The orchestration layer for AI software delivery
Boost software delivery with autonomous engineering teams that live inside your repo.
AgentLoom orchestrates Claude-powered coding agents into governed delivery teams — sessions, roles, permissions, runtime memory, CI, blocking review, merge, repair, and follow-up — run end-to-end through GitHub.
Built on Claude Code — bring your own Anthropic key. Works with GitHub, Jira, monday, Linear, Asana. Start free — no credit card.
AgentLoom · backlog → run → approve → ship
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Backlog
Run
Approve
Ship
P
BACKLOG · ACME/PAYMENTS-API
Scoped
14 open · 3 high
Open
14
High priority
3
With PR
3
Stale
2
AI-suggested
14
All
14
Mine
4
High priority
3
Bugs
6
AI-triaged
14
P
Flaky retry on webhook ingest #479
bug
Medium
▸ Start run
P
Add audit-log export endpoint #482
high-priority
High
▸ Start run
P
Connector status drift on Jira #484
bug
Medium
▸ Start run
P
Org-chart role editor polish #486
ui
Low
▸ Start run
The operator bottleneck
Operating Claude is a full-time job. It shouldn't be yours.
Agents got powerful — and operating them quietly became the job. Right now, you orchestrate the loop by hand: start the task, choose the context, approve the permissions, inspect the output, explain the failures, decide what happens next. As sessions stretch, context drifts and focus degrades.
01
Start task
Choose the context, the skills, and the scope — every session begins by hand.
02
Approve permissions
Tools, commands, domains: each request waits on a human click.
03
Inspect output
Read the diffs and logs to decide whether the work is right.
04
Feed CI failure
Paste the trace, explain what broke, ask again.
05
Ask for the fix
Re-prompt and clarify as session context drifts.
06
Run the review
Surface the gaps yourself, iterate until it holds.
07
Resolve conflicts
Rebase, reconcile, and explain the merge by hand.
08
Merge and follow up
Land it, then chase the loose ends yourself.
That works for one agent window. Maybe two or three. It doesn't scale into governed, organization-level delivery. AgentLoom turns AI capability into coordinated, measurable delivery throughput: it breaks the work into bounded sessions, carries the right state forward, and loops you in only where you gate it. You stop operating and start steering.
What that looks like, end to end
Five scenes, one loop: the team forms around your repo, the backlog flows in, the lifecycle runs, the gates hold — and it keeps shipping for days.
Step 1 of 5 · The setup
A team built for your repo.
AgentLoom reads your codebase and stack, then composes the right roles for the work ahead.
Onboarding runs a deep scan of your repository — languages, frameworks, infra, tests, docs. From that, AgentLoom designs an org chart of role-specialized agents, each carrying only the skills, tools, and permissions your stack actually needs. No generic do-anything agent. No bloated context.
Stack detected from real code, not config guesses
Each role scoped: skills, permissions, tooling
The team evolves as your repo grows
onboarding · reading your repoHover to pause
AgentLoom reads your entire codebase end to end — every file, every config, every doc.
FILE TREE1,284 FILES
src/
└queue/
└index.tsREADING
└worker.ts
└processor.ts
└web/
└app.tsx
└router.ts
└trpc.ts
└api/
└server.py
└routes.py
└models.py
infra/
└main.tf
└variables.tf
└outputs.tf
Dockerfile
docker-compose.yml
pyproject.toml
package.json
tsconfig.json
README.md
AgentLoom maps implementation status — what's built, what's tested, what's only claimed in docs — and surfaces the design gaps.
ANALYZING./src/queue/index.ts01 · 6
01import { Queue } from "bullmq";+bullmq
02import IORedis from "ioredis";
03const conn = new IORedis(env.REDIS_URL);
04const q = new Queue("jobs", { connection: conn });
05export async function enqueue(name, data) {
06 return q.add(name, data, { attempts: 3 });
07}
08
09// queue/worker.ts
10import { Worker } from "bullmq";
11new Worker("jobs", processor, { connection });
12
01import { Queue } from "bullmq";
02import IORedis from "ioredis";
03const conn = new IORedis(env.REDIS_URL);
04const q = new Queue("jobs", { connection: conn });
05export async function enqueue(name, data) {
06 return q.add(name, data, { attempts: 3 });
07}
08
09// queue/worker.ts
10import { Worker } from "bullmq";
11new Worker("jobs", processor, { connection });
12
Stack and tooling auto-detected. AgentLoom builds your team — each role with the skills, permissions, and tools it needs.
Stack detected0/9
redis
bullmq
react
trpc
python
fastapi
postgres
aws
docker
Team composed
FrontendAGENT
· react· trpc
BackendAGENT
· python· fastapi· postgres
PlatformAGENT
· redis· bullmq· docker
InfraAGENT
· aws
dashboard.agent-loom.com · backlog
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P
BACKLOG · ACME/PAYMENTS-API
Active
12 open · 3 high
Open
12
High priority
3
With PR
2
Stale >14d
1
AI-suggested
4
All
12
Mine
4
High priority
3
Bugs
5
AI-triaged
4
P
Add audit-log export endpoint #482
AI TRIAGE RUNNING
Platform
Frontend
QA
agent
api
High
▶ Start run
P
Stripe seat invoicing parity #471
Running
billing
Medium
👁 View run
P
Connector schema validation hardening #468
Running
bug
High
👁 View run
P
Multi-region worker pool failover #455
infra
Medium
▶ Start run
P
Run-detail keyboard shortcuts #441
ux
Low
▶ Start run
P
Hook export to retention scheduler #509
follow-up · from run #482
follow-up
Medium
▶ Start run
Step 2 of 5 · Work flows in
Backlog in, work out.
Roadmap items become scoped, role-assigned execution. Loose ends become new tasks, never lost.
Point AgentLoom at your backlog — GitHub Issues, Jira, monday, Linear, or Asana. AgentLoom scopes each item, assigns the right roles, and dispatches execution. When work surfaces loose ends — a missing test, an unanswered design question — the triager turns them into new backlog items linked to the run that found them.
Items move Ready → In flight → Merged
Every run traceable to its ticket
Loose ends return as tasks, never lost
Step 3 of 5 · The lifecycle
The full lifecycle, end to end.
Plan, code, CI, blocking review, merge, follow-up — every step visible and gated by structure, not convention.
One issue, one continuous run: plan, implement, local tests, CI, multi-role review, merge, post-merge verification, follow-up. AgentLoom carries state across every wait — checkpoints survive CI runs, review rounds, and interruptions — so the loop keeps moving without an operator re-prompting it forward.
Checkpoint-backed: resumes exactly where it left off
Runtime memory carries CI failures and review feedback forward
Gated by structure, not convention
dashboard.agent-loom.com · runs/run-7d2f
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RUN · RUN-7D2F
Add audit-log export endpoint
Running
Plan
0s
team-leader
github.com/your-org/repo · pulls/482
PR #482 · agent/audit-log-export → main
Add audit-log export endpoint
Open
+372
−18
4 commits across 4 role-teams
Backend ✓
Frontend ✓
Security ✓
QA ✓
Backend
feat(audit): emit export job to GCS
14m ago
Frontend
ui(settings): add export status pill
11m ago
Security
chore(audit): KMS-encrypt artifact
8m ago
QA
test(audit): contract + e2e harness
4m ago
✓
ci/build
3m 21s
✓
ci/test
1m 47s
✓
ci/typecheck
28s
✓
ci/lint
12s
✓
All required checks passing
Merge ready · gated by 4 role approvals
Merge pull request
Step 4 of 5 · The gate
All approvals. All checks. All green.
Real GitHub PRs with role-tagged commits and reviews. The auto-merge gate fires only when every role is done.
Everything lands as a real GitHub PR with role-tagged commits. Each assigned reviewer role blocks independently until it's satisfied; the resolver absorbs feedback and pushes fixes until every role approves and CI is green. Auto-merge — if you enable it — is a gate, not a bypass.
Multi-role blocking review, cleared one by one
CI green required before any merge
You choose: human merge or gated auto-merge
Step 5 of 5 · The payoff
Runs for days. Without supervision.
Multiple agent teams ship work across calendar time. Main stays green. Nothing is left behind.
This is the claim single agent sessions can't make. AgentLoom decomposes delivery into many bounded sessions across parallel, isolated environments and keeps the loop moving across calendar time — through CI waits, review rounds, merge conflicts, and post-merge repair. You check a dashboard, not a chat window.
Parallel agent teams across multiple days
Main stays green — repair PRs within minutes
Humans gate only where you configure it
dashboard.agent-loom.com · home
OPS · ACME-CORP
Welcome back, Alex
1 agent in flight
Day 1
→
Day 2
→
Day 3
main · green
Period headroom
72%
Runs · 7d
128
Active repos
5
Pending approvals
1
Live
1
All runs →
acme/api-gateway
Rate-limit retry budgets
Running
acme/storefront
Checkout idempotency keys
Queued
acme/billing
Stripe webhook replay guard
Queued
acme/mobile-app
Offline cart sync
Queued
All runs green · backlog drained
Needs you
1
Inbox →
Security
waiting
Rotate webhook signing secrets
acme/billing
We don't compete with Claude. We compound Claude.
Claude is the agent intelligence. AgentLoom is the orchestration layer above it. As coding-agent runtimes improve, every role in the AgentLoom team gets more capable — and companies still need the layer that decides what runs, when, with what context, permissions, memory, gates, and next state.
Claude gets better
Agent skill improves
Implementation quality, repo reasoning, debugging, and reviews get stronger.
AgentLoom gets better
Team throughput improves
The same orchestration layer drives more capable role-specialized sessions.
You get more
Delivery scales
More work moves through GitHub with governance and less manual operation.
AgentLoom: The product that built itself
Every line of code in AgentLoom was shipped by the same agent teams that ship for our customers.
329K
Lines of code
248
Issues resolved
785
Commits pushed
1496
Multi-role rounds
45
Days running
Clear your backlog.
Connect your repo and your backlog. AgentLoom assembles the team and runs for days, until the work is done.
Boost your delivery.
AgentLoom plugs into your repo and roadmap, runs the lifecycle through GitHub, and ships continuously — without operating every session.
Ship your idea.
AgentLoom sets up the repo, builds the roadmap from your goals, assembles the team, and ships through GitHub end-to-end.