Run your AI coding agents as one managed engineering team.
Your developers already run Claude Code and Codex. Kadmo runs them as one coordinated team: shared role based agents for QA, frontend, backend and senior review. On your infrastructure, live inside Jira and GitHub, on any LLM.
That AI makes you faster and better is settled. Running it well is not.
All of this is barely a year old. Every team is still in the experimentation phase and the same problems show up everywhere. Hover over a problem for the detail.
Everyone is somewhere on this path.
You are not alone: most companies sit between two and three, assuming the answer is more licences.
Private assistants
Everyone has a chat window and types faster. Nothing is written down, nothing is shared, nothing survives the person who found it.
One developer, one agent
A senior runs an agent on one task at a time. Real output, but bound to one person, one machine and one context.
Orchestrated agent teams
Roles split the work, playbooks define the steps, gates hold the quality, one machine per agent. Knowledge is written once and every agent loads it.
Kadmo. The orchestration layer for your agent fleet.
One layer between your boards and any model. Hover over a layer for the detail.
Your boards
Jira, Linear, GitHub, Slack. Work starts as a ticket on your board and comes back as a reviewed pull request. No new tool for your team.
Agent teams
Role based teams, each agent on its own virtual machine in its own branch. Work is broken into subtasks per agent, so every context window stays small and sharp. They build, review, test and merge in parallel, around the clock.
Kadmo
The control plane. Roles, playbooks, guardrails and skills are defined once and every run loads them. Full visibility of progress and cost per agent.
Git
GitHub or GitLab as the system of record. Code, pull requests, docs and skills land here and stay updated automatically with every run.
Any LLM
Plug in Claude, Codex, Gemini or self hosted models. Swap them anytime without losing context. No lock in.
You drop the ticket. The fleet takes it from there.
One live playbook run, from ticket intake to a verified release. Hover over a step for the detail.
Plan and split the ticket
1 / 4Build and open PRs
2 / 4Review and merge the PR
3 / 4Regression test on the live site
4 / 4Roles
Compose the team: product, QA, frontend, backend, senior review. Each role knows what it may touch.
Playbooks
Define the work once. Every ticket of that kind then runs the same way, with the same gates.
Live monitoring
Watch the fleet, the cost and the progress per agent in real time, with the full transcript.
It’s already working and GoStudent even posted their success story.
Live in production: GoStudent, one month of data.
The Kadmo advantage.
Any model, no lock in
Move between Claude, Codex, Bedrock, Vertex or self hosted anytime, with no loss of context or agent quality.
Your data never leaves
Agents run in your own infrastructure, behind your firewall. Code, tickets and credentials stay in your perimeter.
Quality holds as you scale
A senior review gate sits inside every playbook, so more output does not mean more incidents.
Scales with your fleet
Every agent runs on its own VM. Add agents to add capacity, without adding headcount.
Skill discovery that compounds
Agents learn your processes automatically, understand your stack and improve every week.
Live in days, not months
Connect the tools you already use and run your first agent team fast. No rip and replace.