An AI agent squad,
wired into your board.
We drop a PM, Engineering, and QA agent into your Jira, Linear, or GitHub Projects. They plan, build, and test tickets on the board your team already uses — with human approval at every gate.
No migration. No new tools. Every agent action traceable on your board.
The problem
AI can write code.
But code isn't a shipped ticket. Intake, handoffs, QA, release — that's most of the work. And your board is the place it all breaks.
Tickets stall between planning and engineering
Specs land in Jira. Engineers ask three clarifying questions in Slack. The ticket sits for two days while PM and eng re-sync.
QA finds context too late
By the time QA picks up the PR, the why has already been lost. Bugs come back. Context gets rebuilt from scratch.
Your knowledge base is a graveyard
Specs, architecture notes, onboarding docs — written once, stale a month later. Nobody trusts them, so nobody reads them.
AI coding assistants stop at the IDE
Copilot writes a function. But who writes the ticket? Who runs the tests? Who updates the doc? Still all on you.
The gap isn't individual productivity. It's execution across the full ticket lifecycle — and that's what the Kickstart fixes.
What the Kickstart delivers
A squad that ships, not a chatbot that suggests.
Four outcome-led capabilities. All live on your board by handover.
Ships tickets end-to-end
- Agents move tickets from intake → merged PR on your board
- PM writes the spec, Engineering opens the PR, QA posts the verdict
- Every transition visible in Jira or Linear and Slack
Lives in your existing workflow
- Runs on Jira, Linear, or GitHub Projects — no migration
- Reads and writes Confluence, Notion, or Linear docs as it works
- Notifies and takes approvals in the Slack channels you already use
Human approval at every gate
- Your gates, your quality bar — configurable per ticket type
- Nothing merges without a human sign-off you control
- Every agent decision auditable after the fact
Knowledge base that maintains itself
- Agents update specs and runbooks as tickets progress
- New architecture decisions written back as persistent context
- Next sprint's agents inherit what this sprint learned
What changes when your workflow gets an AI execution layer
Before
- Tickets stall between planning and engineering
- QA finds issues late because context was lost in handoffs
- Specs are outdated by the time development starts
- Release coordination is manual and error-prone
- Developers context-switch between task tracker, Slack, docs, and code
After
- Coordinated agents move tickets through each workflow stage
- QA has full context from requirements through implementation
- Knowledge bases are continuously updated as work progresses
- Release management follows explicit gates and checklists
- One execution system connects everything on your existing board
How it works
One productized engagement — the Agent Team Kickstart — in three phases, from workflow to a shipped ticket.
Workflow discovery
Days 1–2
We map how work actually moves through your team today, flag the handoffs where context gets dropped, and pick the pilot ticket the squad will ship.
Agent setup
Days 3–9
PM, Engineering, and QA agents go live one at a time on your real Jira, Linear, or GitHub Projects board. Every handoff visible in Slack, every approval gated by humans.
Work complete
By Day 10
The pilot ticket ships — intake to merged PR — through the squad. You keep the agent configs in your repo. Optional $1,000/month retainer keeps the squad tuned.
Who This Is For
Any team with engineering work that needs to get done — from early-stage startups to established engineering organizations. If you want AI agents to ship tickets, not chat, we'll work with you.
Best fit
- Any team with engineering work that needs to get done
- Teams that want AI agents to actually ship tickets, not just chat
- Teams that have tried AI coding assistants but still struggle with end-to-end delivery
- Engineering leaders who need faster throughput without adding headcount
Less fit
- Teams without established workflows to build on
- Organizations looking for a simple chatbot or coding assistant
- Teams without existing task tracking infrastructure