The harness that keeps agents shipping. For days. Until the work is done.
A single agent session stops when context runs out. AgentLoom's harness doesn't: it breaks roadmap work into scoped sessions, composes the right agents for each step, and runs the lifecycle through GitHub — plan, code, CI, blocking review, merge, follow-up, and post-merge repair.
From roadmap to merged code, kept green.
AgentLoom defines sprints, writes the issues, and coordinates execution end-to-end. Each step gets a scoped agent team with the right tools and the right technical lens for that specific task. Plan. Code. CI. Blocking review. Merge. Follow-up. Post-merge repair. None of it skipped. None of it generalized.
1
Plan
AgentLoom defines sprints and writes the issues from your roadmap — scoped backlog work the team can execute.
2
Code
A scoped agent team implements the change with the right tools and the right technical lens for that specific task.
3
CI
Repo CI runs as it does for any PR. AgentLoom waits on it before review proceeds.
4
Blocking review
Each assigned role reviews independently. Merge is gated on every role approving and CI green — usually after several review-and-fix cycles.
5
Merge
When the team has signed off and CI is green, the change merges. Configurable per repo, on your terms.
6
Follow-up
Loose ends become reviewable follow-up tasks routed to your backlog system, linked back to the run.
7
Post-merge repair
If main turns red after merge, the integrator agent opens a repair PR within minutes — without an operator in the loop.
01
Resolve issue
scoped session
→
02
CI fails
failure captured
→
03
Resolve again
memory carries
→
04
Review blocks
structured feedback
↑
↓
05
Resolve again
re-review
←
06
Merge conflict
integrator repairs
←
07
Post-merge red
repair PR
←
08
Main green
closeout · follow-up
The moat is not one clever invocation. It is session-level orchestration across changing delivery state.
A team built for your repo, composed for the task.
AgentLoom reads your codebase, your stack, and your roadmap, then assembles role-specialized agents shaped to the work ahead. Each run is composed fresh: tools, access, context, and success criteria scoped to the task, then dissolved when the work is done. No bloated skill stacks. No generic do-anything agent.
Stable role-skills, dynamically staged for each task.
AgentLoom separates durable setup from runtime execution. On setup, repo-specific role-skills, loop closures, and permissions are derived from your stack and roadmap. At runtime, the relevant skills are selected and the task-specific execution assets are staged just-in-time.
01 · Fixed on setup
Role-skills
Built from your stack
Frontend, backend, platform, security — shaped by your technology and roadmap direction.
Loop closures
Delivery behavior
Review, repair, closeout, and follow-up flows defined for the repo.
Permissions
Allowed actions
Tools, commands, network domains, repo actions, and secrets boundaries.
Delivery rules
Advance gates
Checks and constraints that must pass before work moves forward.
02 · Staged per task
Roles
Only what's needed
Relevant role-skills are chosen for the issue and adapted to the task.
Artifacts
Structured artifacts
Every session ends in a structured, inspectable artifact — output shape and validation rules generated from the active team and workflow, carried across session boundaries.
Runtime
Staged just-in-time
Team leaders, hooks, tech skills, permission checks, runtime memory.
Loop
Kept moving
CI feedback, review blocks, retries, merge, repair, and follow-up.
Harness engineering, automated.
A single agent session is bounded by context, scope, and time. AgentLoom breaks delivery into many scoped sessions, coordinates parallel agent teams across isolated environments, and carries state across the lifecycle.
The industry has a name for everything around the model — the harness: tools, permissions, verification loops, memory, observability. Teams are hand-building harnesses to make coding agents reliable. AgentLoom automates that engineering: per-role permission envelopes derived from your stack, structured artifacts validated by runtime hooks, runtime memory scoped per issue, and checkpointed state machines that survive CI waits, review rounds, and interruptions — derived from your repo, staged per task, re-derived as your stack and roadmap evolve, with an audit trail under all of it. Claude is the model. AgentLoom is the harness — engineered, governed, and run for you.
The orchestration loop, live.
Watch the delivery loop run. Every run streams its phase timeline, live log, and cost as it executes; approvals queue where you gate them; main's health is always on screen. Nothing in this loop waits for you — and all of it stays visible.
dashboard.agent-loom.com · run detail · live
Hover to pause
RUN · RD-7F3A
Add audit-log export endpoint
Running
Phases
phase 1/5
Plan
0s
in progress…
✓
Queued
18s
Waiting for worker
acme/payments-api · agent/loom-issue-482
live
Waiting for output…
Times in UTC
Run ID
rd-7f3a
Branch
loom/issue-482
Duration
0s
Cost
$0.00
Triggered by
Roadmap · auto
When a run reaches a gate you hold — like a merge gate — it lands in your inbox. You set the automation policy; you approve only what's gated.
dashboard.agent-loom.com · approvals
Hover to pause
Open
3
Auto-approved
0
Rejected
0
Security
Sanitize redeem input · CVE-2026-1042
storefront · PR #2451
Refactor
Refactor checkout flow · isolate hot path
payments-api · PR #2440
Security
Rotate JWT signing key · overlap window
auth-service · PR #2399
S
Sanitize redeem input · CVE-2026-1042
Reject
Approve & merge
✓
Tests
32 passed
✓
Lint
No issues
✓
Type check
OK
✓
Policy
Within bounds
✓
Security
0 vulns
✓
Coverage
+0.4%
✓
Build
Passed
✓
Risk
84/100
−
await processEvent(req);
+
const key = `${req.eventId}:${req.source}`;
+
if (await dedup.seen(key)) return;
✦
AI recommendation: Patches a confirmed SQL-injection vector. Coverage 94% with new regression suite. Approve and merge.
The control room
You steer. AgentLoom ships.
Your backlog is a pool of ready work, and the delivery loop drains it — autonomously, continuously. The feedback loop keeps it full. Three things feed the pool:
You
Add, drop, re-prioritize
The highest-level input. Ask for a feature, report a bug, change a priority — in the dashboard chat or on the board — and every steering request becomes a scoped task in the pool.
Every task
Follow-ups, triaged in
Completed work surfaces loose ends — a missing test, an open design question. The triager turns them into new pool tasks, linked to the run that found them.
The roadmap
Refreshed and realigned
Every so often, AgentLoom re-evaluates the roadmap — and the team setup behind it — against what your repo has become, and queues the next wave of work.
Delivery never waits on you; direction never moves without you.
No proprietary surface. AgentLoom joins yours.
AgentLoom executes where your team already works: GitHub for code, CI, review, and merge; Jira, monday, Linear, or Asana for planning context and status. Ticket descriptions, acceptance criteria, and linked docs flow into the agent team's working context. PR links, blockers, status, and follow-up work sync back. Automation stays adjustable per repo, on your terms.
Built like CI/CD, not like a chatbot.
AgentLoom is governed as delivery infrastructure. Access is scoped per repo and per task. Approval gates are configured per repo. Multi-role review is blocking by structure, not by convention. CI is required before merge. Every agent action is logged, traceable, and tied back to the work it came from.
Ready to put a team inside your repo?
Connect your repo. Start shipping.