AI Agent Harness for Goal-Based Team Work
Use Mrrlin as an AI agent harness that coordinates goals, context, memory, worktrees, checkpoints, review, handoff, and approval gates.
Chat tools vs Mrrlin
The execution layer beats another blank chat window.
Chat tools make you re-send context and manage the work. Mrrlin keeps project memory and spends tokens deliberately.
Why it matters
Capable agents still need something to hold the goal and the rules.
Mrrlin acts as the operating harness around AI agents: it gives them a goal, selected context, durable memory, bounded workspaces, checkpoints, review evidence, handoff notes, and approval gates.
Workflow
Where the harness sits between one goal and anything shipping.
Pin down the intent
The Director turns a goal like launching a referral program page into scope, constraints and acceptance checks. Anything it cannot infer, such as the reward amount or which pages should link in, becomes a question in the inbox before an agent spends a single run on guesses.
Load memory, not a pasted prompt
Each task draws on the wiki: the project constitution, positioning, earlier specs and the decisions recorded with their reasons. Current material is favored over stale notes, so an agent starting today works from what the team settled last week rather than from whatever you remember to paste.
Put walls around each run
Execution happens in an isolated git worktree, not in your own checkout. Outside systems are reachable only through grants, one per destination, with reversibility recorded and every call logged, so an agent can reach only the destinations you granted.
Release on proof and approval
A reviewer on a different model reads the result, because the executor does not grade itself. The task closes only on a receipt, such as a live address, a green build or your own sign-off, and publishing, deploying or emailing a real contact stops in the inbox for you.
Use cases
Where it fits.
Agent goal intake
Type "launch the partner referral page before the next investor update" into the Director. It returns a plan of copy, page build, tracking and QA tasks, each with acceptance checks, plus two inbox questions about reward terms and launch date that no agent can answer for you.
Context and memory management
When a reviewer rejects a hero that promises instant setup, the reason is written back to project memory. The next copy task, even one run by a different agent a week later, starts with that decision and its rationale loaded instead of repeating the same mistake.
Checkpoint review
A run building the referral page records checkpoints, command output and a diff summary as it goes. A reviewer from another provider reads them, notices the signup tracking event never fires on mobile, and returns the task with that note instead of letting it reach you as finished.
Approval-gated execution
The page passes review and a preview deploy produces a working address. Production is a separate step: the task's deploy policy and your guardrails hold it in the inbox with the evidence attached, and nothing reaches the live site until you approve the publish.
Comparison
Agents on a loose leash compared with agents inside a harness.
Keep reading
More on coordinating coding agents.
Start from the AI coding agent orchestration overview, or go deeper with the guides next to this one.
FAQ
Before you start.
What does an AI agent harness add if our agents are already capable?
Capability is rarely the gap. The gap is everything around the model: a goal that stays fixed, memory that survives the session, a workspace the agent cannot wander out of, a reviewer who is not the author, and a stop before anything public. Mrrlin supplies those pieces around Claude Code, Codex CLI and the other agents you already run, so strong output becomes work you can accept.
Does the harness only cover coding agents?
No. The same loop wraps copy, research and page work as well as code. Browser tasks such as web research or website QA run in a dedicated browser with a persistent operator profile, and outside destinations like your site or CMS, messaging or ad accounts each need their own grant. Sending and publishing stay behind the same approval gates as a deploy.
How much autonomy does an agent get inside an AI agent harness?
As much as each task allows. Every task carries an autonomy level, either auto or human review, and a deploy policy of off, preview or production. On top of that sit the "always ask me before" guardrails: emailing a real contact, publishing or deploying, and client-facing claims require approval, while internal drafts and research do not. Autopilot works inside those settings rather than around them.
Tell us the outcome you want AI to execute.
Share the workflow you want to automate. We’ll map the first Mrrlin run — plan, agent routing, review loops, and approval checkpoints.
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