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ControlScope: Workflow Revision and Reliability in LLM Agents

Jingjie Ning, Xueqi Li, Yibo Kong, Dongting Li

arXiv:2609.34313v1cs.AIcs.CLcs.SE

TL;DR

ControlScope asks how much of an executing agent workflow should be revisable and compares continuing code, editing the next call’s data arguments, and replacing the remaining workflow from shared states. Across filesystem, ALFWorld, and AppWorld evaluations, broader revision has mixed results: filesystem reasoning panels favor FULL over KEEP, while other panels show small, equal, or lower outcomes. The paper also examines selected edits, subsequent execution, and review costs.

  • Problem

    The paper asks how much executable workflow revision language model agents should receive, given that program execution carries out decisions already made by the model.

    Source: §1 · p. 2

  • Method

    ControlScope compares nested KEEP, ARG, and FULL permissions from matched public execution states and evaluates both selected edits and their subsequent execution.

    Source: §1 · p. 2

  • Results

    Across two source programs and three reasoning-reviewer draws, FULL completes 15–16/20 filesystem tasks versus 13/20 for KEEP, while four fast draws yield 10–13/20 versus 13/20.

    Source: §1 · p. 2, §8 · p. 9

  • Takeaways & Limitations

    Agent systems should expose both data edits and workflow replacement, while evaluating review timing and valid-operation return rates.

    Source: §7 · p. 9

    The 216 paired local draws test a fixed early boundary in 19 tasks; sustained policies can reach additional states and revision opportunities.

    Source: §5.4 · p. 6

Abstract

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How much of a running workflow should a language model agent revise? ControlScope compares continuing generated code, editing the next tool call's data arguments, and replacing the unfinished workflow from the same public execution state. The nested permissions separate available repairs from the actions an agent selects. We evaluate one-time and repeated reviews across filesystem tasks, ALFWorld, and AppWorld. Across two source programs per task and three reasoning-reviewer draws on 20 filesystem tasks, FULL completes 15-16 tasks versus 13 for KEEP; across four fast draws it completes 10-13 versus 13. Fresh student-record confirmation reproduces a batch-read repair. ALFWorld fast panels yield KEEP/ARG/FULL scores of 85/86/87 on 87 tasks across 52 scenes and 134/134/127 on 134 tasks across four scenes; reasoning on the 87-task cohort also yields 85/86/87 with substantial review cost. An AppWorld V1 official-test panel of 585 task instances from 195 scenario templates shows small net differences. Frozen replays expose viable agent-written replacements interrupted by later revision in two failed file-organization runs. An offline source-trajectory midpoint comparison shows later reviews completing an insufficient repair. Five-call protection saves 19.4% of logged model output and loses one success across 20 fresh source runs. An argument-only shortcut shows that the broader sampled policy can overlook a cheaper successful edit available in both operation sets. These outcomes tie repair access to actual choices and subsequent execution.

1 Introduction

ControlScope asks how much of an executing agent workflow should be revisable, distinguishing edits available to a reviewer from the edits the reviewer selects. Its evaluations show that broader revision can help, but outcomes also depend on review timing and subsequent execution.

1 Introduction

  • The student-record example contrasts correcting the next file path with replacing individual reads by batched reads under a fixed tool-call budget.Both reviewers receive the same public execution record and tool descriptions; permitted edits determine which repair they can make.Source: §1 · p. 2
  • KEEP continues the current program, ARG also edits the next primitive call’s data arguments, and FULL additionally replaces the remaining workflow.A primitive call invokes an original environment API directly; a workflow organizes those calls, and FULL retains access to smaller-scope operations.Source: §1 · p. 2
  • Across two source programs and three reasoning-reviewer draws, FULL solves two or three more tasks than KEEP out of 20; across four fast draws, outcomes range from three fewer successes to equal success.The AppWorld V1 official-test panel shows small aggregate differences across 585 task instances from 195 scenario templates.Source: §1 · p. 2
  • The study tracks repair availability, the edit selected, and whether later reviews and execution preserve that edit, using matched prefixes to connect these observations to outcomes.Availability uses positive witnesses under explicit continuation protocols, with candidate-only and retained-edit replay variants.Source: §1 · p. 2

2 Related Work

Related work spans executable agent architectures, feedback-based repair and replanning, intervention evaluation, and interactive benchmarks. ControlScope focuses on nested revision permissions and the agent’s selected operation during execution.

Agents that reason, plan, and execute

  • Executable-agent architectures place control in generated programs, planners, or compiler-like systems; ControlScope instead holds an already-generated program fixed while varying reviewer edits.Examples include CodeAct [Wang et al., 2024b], LLMCompiler [Kim et al., 2023], and LLM-as-Code [Qi et al., 2026].Source: §2 · p. 2

Feedback, repair, and revision

  • Prior repair work studies feedback, local repair, replanning, and plan stability; ControlScope compares nested action sets and records the agent’s choices and revised continuation.GraSP compares typed local repair with global replanning [Xia et al., 2026], while anticipatory reflection addresses frequent plan changes and consistent execution [Wang et al., 2024a].Source: §2 · p. 2

Intervention value and execution interfaces

  • Same-prefix branching has been used to study intervention value and continuation risk [Zhang et al., 2026a]; ControlScope applies same-state branching to executable revision scope and selected operations.Its main comparisons share a tool backend, with a matched-interface fast control in the configuration analysis.Source: §2 · p. 2

Interactive evaluation

  • The evaluation spans MCPMark-derived filesystem tasks, ALFWorld, and an AppWorld V1 official-test panel alongside prior interactive benchmarks.The paper states execution contracts and filesystem task-selection rules to contextualize scope effects against benchmark contracts.Source: §2 · p. 3

3 Measuring Revision Scope

ControlScope compares revision operations from a shared public execution state, separates one-time interventions from repeated policies, and uses replay diagnostics to test candidate repairs and later execution. The shared planner and stated execution contracts delimit what these comparisons measure.

3.1 A shared execution state and nested operations

  • From the same public record and environment state, KEEP preserves the continuation, ARG adds valid edits to the next call’s data arguments, and FULL also permits replacing the remaining continuation.Argument edits preserve the pending API and later program structure, while workflow revision can change call selection, sequencing, loops, and dependencies.Source: §3.1 · p. 3
  • Granted scope is the set of available operations, whereas realized scope is the operation selected; reliability is native terminal success and stability tracks whether revisions persist through later review and planning.Because FULL includes restricted choices, losses under the larger set reflect the deployed selection and execution policy, including interface and budget interactions.Source: §3.1 · p. 3

3.2 Single revisions and sustained policies

  • A local intervention offers one revision at a fixed boundary with subsequent decisions set to KEEP, while a sustained policy repeatedly offers the assigned scope.Later revisions can change whether an earlier repair completes; all conditions retain ordinary planning, and ALFWorld also retains common failure recovery.Source: §3.2 · p. 3
  • Paired executions use task–source-program instances, with reviewer draws sharing task structure and grouped in interpretation.A prefix replay reconstructs the prior environment interactions in an isolated workspace, and selected branches receive the same public record before their actions diverge.Source: §3.2 · p. 3, §3.3 · p. 4

3.3 Replay and candidate execution

  • Replay diagnostics test actual argument patches through both scope labels and retain actual workflow replacements with later scope interventions disabled.Endpoints distinguish completion by the replacement block or original budget from complete recovery after the retained edit and ordinary planning.Source: §3.3 · p. 4

4 Experimental Setup

The experiments use filesystem, ALFWorld, and AppWorld panels with task-specific execution contracts and reviewer configurations. The design distinguishes completed sustained-policy panels from local interventions and follow-ups, while treating small task-category counts as a source of uncertainty.

Tasks and source programs

  • The main filesystem comparison samples 20 tasks across seven categories from a 24-task MCPMark adaptation, excludes four Python-restricted tasks, and evaluates two independently generated source programs per task.Native file-target verifiers determine success under the execution contract.Source: §4 · p. 4
  • ALFWorld includes 87 valid seen tasks across 52 scenes and 134 valid unseen tasks across four scenes, both reviewed in fast mode under an 8,192-token cap.A separate 87-task reasoning panel uses automatic tool choice and a 16,384-token cap; its scores include common recovery.Source: §4 · p. 4
  • The AppWorld V1 panel contains 585 official-test tasks across 195 templates, with one review per natural block and a 40-block task cap.Its current-block replacement renews that block’s allowance, unlike filesystem panels’ fixed 100-call task cap.Source: §4 · p. 4

Model and budget

  • Source generation and ordinary planning use served deepseek-flash in fast mode, while reviewers use fast or high-effort reasoning mode under matched tool schemas and request caps.Filesystem and ALFWorld conditions receive 100 primitive calls and 100 ordinary planning turns; ALFWorld allows up to five ordinary repairs.Source: §4 · p. 4

Analysis panels

  • The repeated local experiment freezes 36 eligible states from 19 tasks across two source programs, yielding 432 decisions and 216 paired draws.A seven-category bootstrap provides exploratory MCP uncertainty, with independent category count governing its precision.Source: §4 · p. 4

5 Results

Results vary by benchmark and review setup: filesystem gains depend on source program and reviewer draw, ALFWorld outcomes vary across fixed cohorts, and AppWorld V1 aggregate differences are small. At one fixed early boundary, ARG and FULL often leave terminal outcomes unchanged.

5.1 Workflow revision provides configuration-dependent gains

  • Across reasoning draws, FULL completes 15–16 tasks versus 13 for KEEP; the matched fast panel ties KEEP at 13, with ARG at 11.Which tasks benefit varies by source program and reviewer draw.Source: §5.1 · pp. 4–5, Table 1 · p. 5

5.2 Common recovery changes the visible benefit

  • After ordinary recovery, KEEP, ARG, and FULL succeed on 85, 86, and 87 tasks in the 87-task reasoning panel; FULL uses 1,124 actions versus KEEP’s 1,336.The Figure 2 effort comparison also reports completion within action thresholds and logged output tokens.Source: Figure 2 · p. 5, §5.2 · p. 5
  • FULL gains two terminal successes over KEEP with 29.7 times its output tokens; its 34.5% lower output than ARG is concentrated 98.3% in two heating tasks.FULL logs more output on 60 of 87 tasks, so aggregate token totals reflect reviewer validity and subsequent workload.Source: §5.2 · p. 6
  • On the 87-task cohort, fast and reasoning reviewers both finish 85/86/87; on the separate 134-task fast cohort, KEEP/ARG/FULL finish 134/134/127.The observed FULL direction changes across these fixed cohorts.Source: §5.2 · p. 6

5.3 AppWorld official tests show small aggregate differences

  • Across 1,755 completed AppWorld V1 runs, FULL exceeds KEEP by one task on each split; paired TGC gains are +0.60 and +0.24 percentage points.The scenario-cluster bootstrap intervals include zero, and each V1 FULL replacement renews a 250-request block allowance that favors FULL.Source: Table 2 · p. 6, §5.3 · p. 6

5.4 A single early intervention often leaves outcomes unchanged

  • At one early boundary, ARG and FULL have identical terminal outcomes in all 216 paired draws; 391 of 432 decisions continue unchanged, while all 41 actual revision branches pass execution checks.Two reasoning-enabled draws on the second file-splitting source improve over KEEP, and both scopes succeed in those draws.Source: §5.4 · p. 6
  • The equality covers a fixed early boundary in 19 tasks; sustained policies reach additional states and can exercise different revision opportunities.Source: §5.4 · p. 6
  • With both reviewers granted FULL once at the same natural boundary, code/history and feedback views each succeed on 12/17 pairs, while feedback selects nine replacements versus three.Each view has 11 joint successes; the diagnostic measures added visibility at the paused review.Source: §5.4 · p. 6

6 How Revision Helps and Hurts

Workflow revision repairs batching and parsing in some student-record tasks, but outcomes depend on source programs and selected edits; later reviews can either complete or interrupt repairs, with protection trading output for one success. The results span fresh confirmations, frozen replays, and scheduled-review comparisons.

6.1 Available repairs and selected edits

  • 94 calls with batch reads complete the student-record task, while the original loop reaches 100; a second source completes with batching at 28 calls from a 26-call prefix.The 19 selected students are checked against all original records.Source: §6.1 · p. 7
  • A replacement parser handles unquoted address commas and returns correct results for all 150 students, while the other natural source already parses the records correctly.The workload follow-up scores KEEP/ARG/FULL at 5/6, 5/6, and 6/6 for 25- and 75-student subsets, and 6/6 for each at 150 students; the gain occurs on one 75-student source.Source: §6.1 · p. 7
  • 5/6, 5/6, and 6/6 successes for KEEP/ARG/FULL recur across three fresh subsets in both reviewer modes, with a successful FULL repair following 13 identical API calls and receipts.The second source already succeeds under KEEP on all three subsets.Source: §6.1 · p. 7
  • Relative to ARG, FULL logs 23.4% fewer output tokens in fast mode and 81.1% fewer in reasoning mode; a shared argument edit also succeeds in 11 calls versus 25 for sampled FULL.The 11-call edit expands a pending API path list from 2 to 75; sampled FULL continues unchanged and uses 14 additional primitive calls.Source: §6.1 · p. 7

6.2 Later reviews shape repair completion

  • 49/71 and 46/62 actual replacements satisfy the goal in frozen replays of two failing runs, and 48 successful first-source candidates also finish their replacement block.These are correlated candidate-level replays from one task, not independent task outcomes.Source: §6.2 · pp. 7–8, Figure 3 · p. 8
  • In three fresh file-organization instances, KEEP and ARG succeed on all three while FULL succeeds on two; retaining the first replacement and allowing ordinary planning then succeeds in 77 calls.The first replacement alone ends after seven calls with the goal incomplete.Source: §6.2 · p. 8
  • 5/10 fast and 9/13 reasoning runs succeed under sustained revision, versus 4/10 and 7/13 when retaining only the first revision; later reviews complete repairs in the three changed outcomes.The tested retention rule has zero paired gains and three losses.Source: §6.2 · p. 8
  • 13/20 fresh source runs succeed under continuous FULL versus 13/20 under protected FULL, while five-call protection saves 19.4% of logged output and loses one success.Across the midpoint schedule, FULL succeeds on 13/20 tasks with one review and 14/20 with continuous review; continuous FULL uses 294 reviews against 20 and logs 64.0k against 45.8k output tokens.Source: §6.2 · pp. 8–9, Table 4 · p. 8

7 Implications for Agent Design

The design implications connect revision permissions to both repair capability and the agent’s actual choices. Evaluation should account for operation selection, review timing, and computation as well as task success.

7 Implications for Agent Design

  • Expose data edits and workflow replacement: batching and parser repairs require wider control, while a shared argument edit can save 14 calls.Review timing and valid-operation return rates also belong in evaluation; five-call protection saves 19.4% of output while losing one success.Source: §7 · p. 9

8 Conclusion

ControlScope compares revision scopes from matched public states and finds that both repair access and subsequent choices matter. Results vary across tasks and settings, and broader access does not ensure that the agent selects the cheaper successful edit or preserves it through execution.

8 Conclusion

  • Across two source programs and three reasoning draws, FULL completes 15–16/20 tasks versus KEEP’s 13/20, while four fast draws yield 10–13/20 versus 13/20.Other panels show small AppWorld V1 differences and different ALFWorld outcomes across cohorts; five-call protection loses one success across 20 fresh source runs while saving 19.4% of output.Source: §8 · p. 9

AI Use Statement

The authors report contributions from generative AI assistants across research and manuscript work, with outputs checked and responsibility retained by the authors.

AI Use Statement

  • Generative AI assistants contributed to the research question, experimental design, code implementation, and result analysis.Source: AI Use Statement · p. 9
  • The assistants also contributed to the literature review, manuscript writing, and figure production.Source: AI Use Statement · p. 9
  • Assistant outputs were checked against saved execution records, native task scores, and bibliographic sources, and the authors take responsibility for the claims and artifacts.Source: AI Use Statement · p. 9

A Execution Contract and Reproducibility

The study fixes what reviewers can see and change, and records the execution boundaries used to compare policies. Matched prefixes and replay checks connect selected edits to subsequent task outcomes.

A.1 Scope boundaries and common planning

  • Reviewers receive the pending call and public execution context; workflow replacement cancels the pending call and substitutes code for the unfinished reply.Filesystem effects pass through an MCP proxy, with isolated workers and separate task workspaces for branches.Source: §A.1 · p. 12

A.2 AppWorld V1 protocol

  • KEEP/ARG/PARAMS/FULL each succeed on 154/172 AppWorld V2 local runs; one fixed-candidate scenario succeeds 0/3 with KEEP and 3/3 with PARAMS or a fixed paging workflow.The local continuations use shared remaining-block allowance and coordinated data editing; V1 instead measures sustained full-task policies.Source: §A.2 · p. 12

A.3 Feedback visibility at a fixed boundary

  • Each view succeeds on 12/17 pairs; feedback selects nine replacements versus three and accumulates 644 versus 466 tool calls.The two terminal flips involve both the selected operation and downstream continuation.Source: §A.3 · p. 13
  • Across 17 feedback-visibility pairs, both views succeed on 12; feedback selects nine replacements versus three and uses 644 versus 466 tool calls.Two grade-scoring source units account for 167 of the 178 additional calls.Source: §A.3 · p. 13

A.4 Runtime and information checks

  • Malformed reviews and review-stage provider errors fall back to KEEP, whereas ordinary-planning provider errors end the trajectory and the native verifier scores files produced so far.Paired branches undergo worker-transport, source-observation, and prefix checks.Source: §A.4 · p. 13

A.5 Budgets and model requests

  • Exact-request caches couple ordinary-planning draws when requests coincide, and logical token totals include replayed work rather than representing all actual API use.The AppWorld V1 comparison also holds model, API descriptions, generation cap, and task code-block cap constant across scopes.Source: §A.2 · p. 12, §A.5 · p. 13, Table 5 · p. 13

B Study Coverage and Outcome Accounting

B.1 Source-trajectory-defined midpoint schedule

  • The midpoint schedule fixes review after ⌊N/2⌋ calls from the completed baseline trajectory, then compares one-time and continuous review from the same prefix.Continuous review can recur after another public call; one-time review makes later scope decisions KEEP.Source: §B.1 · p. 14

B.2 Five-call execution protection

Five-call protection changes the execution opportunity after a replacement, while shared-operation and retained-replacement replays test whether chosen edits remain useful under continued execution.

B.2 Five-call execution protection

  • KEEP/ARG/continuous-FULL/protected-FULL succeed on 13/12/14/13 tasks, respectively; protection yields zero paired wins, one loss, and 19 equal outcomes.The only terminal change is English-student selection: continuous FULL succeeds after a 300-path read at call 88, while protected FULL reaches the 100-call budget.Source: §B.2 · pp. 14–15
  • File-organization exploration yields 8/8, 8/8, and 7/8 successes; three fresh instances at 16 files and three directory levels yield 3/3, 3/3, and 2/3.Both sets remain grouped within their respective task families.Source: §B.2 · p. 15

C.1 Shared argument operations

  • Replaying the actual ARG patch under both labels succeeds in 11 calls with matching traces and files, reducing calls from 25 to 11.The sampled KEEP, ARG, and FULL policies all succeed, demonstrating an efficiency selection gap.Source: §C.1 · p. 15

C.2 Preserving a selected replacement

  • Retaining the first replacement with ordinary planning rescues a failed sustained-policy instance in 77 calls, although the replacement block alone stops after seven calls with an incomplete goal.All three generated confirmation instances succeed when later scope changes are disabled.Source: §C.2 · p. 15
  • Across 23 first-replacement prefixes, retaining the edit changes fast success from 5/10 to 4/10 and reasoning success from 9/13 to 7/13, with zero gains and three losses.Losses concern grade scoring in both modes and English-student selection in reasoning mode.Source: §C.2 · p. 15

C.3 ALFWorld recovery and computation

On the frozen ALFWorld cohorts, fast and reasoning-reviewer panels show different scope patterns by cohort. The reasoning panel also records substantial review fallbacks.

C.3 ALFWorld recovery and computation

  • KEEP/ARG/FULL score 85/86/87 on the 87-task, 52-scene cohort and 134/134/127 on the 134-task, four-scene cohort.The 87-task reasoning-reviewer configuration also scores 85/86/87 on the same source plans and budgets.Source: §C.3 · p. 15, Table 7 · p. 16
  • The 87-task reasoning panel records 26 ARG patches, 31 FULL replacements, one FULL argument patch, and invalid-review totals of 51 for ARG and 16 for FULL.All 261 scored trajectories meet action and repair budgets; FULL also has one service-error fallback.Source: §C.3 · pp. 15–16
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