The workspace for Team-based agentic software development.
Define, plan, and ship software with AI in one shared workspace. Plans are reviewed before execution. Work is visible as it runs. Every run improves the next.
One system. Requirements, plans, code, and validation, all shared across your team and agents.
Product
Define requirements that stay attached to execution, not scattered across docs and threads. Collaborate on implementation plans before code is written. Ship bug fixes and small features without consuming engineering sprints. See progress in real time with no status meetings required.
Design
Skip dev handoff and ship visual improvements directly. Prototype new screens and features inside existing functional apps, polish and refine what engineering delivers, and edit components directly to enforce design system consistency.
Engineering
Build from structured, team-reviewed implementation plans and run multiple agent workflows in parallel with shared context. Manage agent context, not just within produced artifacts like PRDs but also across them. Maintain quality through visible execution and receiving reviews from agents and teammates before merging.
Built for control, not just speed
Speed without control creates risk. Every step is visible, reviewable, and enforceable so teams can trust what ships.
Plans are reviewed before execution
No code runs until the team aligns on how it will be built.
Work is visible while it runs
See progress, changes, and outputs in real time across the team.
Outputs are validated before merge
Every result is reviewed against the original intent before shipping.
Background job queue migration
Implementation Plan: Background Queue Migration
Summary
Migrate transactional emails and webhook delivery off the legacy queue with no downtime. Goal: cut p95 worker latency by 40% and remove the single-broker dependency in the worker tier. Rollout follows a dual-write → shadow-compare → consumer cutover sequence with a flag per consumer for fast revert.
Scope:
- In-scope: webhook delivery, transactional emails, retry policy, dead-letter handling, and per-consumer cutover flags.
- Out-of-scope: analytics events pipeline, user-facing notification preferences, and the realtime presence channel.
Implementation Steps
- ✓
Add new queue adapter
Wire the SQS adapter behind the existing Queue interface. No callers change yet.
- ✓
Dual-write events
Publish to both the legacy broker and SQS. Compare deliveries via the shadow consumer.
- 3
Cut over consumers
Move webhook + email workers to read from SQS once shadow parity is ≥ 99.95%.
- 4
Decommission legacy broker
Remove the old adapter, drop infra, and update on-call docs.
You're paying for unused capacity
Most teams underutilize their AI subscriptions. ClosedLoop.ai keeps agents running on real work with the right context, so you consistently hit usage limits on productive tasks instead of wasting capacity on isolated sessions.
- Run multiple agent workflows in parallel instead of one session at a time
- Use shared context so agents don't restart from scratch each task
- Keep agents working continuously across real features, bugs, and plans
- Run work across different models and runtimes without manual switching.
Start building with your team today
Move from isolated AI sessions to a shared system your whole team can rely on. Start with the hosted version or explore the docs to learn how it works.
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