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agent-harness

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Agent Harness Research

DOI

Author: Mandeep Sidhu, Independent Researcher

Release: 1.0.0 preprint candidate

Paper: Dissecting Repository-Scale Code-Agent Harnesses

Archival DOI: 10.5281/zenodo.21781711

This repository is an experimental platform for a scientific study of how repository-navigation harnesses affect an LLM coding agent on codebases that do not fit in the model context window.

Study 1 fixes Qwen3.6-35B-A3B (35B total parameters, 3B active parameters). The completed Studies 2--5 evaluate that model, GPT-OSS-20B, and Qwen3-Coder-30B-A3B-Instruct, served locally by LM Studio on port 1234. Experiments abort if the exposed model, variant, quantization, context, or reasoning default does not match. The canonical model is documented by the Qwen release and official model repository.

LM Studio exposes model discovery through /api/v1/models and OpenAI-compatible inference and tool calling through /v1/chat/completions. See the LM Studio server documentation, model listing documentation, and tool-use documentation.

Completed research program

21 immutable harness specifications (H000 through H020)
Fifteen completed experiment families (E01--E05 and E07--E16)
5,453 audited cells: 965 Study 1, 912 Study 2, 540 Study 3, 210 Study 4, and 2,826 Study 5
Separate robustness scenarios and vector-index backend profiles
Strict local-model discovery with no silent model fallback
Append-only JSONL telemetry and deterministic run identities
Confirmatory statistical analysis with paired exact tests, task bootstrap intervals, Holm correction, and a mixed-effects factorial model
A reproducible research paper with generated figures/tables, checksums, and page-by-page rendered-PDF quality assurance

Powered Study 2

60 validated real-change tasks across GitLab Runner, GitLab CLI, and python-gitlab
Go and Python repositories whose full source exceeds the 65,536-token treatment cap
Five component harnesses plus controlled Agentless-style and SWE-agent-style baselines
Qwen3.6-35B-A3B and GPT-OSS-20B under identical local-runtime controls
840 deterministic main cells plus 72 preregistered stochastic reliability cells
Exact paired testing, task-cluster intervals, multiplicity control, and hierarchical analysis
Primary result: H007 hybrid and H000 lexical both resolved 2/60 Qwen tasks (risk difference 0.000, 95% task-bootstrap CI [-0.050, 0.050], exact p=1)
No secondary contrast survived correction; GPT-OSS produced no accepted repair and exposed a strong model/tool-protocol compatibility limitation

Protocol-normalized Studies 3--4

E09: 60 tasks x 3 models x 3 edit interfaces = 540 real local-LLM cells
GPT-OSS exact replacement accepted edits on 27/60 tasks versus 0/60 for raw diff (RD +0.450, 95% CI [0.333, 0.583], exact p=1.49e-8)
Resolution-blind gate: P002 for Qwen3.6; P003 for GPT-OSS and Qwen3-Coder
E10: 20 fresh tasks x 3 gated models x exact/hybrid/oracle retrieval = 180
Hybrid resolved 1/60 model-task pairs versus exact 0/60 (RD +0.0167, task-cluster CI [0, 0.05], exact p=1); oracle file names resolved 4/60
E11/E12: 30 prespecified stochastic and context-scarcity sensitivity cells

Harness-design Study 5

E13: 60 tasks x 8 lexical/syntax/dense combinations x 3 models = 1,440 cells
E14: 20 tasks x 3 retrievers x 3 edit interfaces x 3 models = 540 cells
E15: 20 tasks x 9 graph/query/interface/packing harnesses x 3 models = 540 cells
E16: six deterministically selected archetypes x 17 fresh tasks x 3 models = 306 cells
No E13 component main effect survives correction; E14 finds no adjusted retrieval-by-action interaction
Iteration and whole-file packing reliably increase cost without a demonstrated applicable-patch benefit
E16 quality-rank transfer is weak (accepted Spearman rho 0.04; applicable rho -0.21), while token-cost rank is stable (rho 0.94)
Only one E16 cell resolves, so the study reports stage-aware trends and Pareto candidates rather than a universal winning harness

Repository layout

text

configs/
  harnesses/     one immutable file per H000-H020 treatment
  experiments/   staged E00-E16 experiment manifests
  models/        fixed Qwen/LM Studio runtime policy
  embeddings/    independently pinned dense-retrieval model
  scenarios/     robustness perturbations
  backends/      dense-index systems profiles
src/agent_harness/  schemas, LM Studio client, interfaces, metrics, telemetry
tasks/              task manifests and frozen split lists
results/            immutable local raw artifacts, analyses, and reports
docs/               protocol, catalog, metrics, runtime, and paper plan
tests/              standard-library unit tests
paper/              LaTeX manuscript and bibliography
output/pdf/         stable rendered research paper

Dense retrieval uses the separately pinned Qwen3 Embedding 0.6B model (text-embedding-qwen3-embedding-0.6b) through LM Studio. The local GGUF Q8_0 model produces 1,024-dimensional normalized vectors and exposes a 32,768-token maximum context. The generative Qwen agent and embedding model remain separate experimental components.

Quick start

Create a clean Python 3.11 environment and install the harness plus its pinned research dependencies:

bash

python3.11 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -r requirements.lock
.venv/bin/python -m pip install -e . --no-deps

For the saved-evidence reproduction path, LM Studio and model weights are not required. See REPRODUCING.md for the exact artifact and inference workflows.

bash

PYTHONPATH=src python3 -m agent_harness.cli validate
PYTHONPATH=src python3 -m agent_harness.cli list-harnesses
PYTHONPATH=src python3 -m agent_harness.cli plan --experiment E03
PYTHONPATH=src python3 -m agent_harness.cli probe-model
PYTHONPATH=src python3 -m agent_harness.cli probe-embedding --infer
PYTHONPATH=src python3 -m agent_harness.cli run-pilot --experiment E00
PYTHONPATH=src .venv/bin/python -m agent_harness.cli run-live-agent --experiment E07
PYTHONPATH=src .venv/bin/python scripts/preflight_study2.py
PYTHONPATH=src .venv/bin/python -m agent_harness.cli run-study2
PYTHONPATH=src .venv/bin/python -m agent_harness.cli run-study2-reliability
PYTHONPATH=src .venv/bin/python scripts/analyze_confirmatory.py
MPLCONFIGDIR=/tmp/agent-harness-mpl PYTHONPATH=src .venv/bin/python scripts/analyze_live_agent.py
MPLCONFIGDIR=/tmp/agent-harness-mpl PYTHONPATH=src .venv/bin/python scripts/analyze_study2.py
MPLCONFIGDIR=/tmp/agent-harness-mpl PYTHONPATH=src .venv/bin/python scripts/analyze_study3.py
MPLCONFIGDIR=/tmp/agent-harness-mpl PYTHONPATH=src .venv/bin/python scripts/analyze_study4.py
PYTHONPATH=src .venv/bin/python scripts/analyze_study4_ancillary.py
MPLBACKEND=Agg MPLCONFIGDIR=/tmp/agent-harness-mpl PYTHONPATH=src .venv/bin/python scripts/analyze_study5.py
PYTHONPATH=src .venv/bin/python scripts/check_study5_paper_consistency.py
PYTHONPATH=src .venv/bin/python -m pytest -q

probe-model discovers and validates the exact model key, MLX 4-bit variant, 262,144-token loaded context, and reasoning default. Add --infer to perform a small completion and verify the response semantics after discovery.

Scientific protocol

Start with the scientific protocol, harness catalog, LM Studio policy, metric and artifact schema, observed runtime baseline, pilot dataset, pilot results, memory-safety protocol, and paper plan. Study 2 is governed by its preregistration, implementation contract, design audit, and preflight procedure. Study 3 and 4 use Study 3 preregistration, Study 4 preregistration, and their frozen design audits, compatibility gate, preflights, and ancillary manifest audit. Study 5 uses its prospective preregistration, frozen cell manifests, and sequential E16 selection ledger. Configuration files are part of the experimental treatment and become immutable once a recorded run uses them. Any treatment change receives a new harness ID or experiment version.

The completed manuscript is output/pdf/dissecting_repository_scale_code_agent_harnesses.pdf. Its source and build instructions are under paper/. The immutable local analysis used by the paper is results/derived/confirmatory_analysis_174ce71bcbce plus results/derived/e07, results/derived/study2, results/derived/study3, results/derived/study4, results/derived/study4_ancillary, and results/derived/study5.

Citation and release

Citation metadata is provided in CITATION.cff, and the exact Zenodo record fields are in .zenodo.json. The canonical archival DOI is 10.5281/zenodo.21781711. Publication upload instructions are in README_ZENODO.md.

The repository intentionally contains one canonical PDF. Raw trajectories, repository checkouts, index caches, and model weights are excluded from Git and from the compact release; raw trajectories belong in a separate checksum-addressed Zenodo archive.

Licensing

Original software is MIT licensed under LICENSE. The manuscript, documentation, figures, tables, and original derived research artifacts are CC BY 4.0 under LICENSE-DATA. Benchmark patches retain their upstream licenses; see THIRD_PARTY_NOTICES.md.

cuber12/agent-harness