The delivery system for AI coding agents
Steering intelligence. Shipping software.
Agents write code. AgentLoom runs the governed delivery lifecycle — planning, execution, CI, review, merge, repair, follow-up and re-planning across bounded agent runs.
Built on Claude Code — bring your own Anthropic key. Works with GitHub, Jira, and monday (Linear and Asana coming soon). Start free — no credit card.
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 run the loop by hand: start the task, supply context, police permissions, interpret failures, re-prompt, push through review, decide risk, merge, repair, and plan the next step.
01
Start task
Pick the issue and open a session — every piece of work begins by hand.
02
Supply context
Files, docs, skills: you assemble what the agent needs to know.
03
Police permissions
Tools, commands, domains: each request waits on a human click.
04
Interpret failure
Read the logs, paste the trace, explain what broke.
05
Re-prompt
Clarify and ask for the fix as session context drifts.
06
Push through review
Surface the gaps yourself, iterate until it holds.
07
Decide risk and merge
Resolve the conflicts, judge the risk, land it by hand.
08
Repair and plan next
Fix main when it goes red, 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 governed, reproducible delivery throughput: it composes bounded sessions into one lifecycle, carries the right state, context, and artifacts across every boundary, 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 and its harness form around your repo, direction flows in, the lifecycle runs, the gates hold — and delivery keeps going across every interruption.
Step 1 of 5 · The setup
A team built for your repo. A harness derived from it.
AgentLoom reads your codebase and stack, then composes the roles, permissions, and skills the work ahead needs.
Onboarding runs a deep scan of your repository — languages, frameworks, infra, tests, docs. From that, AgentLoom designs an org chart of role-specialized agents and derives the harness each one runs inside: skills shaped by your stack, permissions inferred from your toolchain, output contracts and hooks that reject incomplete work. Where you've made a choice, it's respected; where you haven't — no CI, no security posture — a best practice is put in place.
Stack detected from real code, not config guesses
Each role scoped: skills, permissions, hooks, context
Team and harness re-derive 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
Hover to pause
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 · Direction in
Direction in. Roadmap out.
A north star, a ticket, or "plan this module" becomes scoped, role-assigned execution. Every delivery step feeds what happens next.
Feed work the way you think about it: from your roadmap, from GitHub Issues, Jira, or monday, or in plain language to the assistant — a bug, a feature, a whole module to plan — and it becomes well-formed issues the team can act on. When work surfaces loose ends, the triager turns them into roadmap tasks linked to the run that found them; when the runway drops below a floor, the roadmap re-plans itself. Every part of that is a switch you own.
Items move Ready → In flight → Merged
Loose ends return as roadmap tasks, never lost
Generate and refresh are opt-in, one switch at a time
Step 3 of 5 · The lifecycle
The full lifecycle, end to end.
Plan, code, CI, review against acceptance criteria, merge by risk, follow-up — every step visible and gated by structure, not convention.
One issue, one coherent run across many bounded sessions: implement, run local checks, wait for CI, review against the issue's own acceptance criteria with evidence, merge under a risk ceiling, verify main, close out with follow-ups in the same PR. AgentLoom carries state, context, and artifacts across every boundary, so the loop keeps moving without anyone re-prompting it forward.
Checkpoint-backed: resumes exactly where it left off
Each round briefed with the CI failure or review findings it needs
Gated by structure, not convention
dashboard.agent-loom.com · runs/run-7d2f
Hover to pause
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. Merge waits under the risk ceiling you set.
Everything lands as a real GitHub PR with role-tagged commits. Reviewers check the issue's acceptance criteria with evidence and score the risk; the resolver absorbs findings and pushes fixes until the review clears and CI is green. Auto-merge is a 0–100 risk ceiling, not a switch: low-risk PRs land on their own, and anything above your line waits for you.
Review against acceptance criteria, with evidence
CI green required before any merge
A risk ceiling you set, per repo
Step 5 of 5 · The payoff
Built for interruption, not just execution.
CI waits, rate limits, outages, killed containers: every stop is a pause with a resume path. Main stays green.
Real delivery is full of interruptions. AgentLoom treats them as part of the lifecycle: state survives the stop, transient failures heal themselves, a rejected key fails over to a standby, and what only you can decide arrives as one actionable gate. Agents work in sessions; the delivery is continuous. You check a dashboard, not a chat window.
Resume, recover, fail over — or ask
Main stays green — repair PRs within minutes
You 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 engineer. AgentLoom is the engineering organization around it — planning, review policy, CI gates, merge rules, the repair loop, and the roadmap that keeps it busy. Run at scale and unattended inside that lifecycle, every gain in Claude compounds into delivered software.
Claude gets better
Agent skill improves
Implementation quality, repo reasoning, debugging, and reviews get stronger.
AgentLoom gets better
Team throughput improves
The same system drives more capable role-specialized sessions through the same governed loop.
You get more
Delivery scales
More work moves through GitHub with governance and no one operating a session.
AgentLoom: The product that built itself
From an empty repository to a fully functional production release in 23 days. Every line of code in AgentLoom was shipped by the same agent teams that ship for our customers.
1.1M
Lines of code
607
Issues resolved
1,299
PRs merged
2,798
Multi-role rounds
175
Days running
Clear your backlog.
Connect your repo and your backlog. AgentLoom composes the team and keeps the loop running until the work is done.
Boost your delivery.
AgentLoom plugs into your repo and roadmap, runs the lifecycle through GitHub, and ships continuously — without anyone operating a 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.