Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

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📰 News

• [2026-05-14] 🏆 AHE (on GPT-5.5) ranked #3 on the Terminal-Bench 2.0 leaderboard with 84.7% — ranking as of 2026-05-15
• [2026-04-30] ✍️ Blog post on Dawning Road (English & Chinese) — a more detailed account of the exploration behind AHE: Agentic Harness Engineering
• [2026-04-28] 📄 Paper released on arXiv: Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
• [2026-04] 🎉 Framework released

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🎯 Overview

AHE (Agentic Harness Engineering) is an open observability system for automatically evolving the harness around a coding agent. The base model is held fixed; what evolves are the harness components — system prompts, tool descriptions, tool implementations, middleware, skills, sub-agents, and long-term memory.

AHE rests on three observability layers:

• Component observability — NexAU decomposes the harness into seven orthogonal, file-level components, each git-tracked so every edit is auditable and revertible.
• Experience observability — Agent Debugger distills ~10M-token raw traces into layered, sourced reports; the optimizer reads digests by default but can always drill back to any rollout's raw trace.
• Decision observability — Evolve Agent proposes evidence-backed edits, predicts their impact, and is automatically falsified by the next iteration's flipped tasks.

Across ten evaluate → analyze → improve iterations, AHE (Agentic Harness Engineering) lifts Terminal-Bench 2 pass@1 from 69.7% to 77.0% on GPT-5.4, surpasses the hand-written Codex (71.9%) and the self-evolving ACE and TF-GRPO baselines, and produces a frozen harness that transfers without re-evolution to SWE-bench-verified and to four alternate base models, indicating that the evolved components encode general engineering experience rather than benchmark-specific tuning.

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🚀 Quick Start

0. Prerequisites

• Python ≥ 3.13
• uv
• tmux

# macOS
brew install uv tmux

# Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
sudo apt install -y tmux

1. Clone + install dependencies

git clone https://github.com/Curry09/agentic-harness-engineering.git
cd agentic-harness-engineering
uv sync

uv sync installs every dependency declared in pyproject.toml.

2. Configure environment variables

cp .env.example .env

Edit .env. At minimum, set:

ADB_LLM_* and GPT54_LLM_* are optional — leave them unset to fall back to LLM_*, or set them to point ADB / the gpt-5.4 experiment at a stronger model. LANGFUSE_*, BP_HTML_PARSER_*, and FEISHU_WEBHOOK are all optional observability / convenience hooks; see .env.example for the full list.

E2B sandbox: SaaS vs. self-hosted

AHE runs every rollout inside an E2B sandbox. Two deployment modes are supported:

• SaaS E2B (default). Set only E2B_API_KEY and leave E2B_API_URL / E2B_DOMAIN unset (or commented out). The SDK talks to e2b.dev automatically.

  ⚠️ Concurrency cap. SaaS E2B enforces a per-account concurrent sandbox limit tied to your tier. If harbor tries to spawn more sandboxes than the cap allows, the extra sandboxes fail to start and the iteration stalls. Before raising parallelism in your harbor / experiment config, check your tier's quota and stay safely under it.

• Self-hosted E2B cluster. Set E2B_API_KEY and point the SDK at your cluster:

  E2B_API_KEY="your_e2b_key"
  E2B_API_URL="https://your-e2b-host.example.com"
  E2B_DOMAIN="your-e2b-host.example.com"
  

  No shared concurrency cap applies, but the cluster's hardware capacity still does.

3. Build E2B templates (one-time per dataset)

The dataset here is a pack from laude-institute/harbor-datasets — clone the subset you need and point --dataset-dir at its directory.

Every rollout runs inside an E2B sandbox spawned from a prebuilt template that already has uv and the NexAU/harbor venv at /opt/nexau-venv. Build those templates once before launching:

# Build every template declared by the dataset, 16 in parallel
uv run python scripts/build_templates.py --dataset-dir /path/to/dataset -j 16

# Resume after a failure: only retry tasks whose latest E2B build status is ERROR
uv run python scripts/build_templates.py --dataset-dir /path/to/dataset --retry-failed

# Build a specific subset of tasks
uv run python scripts/build_templates.py --dataset-dir /path/to/dataset task_a task_b

The dataset directory must contain one subdir per task with a task.toml declaring [environment].docker_image (or an environment/Dockerfile fallback). Each task's template alias is <task_name> with . replaced by -.

The default packages baked into each template come from scripts/build_templates.py:DEFAULT_NEXAU_PACKAGES (a public NexAU + the in-sandbox NexAU-harbor variant, intentionally distinct from the host-side harbor-LJH in pyproject.toml). Override with one or more --nexau-package <git-or-pip-spec> flags if you need a different revision in the sandbox.

If your tasks pull from a private Docker registry, also export DOCKER_REGISTRY_USERNAME and DOCKER_REGISTRY_PASSWORD before invoking the script.

4. Launch

# Run a single experiment in the background via tmux
./scripts/evolve.sh configs/experiments/exp-simple-code-gpt54.yaml

# Launch and auto-attach to the log stream
./scripts/evolve.sh --attach configs/experiments/exp-simple-code-gpt54.yaml

# Batch: launch every experiment under configs/experiments/
./scripts/evolve.sh --batch

Common tmux operations after launch:

tmux ls                         # list sessions
tmux attach -t <session>        # attach to a session
# Ctrl-b d                      # detach (keeps running in background)
tmux kill-session -t <session>  # terminate

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🔧 How It Works

The base model is held fixed; what evolves is the harness around it. Each outer iteration is evaluate → analyze → improve, built on the three observability layers from the Overview.

1. Evaluate — emit traces, not just scores

harbor runs the current code_agent over the dataset inside isolated E2B sandboxes. Per task it writes:

• agent/nexau_in_memory_tracer.cleaned.json — full step-level trace (messages, tool calls, middleware events)
• agent/nexau.txt — runtime log (middleware errors, crashes, warnings)
• verifier/reward.txt — pass/fail outcome

The trace, not the pass rate, is the unit every later step operates on.

2. Analyze — distill ~10M-token traces into sourced evidence

Agent Debugger compresses each iteration's raw traces (routinely >10M tokens) into layered reports:

• analysis/overview.md — cross-task root-cause summary
• analysis/detail/{task}.md — per-task deep analysis

The optimizer reads digests by default, but every claim links back to the originating raw trace, so it can drill down before committing to a change.

Note on Agent Debugger licensing. The current release ships a partially open-sourced Agent Debugger; due to company strategy, it cannot be fully open-sourced at this time.

3. Improve — evidence-backed, falsifiable edits

Evolve Agent may only write inside workspace/, which exposes the seven NexAU components: systemprompt.md, code_agent.yaml, tool_descriptions/, tools/, middleware/, skills/, sub_agents/ (plus LongTermMEMORY.md). For every edit it must commit four fields:

1. Failure evidence — the failing tasks and trace excerpts that motivate the change
2. Root cause — why it failed, not just what failed
3. Targeted fix — the change that directly addresses that cause
4. Predicted impact — which tasks should flip to pass, and which are at risk

4. Loop — staggered generations enable falsification

Each runs/iteration_NNN/ mixes two generations: input/ holds the workspace produced by loop NNN-1 (just evaluated), evolve/ holds what loop NNN writes (evaluated next loop). Flips (pass↔fail) on the next eval are attributed back to this loop's edits in change_evaluation.json — predictions that don't hold get rolled back or revised. The loop terminates on target_pass_rate or max_iterations.

Main components

Directory layout

agentic-harness-engineering/
├── evolve.py                       # main loop
├── trace_converter.py              # rollout trace → debugger-friendly JSON
├── agents/
│   ├── code_agent_simple/          # the coding agent under evolution
│   ├── evolve_agent/               # the evolution meta-agent
│   │   ├── evolve_prompt.md
│   │   ├── middleware/             # context compaction / failover / ralph loop …
│   │   ├── skills/                 # agent-debugger-cli / nexau-evolution-guide
│   │   └── tools/                  # file / shell / web / session tools
│   └── explore_agent/              # exploration agent (sources + web)
├── configs/
│   ├── base.yaml                   # shared defaults
│   └── experiments/                # one overlay per experiment
├── scripts/
│   ├── evolve.sh                   # tmux launcher
│   └── evolve-resume.sh            # resume helper
└── .env.example

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Configuration (base + overlay)

configs/base.yaml holds the shared defaults. Each configs/experiments/exp-*.yaml inherits it via a leading _base: ../base.yaml line and overrides only the fields that differ. Any ${ENV_NAME} reference inside a YAML file is substituted from .env.

Key fields in base.yaml:

Dataset configuration

An experiment's data source is specified via path or dataset — pick one:

Public dataset packs (in the layout AHE expects under path:) are published at laude-institute/harbor-datasets — clone or download the subset you need and point path at its directory.

The default path values in base.yaml and configs/experiments/*.yaml are placeholders only — adjust them for your environment, or comment out path and uncomment the dataset line to use a harbor built-in dataset instead.

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CLI reference

python evolve.py

./scripts/evolve.sh

A thin wrapper around uv run python evolve.py + tmux.

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Common scenarios

Resume an interrupted experiment from iteration 16:

./scripts/evolve.sh \
  --experiment 2026-04-10__23-20-14__gpt54 \
  --start-iteration 16 \
  configs/experiments/exp-simple-code-gpt54.yaml

Run only evolve_agent without re-running evaluation:

./scripts/evolve.sh \
  --experiment <existing-exp-dir> \
  --skip-eval \
  configs/experiments/exp-simple-code-gpt54.yaml

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License

MIT

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