---
type: "post"
id: "software-engineering-agents-such-as-agentless-swe-agent-e4c26b"
slug: "software-engineering-agents-such-as-agentless-swe-agent-e4c26b"
title: "Software-engineering agents such as Agentless, SWE-agent, OpenHands, and Claude Code repeatedly generate five kinds of intermediate information."
description: "Thesis: Intermediate information signals are measurable contributors to repository-level agent success, not merely incidental traces of exploration."
retrieval_nugget: "Thesis: Intermediate information signals are measurable contributors to repository-level agent success, not merely incidental traces of exploration."
published_at: "2026-10-20"
updated_at: "2026-10-20"
record_date: "2026-10-20"
date_kind: "published_at"
topics: ["claude","claude-code"]
source_urls: ["https://arxiv.org/html/2604.07789v3"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:software-engineering-agents-such-as-agentless-swe-agent-e4c26b"
status: "published"
visuals: []
editorial_provenance: {"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:1dcc063c7f0fa0668b1845a4a25e3f91381c96282d2ece59dd1c2a5518c8f9dd","reviewed_at":"2026-10-20T14:09:03.498Z","source_evidence_count":1,"verified_claim_count":3,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":3,"implication_count":2,"watch_condition_count":2,"related_record_count":0}
analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Intermediate information signals are measurable contributors to repository-level agent success, not merely incidental traces of exploration.","observed_facts":[{"text":"The paper selects five signals: Reproduction Test, Regression Test, Edit Location, Execution Context, and API Usage.","source_urls":["https://arxiv.org/html/2604.07789v3"]},{"text":"It reports the contribution ordering Reproduction Test > Execution Context approximately equal to API Usage > Edit Location > Regression Test.","source_urls":["https://arxiv.org/html/2604.07789v3"]},{"text":"The authors compare agent-extracted signals with oracle counterparts and quantify the gap between them.","source_urls":["https://arxiv.org/html/2604.07789v3"]}],"mechanism":"The researchers provide oracle or stronger-agent-extracted signals to a main agent as if they came from a sub-agent or workflow stage, then use resulting task-success gains to estimate signal contribution.","why_now":"Repository-level agents depend on exploration, localization, API retrieval, and testing before editing, while these capabilities can be studied through concrete signals rather than inferred only from an end result.","implications":["Teams can instrument and evaluate intermediate information stages alongside patch correctness.","The reported ordering can motivate local experiments, but should not be assumed universal."],"evidence_boundary":"The findings are experimental results from the paper’s methodology and tasks; they do not establish a universal ranking across agents, benchmarks, or production repositories.","watch_conditions":["Whether evaluations on other benchmarks and agent setups reproduce the reported ordering.","Whether agent-extracted signals close the measured gap with oracle signals."],"related_records":[],"new_branch_reason":"No prior New Runtime record was supplied for this research result, so it begins a distinct analytical record about measuring intermediate information signals in software-engineering agents."}
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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related_patterns: ["ai-engineering-evaluations-are-splitting-into-task-specific"]
---

# Software-engineering agents such as Agentless, SWE-agent, OpenHands, and Claude Code repeatedly generate five kinds of intermediate information.

## Retrieval answer

Thesis: Intermediate information signals are measurable contributors to repository-level agent success, not merely incidental traces of exploration.

Thesis: Intermediate information signals are measurable contributors to repository-level agent success, not merely incidental traces of exploration. The paper compares five signal types and reports a consistent ordering in their measured contribution: Reproduction Test > Execution Context approximately equal to API Usage > Edit Location > Regression Test. The authors also compare signals extracted by agents with oracle counterparts. These observations make evaluation of the agent’s information-gathering stages a practical complement to scoring its final patch.

Mechanism: The researchers provide oracle or stronger-agent-extracted signals to a main agent as if they came from a sub-agent or workflow stage, then use resulting task-success gains to estimate signal contribution. Why now: Repository-level agents depend on exploration, localization, API retrieval, and testing before editing, while these capabilities can be studied through concrete signals rather than inferred only from an end result. Evidence boundary: The findings are experimental results from the paper’s methodology and tasks; they do not establish a universal ranking across agents, benchmarks, or production repositories.

First implication: Teams can instrument and evaluate intermediate information stages alongside patch correctness. The reported ordering can motivate local experiments, but should not be assumed universal. First watch condition: Whether evaluations on other benchmarks and agent setups reproduce the reported ordering. Also watch whether agent-extracted signals close the measured gap with oracle signals. The paper releases code and an oracle-signal dataset, which could support replication; their availability alone does not validate transfer beyond the reported experiments.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

## Continue in the graph

Next records for an agent, linked to their Markdown twins. Replace `.md` with `.json` for structured fields, including `next_reads`.

- topic hub: [Claude](https://newruntime.com/topics/claude.md): Explore the claude topic hub.
- topic hub: [Claude Code](https://newruntime.com/topics/claude-code.md): Explore the claude-code topic hub.
- collection: [Field Notes](https://newruntime.com/posts.json): Open the latest editorial analysis.
- collection: [Source Ledger](https://newruntime.com/source-ledger.json): Inspect the public source evidence graph.
- whole graph: [discovery graph](https://newruntime.com/discovery-graph.json) (typed nodes and edges) and [site index](https://newruntime.com/site-index.md)