What problem does Agent Harness solve?

It lets a stronger supervisor delegate bounded coding tasks to cheaper workers while retaining verification, routing, policy, and operator control.

AI agent execution harness

Route code tasks to cheap workers without losing control.

Use one strong supervisor model as the architect, then route bounded coding work to cheap local workers with deterministic verification, policy guardrails, and operator control.

Optimized for solo power users running local or mixed-model execution.

Start Here Read the Docs View GitHub

agent-harness://quickstartlocal profile

harness init --profile local
harness doctor
harness demo --list-tasks
harness demo --dry-run --task ts-isUUID

# then run one bounded task
harness run tasks/ts-lib/ts-isUUID.json \
  --repo test-repos/ts-lib

supported install modes: npm, Docker, source

first-run flows: init, doctor, demo, docs

CLI integration tests protecting onboarding

Local-first

local-first, scales to team operation

What it does

Productize agent delegation without turning it into a black box.

Agent Harness is the execution layer between a high-skill supervisor and a pool of cheaper workers. The point is not raw autonomy. The point is repeatable bounded execution with evidence, routing, verification, and recovery you can actually trust.

Bounded worker execution

Delegate narrow coding tasks to local or cheap workers while the supervisor stays focused on planning and review.

Deterministic verification

Check expected text, file scope, policy, and test commands before a task is allowed to pass.

Profile-driven first run

Generate current-schema configs with local, distributed, or semantic profiles instead of hand-assembling JSON from source code.

Operator trust surfaces

Doctor checks, reports, approvals, audit logs, and structured artifacts make failures and recoveries explainable.

Routing and provider flexibility

Mix Ollama, LM Studio, Anthropic, OpenAI, and OpenAI-compatible endpoints behind one model registry and router.

Distributed when needed

Run a local single-user harness today, then move to controller plus workers without changing the product story.

How it works

Start local, add sophistication only where it buys reliability.

Install Agent Harness

Download and run the platform installer for Windows, macOS, or Linux. Native installers are coming soon.

Generate and validate config

Use `harness init --profile ...` to create a current config, then `harness doctor` to verify paths, git, and model connectivity.

Run a bounded task

Point the harness at a repo and one or more task definitions. A supervisor can route, verify, escalate, or require approval.

Inspect artifacts and recover

Use reports, audit logs, approvals, and demo flows to understand outcomes and recover from the common failure cases.

Supervisor agent via MCP or SDK

Worker adapters for local and hosted models

Git worktree isolation

Deterministic verifier

Policy and approvals

Reports, audit, and recovery artifacts

First-run shape

Start with `harness init`, `harness doctor`, and `harness demo`. Expand to distributed workers, semantic context, and approvals only when your workflow actually needs them.

Positioning

Not another general chat UI. A harness for execution quality.

Local-first architecture

Agent HarnessYesAiderNoOpenHandsNoCursorNo

Local-worker orchestration

Agent HarnessYesAiderNoOpenHandsYesCursorNo

Structured approvals and policy

Agent HarnessYesAiderNoOpenHandsNoCursorNo

Profile-driven init and doctor

Agent HarnessYesAiderNoOpenHandsNoCursorNo

Distributed controller and workers

Agent HarnessYesAiderNoOpenHandsYesCursorNo

Soft-stable reports and CLI surface

Agent HarnessYesAiderNoOpenHandsNoCursorNo

Capability	Agent Harness	Aider	OpenHands	Cursor
Local-first architecture	Yes	No	No	No
Local-worker orchestration	Yes	No	Yes	No
Structured approvals and policy	Yes	No	No	No
Profile-driven init and doctor	Yes	No	No	No
Distributed controller and workers	Yes	No	Yes	No
Soft-stable reports and CLI surface	Yes	No	No	No

Current product path

Solo developer or team — Agent Harness adapts to your scale.

The roadmap prioritizes reliable weekly usage, benchmarkable quality, and operator trust before any hosted-control-plane story.

Local

Best-supported path today. Single-user standalone operation with local or mixed-model execution.

Yesnpm, Docker Compose, and source install paths
YesProfile-driven init and doctor
YesTask execution, verification, approvals, and reports
YesDocs and examples for local, distributed, semantic, and regulated flows
YesDirect file-level control and local execution

Read Quickstart

Power User

Current focus

Phase 14: benchmark-driven routing and retrieval quality — eval loops, routing evidence, and persisted comparison reports.

YesEval loops for routing decisions
YesRouting evidence and comparison reports
YesSemantic-context retrieval improvements
YesBenchmark-driven quality gates
YesOperator-first failure and recovery guidance

View Roadmap

Team-Ready Later

Planned

Shared-controller and multi-operator features are planned, but not allowed to distort the standalone product path.

YesRemote controller patterns
YesAuditable operator actions
YesExport and backup flows
YesStorage abstraction for future DB backends
YesStandalone mode remains first-class

Read Docs

FAQ

Practical answers before platform mythology.

Can I self-host Agent Harness?

Yes. Agent Harness runs entirely on your own machine using Docker Compose and local models. No cloud dependency required.

What problem does it solve?

It gives supervisors a reliable way to delegate bounded coding work to cheaper workers while keeping verification, policy, routing, and recovery under operator control.

Do I need local models?

No, but that is the primary wedge. Agent Harness also supports hosted and OpenAI-compatible providers through the shared adapter layer.

What is the best first run?

Generate a config with `harness init --profile local`, run `harness doctor`, then inspect or run the built-in `harness demo` scenario.

Is this a hosted team product?

Not in this cycle. Team-ready operation is planned later, but the current roadmap explicitly keeps standalone local use as the primary product path.

Where should I start if I want to evaluate it quickly?

Use the install guide, quickstart, and examples docs. They map directly to the current first-run CLI and tested example configs.

Affiliate Program

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30%

Commission

12 invoices

Window

60 days

Get a real first run in under 10 minutes.

Start with the tested quickstart flow, inspect one demo task, and validate the example configs before you wire it into a bigger agent stack.

Open the Quickstart Path Quick Start Docs