depends on how far execution diverges from that plan.

6.1.1 Omission

  • Across 5,115 sessions, mean omission was 0.906, so reports referred to about 9.4% of executed actions, roughly one in eleven.Sessions needing human correction had slightly higher omission (b = 0.016, about 1.6 percentage points).Source: §6.1.1 · pp. 7–8

6.1.2 Recoverability

  • A report-only probe reconstructed the log with mean F1 = 0.202, and recoverability was unrelated to plan-execution divergence (r = −.006).The typical session’s F1 was 0.143.Source: §6.1.2 · p. 8

6.2.1 Plan-execution divergence

  • Mean divergence was 0.357 and agents abandoned 27.7% of planned steps; the 0.870 emergent rate partly reflects six-unit plans versus 59-action logs.Figure 3 covers 2,646 sessions; zero indicates every planned step appears in the log in order.Source: §6.2.1 · p. 8, Figure 3 · p. 8

6.2.2 Repair is conditional

  • Figure 4 shows that reports track execution when agents follow their plans, but drift toward abandoned plans as divergence grows.The relationship remained after controlling for sequence lengths (b = 0.731, 95% CI [0.587, 0.874]).Source: Figure 4 · p. 9, §6.2.2 · p. 9

6.2.3 Robustness of the repair measure

  • The repair index is coarse with four-claim median reports, but its divergence relationship persisted; an alternative ratio definition marked repair positive in 73.1% of sessions.The ratio definition inflates the baseline because of sequence-length asymmetry; the containment measure is reported as primary.Source: §6.2.3 · p. 9

6.3 What omission looks like

  • Unsupported cases commonly asserted outcomes without the action producing them or gave counts that the session log could not substantiate.The validation table contrasts hand-judged unsupported claims with supported ones; these cases were not necessarily fabricated, but could describe a believed state as observed.Source: §6.3 · pp. 9–10, Table 3 · p. 10

7 Discussion

The discussion finds that self-reports provide thin evidence about agent process and that report drift toward plans is conditional on execution departing from them. It draws interface implications for presenting reports, outcomes, and plans alongside the log.

7.1 Trust calibration when the automation authors its evidence

  • About 9% of executed actions are referenced and about 20% of the action surface is recoverable from reports, which the discussion treats as thin evidence for trust calibration.Claims lean toward outcomes; hand-coded unsupported cases often assert outcomes without the actions that would produce them.Source: §7.1 · p. 10
  • Recoverability did not vary with divergence, and divergence did not predict later human correction, so thin reports were not limited to sessions that went wrong.The discussion characterizes this as the ordinary form of the report.Source: §7.1 · p. 10

7.2 Situated action, narrated

  • Agents abandoned about a quarter of stated steps, with no corresponding penalty in whether a human later intervened; the discussion describes this departure as not deviance.This is presented as consistent with the situated-action view and contrasts with the symbolic-processing view that treats deviations as exceptions.Source: §7.2 · p. 10
  • Reports followed execution when it followed the plan, but leaned back toward the plan when execution abandoned it, making post-hoc reconstruction measurable in text.The discussion says the degree of this reconstruction rises with the amount of situated action to narrate.Source: §7.2 · p. 10
  • The study does not establish intent or outcome inaccuracy; it finds sequence-level resemblance to the plan when plan and execution differ.The data cannot determine whether this reflects training toward coherent narratives, plan-anchored summarization, or another cause.Source: §7.2 · p. 11

7.3 Implications for oversight interfaces

  • Present the self-report as one of three records, alongside the plan and log, since readers need the log most when reports least point toward it.The discussion argues against making the self-report the sole record.Source: §7.3 · p. 11
  • Link action claims to their tool calls and flag outcome claims lacking a verifying action in the log.Hand-coded cases suggest that the verification link is often missing.Source: §7.3 · p. 11
  • Show which planned steps were executed and which executed steps lacked a planned antecedent, using plans as a lens rather than a checklist.This would make divergence visible instead of letting reports smooth it over.Source: §7.3 · p. 11

8 Limitations and Ethical Considerations

The authors qualify their findings by measurement reliability, corpus scope, and the definition and availability of plans. They also describe the corpus and privacy protections relevant to the study.

8 Limitations and Ethical Considerations

  • Claim adjudication agreed with hand coding at κ = .185 and was lenient toward agents, so the authors report commission descriptively and draw no inference from it.Because leniency can only increase referenced-action counts, omission is conservative under this bias.Source: §8 · p. 11
  • The evidence comes from coding-agent sessions in one corpus and capture tool, with 82.9% using one scaffold; scaffold and model version are covariates, not comparisons.Several cells are thin, and candidate non-coding corpora lacked agent narration, limiting narrative-layer replication.Source: §8 · p. 11
  • The extractor and probe share a model family with most sessions, leaving possible self-preference bias; a planned open-weight anchor-model check was not run.The passage notes that self-preference bias in model judges has been documented [28].Source: §8 · p. 11
  • With a median of four claims per report, the containment index takes few distinct values, although repair direction and significance do not depend on this coarseness.The distribution does depend on it.Source: §8 · p. 11
  • Structured task-list and prose plans agreed only weakly, and the study did not test whether those registers diverge from execution differently.Using only one plan source would not yield the reported divergence values.Source: §8 · p. 11
  • Divergence and repair cover only 2,646 sessions with a recoverable stated plan, and that exclusion is not random with respect to session type.Source: §8 · p. 11
  • The public ODC-BY-1.0 corpus contains real developer prompts and repository names, but examples were stripped of paths, repository names, and identifiers.No human participants were recruited, interacted with, or observed; the claims concern agent systems rather than developers.Source: §8 · p. 11

9 Conclusion

Self-reports capture little of agents’ executed work, while their drift toward plans depends on how far execution departs. The conclusion connects these findings to trust calibration and situated action.

9 Conclusion

  • Self-reports reference about one action in eleven and let readers recover about a fifth of the action surface, regardless of how the session went.Source: §9 · pp. 11–12
  • Reports do not generally favor plans over execution; their drift toward plans increases as execution departs from the stated plan.Source: §9 · p. 12
  • The conclusion frames uniformly thin reports as relevant to trust calibration and conditional post-hoc plan reconstruction as a measurable feature of text.Source: §9 · p. 12
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