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arxiv: 2605.11495 · v1 · submitted 2026-05-12 · 💻 cs.HC

Recognition: no theorem link

Hedwig: Dynamic Autonomy for Coding Agents Under Local Oversight

Tanjal Shukla , K. J. Kevin Feng , Leijie Wang , Mohammad Rostami , Amy X. Zhang
Authors on Pith no claims yet

Pith reviewed 2026-05-13 01:39 UTC · model grok-4.3

classification 💻 cs.HC
keywords coding agentsautonomy calibrationhuman-AI collaborationdynamic oversightdeveloper feedbacklongitudinal interactionCLI tools
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The pith

Hedwig is a coding agent that dynamically adjusts its autonomy by learning behavioral guidelines from developer decisions and feedback across sessions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Developers face ongoing frustration when deciding how much independence to give coding agents, since preferences shift by task and change over time, and static permission settings cannot keep up. The paper presents Hedwig as a CLI agent that observes interactions longitudinally to derive an evolving set of guidelines, granting more freedom where the agent has earned trust and requiring more input where it has not. This setup is meant to cut down on the mental effort of constant oversight while still catching unintended edits or scope drift. The approach is grounded in survey findings from 21 engineers who reported evolving needs for calibration.

Core claim

Hedwig is a CLI coding agent that dynamically adjusts its autonomy level based on developer-agent interactions across sessions. Rather than operating on a global, fixed autonomy configuration, Hedwig learns an evolving set of behavioral guidelines from developer decisions and feedback, reducing friction on work for which the agent has earned trust, while tightening oversight when the agent operates outside familiar territory.

What carries the argument

The longitudinal learning of behavioral guidelines from developer decisions and feedback that directly controls when the agent acts independently or requests input.

Load-bearing premise

Developer decisions and feedback supply clear, consistent signals that can be turned into reliable guidelines for adjusting autonomy without adding new friction or mistakes.

What would settle it

A multi-session user study measuring whether Hedwig users produce fewer unintended edits, spend less time on oversight decisions, or report higher satisfaction than users of a static-permission coding agent; no measurable improvement would falsify the benefit of the dynamic approach.

Figures

Figures reproduced from arXiv: 2605.11495 by Amy X. Zhang, K. J. Kevin Feng, Leijie Wang, Mohammad Rostami, Tanjal Shukla.

Figure 1
Figure 1. Figure 1: Hedwig is a CLI coding agent that dynamically calibrates its level of autonomy in response to user interactions and feedback. Hedwig records and reasons over user interactions (e.g., code edits, plan corrections, rejected/approved commands) over time to maintain an evolving set of behavioral policies. These policies are used to govern the agent’s actions and frequency of check-ins with the developer. Abstr… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of Hedwig. (1) A developer’s interactions with the agent are stored in the agent’s memory (2). Also in the memory module are hard constraints and softer behavioral guidance. The model can retrieve from memory via the retrieval path to inform its behavior. Separately, the policy path trains a policy engine (3), a regression classifier. The model can ask to use tools (4), and tool use is also go… view at source ↗
read the original abstract

Despite coding agents' advances in handling increasingly complex tasks, their continued tendency to introduce unintended edits, subtle bugs, and scope drift that slip past code review means developers must still decide how much autonomy to grant them. However, existing approaches for setting an agent's level of autonomy, such as static permission settings or instruction files, cannot account for how developers' preferences for agent autonomy can shift across tasks and over time. We conducted a formative survey with 21 software engineers who use coding agents and found that they experience frustration with calibrating autonomy and have evolving preferences for level of oversight. Building on these insights, we present Hedwig, a CLI coding agent that dynamically adjusts its autonomy level based on developer-agent interactions across sessions. Rather than operating on a global, fixed autonomy configuration, Hedwig learns an evolving set of behavioral guidelines from developer decisions and feedback, reducing friction on work for which the agent has earned trust, while tightening oversight when the agent operates outside familiar territory. Hedwig demonstrates the potential of a new paradigm where agents intelligently adapt their level of autonomy based on user trust through active, longitudinal collaboration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper claims that static autonomy settings for coding agents fail to accommodate developers' shifting preferences across tasks and time, as evidenced by a formative survey of 21 software engineers; it introduces Hedwig, a CLI coding agent that dynamically adjusts autonomy by learning an evolving set of behavioral guidelines from longitudinal developer decisions and feedback, thereby reducing friction in trusted areas while increasing oversight elsewhere.

Significance. If the described learning mechanism for autonomy calibration can be implemented and validated, the work could meaningfully advance HCI research on adaptive AI agents by establishing a longitudinal, interaction-driven alternative to fixed permission models. The survey provides initial qualitative grounding for user needs, and the conceptual design articulates a clear paradigm shift toward trust-based collaboration.

major comments (3)
  1. [Abstract / Hedwig system description] Abstract and Hedwig system description: The central claim that Hedwig 'learns an evolving set of behavioral guidelines from developer decisions and feedback' lacks any specification of the learning process, data structures for representing guidelines or trust, extraction algorithms, or update rules. This is load-bearing because the manuscript positions the learning mechanism as the key innovation enabling dynamic autonomy without new friction or errors, yet provides no basis for assessing feasibility or correctness.
  2. [Formative survey] Formative survey section: The survey (n=21) is invoked to establish that developers experience frustration with calibrating autonomy and hold evolving preferences, but no methodology details, question instruments, response distributions, or direct mapping from findings to specific behavioral guidelines are supplied. This undermines the motivation for the proposed design.
  3. [Evaluation / Results] Evaluation and results: No implementation, prototype metrics, user study, or longitudinal deployment data are reported to measure changes in oversight levels, error rates, task friction, or calibration accuracy across sessions. The abstract's assertion that Hedwig 'demonstrates the potential' of the paradigm therefore rests on an unevaluated concept rather than evidence.
minor comments (1)
  1. [Abstract] The abstract and introduction could explicitly note that the current contribution is a system concept and survey rather than a fully implemented and evaluated prototype, to set reader expectations.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which identifies key opportunities to improve the clarity, grounding, and scope of the manuscript. We address each major comment below, indicating where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract / Hedwig system description] Abstract and Hedwig system description: The central claim that Hedwig 'learns an evolving set of behavioral guidelines from developer decisions and feedback' lacks any specification of the learning process, data structures for representing guidelines or trust, extraction algorithms, or update rules. This is load-bearing because the manuscript positions the learning mechanism as the key innovation enabling dynamic autonomy without new friction or errors, yet provides no basis for assessing feasibility or correctness.

    Authors: We agree that the current manuscript describes the learning mechanism at a high conceptual level without specifying data structures, extraction methods, or update rules. This reflects the paper's focus on the HCI paradigm of longitudinal, trust-based autonomy adjustment rather than a systems implementation. In revision, we will expand the Hedwig system description to include proposed data structures (e.g., guideline representations as conditional rules paired with per-context trust scores), high-level extraction from interaction logs (e.g., identifying patterns in accepted edits or feedback), and update heuristics (e.g., threshold-based reinforcement or decay). These additions will provide a clearer feasibility basis while preserving the manuscript's emphasis on design principles over algorithmic detail; full pseudocode and implementation would be reserved for a follow-up systems paper. revision: partial

  2. Referee: [Formative survey] Formative survey section: The survey (n=21) is invoked to establish that developers experience frustration with calibrating autonomy and hold evolving preferences, but no methodology details, question instruments, response distributions, or direct mapping from findings to specific behavioral guidelines are supplied. This undermines the motivation for the proposed design.

    Authors: The referee is correct that the absence of survey methodology details weakens the grounding of the design motivation. We will add a new subsection to the formative survey section that reports recruitment approach, survey instrument (including sample questions on autonomy preferences and frustration points), participant demographics, and summarized response distributions. We will also explicitly map key findings (such as preferences for reduced oversight on familiar tasks) to specific elements of Hedwig's guideline-learning approach and autonomy calibration logic. revision: yes

  3. Referee: [Evaluation / Results] Evaluation and results: No implementation, prototype metrics, user study, or longitudinal deployment data are reported to measure changes in oversight levels, error rates, task friction, or calibration accuracy across sessions. The abstract's assertion that Hedwig 'demonstrates the potential' of the paradigm therefore rests on an unevaluated concept rather than evidence.

    Authors: We acknowledge that the manuscript contains no implementation, metrics, or user study data. Hedwig is presented as a conceptual design whose 'demonstration' consists of articulating how the paradigm would function in practice, informed by the formative survey. We will revise the abstract, introduction, and conclusion to explicitly frame the work as a design proposal and paradigm introduction, with the demonstration being illustrative rather than empirical. Evaluation via prototype and longitudinal studies is identified as future work. revision: yes

Circularity Check

0 steps flagged

No circularity: conceptual system proposal grounded in external survey data

full rationale

The manuscript contains no equations, derivations, fitted parameters, or predictive models. Its core contribution is a survey-informed system concept (Hedwig learns behavioral guidelines from interactions) rather than any computation that reduces to its own inputs by construction. The formative survey (n=21) supplies independent empirical motivation for the design choices, and no self-citation chains, uniqueness theorems, or ansatzes are invoked to justify load-bearing claims. This is a standard non-circular HCI/systems paper whose validity rests on future implementation and evaluation, not on internal definitional closure.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

This is an HCI system and survey paper with no quantitative models or derivations. The core assumption is that user interactions yield learnable guidelines for autonomy; no free parameters or invented physical entities are involved.

axioms (1)
  • domain assumption Developers' preferences for agent autonomy evolve across tasks and sessions and can be inferred from their decisions and feedback.
    This underpins the entire Hedwig design and is stated as motivation from the survey.
invented entities (1)
  • Hedwig no independent evidence
    purpose: CLI coding agent that learns and applies dynamic autonomy guidelines
    The system is the primary contribution; no independent evidence outside the paper is provided in the abstract.

pith-pipeline@v0.9.0 · 5503 in / 1185 out tokens · 34987 ms · 2026-05-13T01:39:07.687991+00:00 · methodology

discussion (0)

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