Handoff Debt: The Rediscovery Cost When Coding Agents Take Over Interrupted Tasks
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue. Real software work is messier: tasks are interrupted, reassigned, reviewed, and resumed from partial states left by another agent or engineer. We study this missing dimension through handoff debt: the rediscovery cost imposed when a predecessor's work is opaque or incomplete. Our takeover protocol interrupts a coding agent at deterministic handoff points, freezes the repository, and evaluates successor agents under four handoff views: repository state only, raw trace, summary notes, and structured notes. Across 75 source tasks, the protocol generates 181 handoff-point tasks and 724 takeover runs per successor model. Across three successor models, context-bearing handoffs reduce median agent events by 20-59% and cumulative prompt tokens by 42-63% relative to repository-only takeover. Solved-rate effects are smaller and model-dependent, but efficiency gains are consistent. These findings suggest that coding-agent evaluation should report not only whether a task is solved, but also how costly that work is for another agent to resume.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 34 references that are in the map. First posted on arXiv 1 Jun 2026.
Builds on
17 of 34 references.
Closest ancestors · 6
Same direction, newest first
- 2026ContextBench: A Benchmark for Context Retrieval in Coding Agents5 Feb
- 2026Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering1 Feb
- 2025REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinement4 Oct
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024Agentless: Demystifying LLM-based Software Engineering Agents1 Jul
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug
Other references · 11
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2020Longformer: The Long-Document Transformercited by 3,362
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2023Efficient Streaming Language Models with Attention Sinkscited by 2,115
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
- 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,280
- 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
- 2026From Guessing to Seeing: Enhancing LLM-Based Program Repair via Trace-Guided Multi-strategy Debatecited by 5
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
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- 2026Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software
- 2026RippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agents
- 2026AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development
- 2026Do Coding Agents Reuse Existing Code or Reinvent the Wheel?
- 2026Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
- 2026The Working Set of a Coding Agent: Coherence Debt in Repository-Scale Tasks
- 2026Before the Pull Request: Mining Multi-Agent Coordination