vs. coding agents
Agents build, teams decide
Coding agents keep getting better at building. What to build is decided by the team, together, and that reasoning needs a home of its own.
Speed is solved. Alignment isn't.
Coding agents already multiply what a team can build. The decisions still belong to the team: weighing the options together, against the real product, and keeping the reasons on record. Adding AI features to tickets and documents won't get you there, because the software development lifecycle itself has to be reinvented around AI. Kairo is built for that new lifecycle: the AI-native SDLC.
The layer your agents and tools don't cover
Coding agents execute. Developer spec frameworks live in the repository. Tickets track work. Kairo is where the team thinks it through together, from defining intent to checking what shipped against the acceptance criteria agreed up front.
vs. developer spec frameworks
Agent features are single-player
Instruction files, plan modes, and spec frameworks are written by developers, for agents. Deciding what to build involves people who will never open a terminal.
vs. tickets
Tickets assume the thinking is done
Linear and Jira were designed to orchestrate work among humans. The ticket is what you tell the team to do; the spec is how you know it's the right thing.
One source of truth
Every requirement, in one place
Fragmentation isn't counted in tools. It's counted in places where the truth about a requirement can diverge. Today a single feature's truth spans a Notion draft, a Jira epic, four Slack threads, and two meetings. In Kairo, each change request has one requirements document, and that document is the single source of truth, for your team and for your agents.
How Kairo compares with docs and tickets
| Aspect | Docs & tickets today | The Kairo way |
|---|---|---|
| Unit of work | Static prose plus atomized tickets | A living, execution-ready specification |
| Codebase awareness | None: the truth drifts from the repo | Grounded in the actual code, patterns, and constraints |
| Ambiguity | Discovered mid-implementation | Settled in an AI-led dialogue before the document is written |
| Source of truth | Spread across docs, tickets, and threads | One requirements document, for the team and its agents |
| Agent handoff | Requirements pasted into prompts by hand | Specs served to any coding agent over MCP |
| Team model | Sequential: write, then translate, then clarify | Multiplayer: product, design, and engineering in one spec |
| User feedback | In a separate tool, disconnected from the specs | Suggested change requests from Contributors |
Questions
What teams ask us
Who is Kairo for?
AI-forward software teams, typically 5 to 50 people across product and engineering, where coding agents already accelerate implementation and the team wants AI to strengthen its product decisions as well.
What is an AI-native SDLC?
A software development lifecycle where AI amplifies every step, not just coding. Agents write more and more of the code, and AI also helps teams explore options, understand the existing product, and decide what to build. In an AI-native SDLC, the spec replaces the ticket as the central artifact: it is grounded in the codebase, agreed by the whole team, read directly by coding agents, and used to review what shipped. Kairo is the workspace built to run that lifecycle.
Where does Kairo fit next to the tools we already use?
Notion and Confluence document the project, statically. Jira and Linear slice the work into tasks. In between, where the what, the why, and the how actually get decided, there's usually nothing but meetings, comments, and Slack threads. Kairo is built for that layer, and it works on its own: you don't need a separate tracker or document tool to run it.
How is Kairo different from coding agents and spec frameworks?
Coding agents execute specs, and developer spec frameworks organize them inside the repository, for developers. Kairo is the product layer above both: product, design, and engineering define the intent together, the spec is grounded in the codebase, any agent reads it over MCP, and the agent checks its work against the acceptance criteria agreed in Kairo. Your agents can change; your decisions stay in Kairo.
Does Kairo replace Jira or Linear?
It can. Kairo runs the whole flow on its own: change requests move across its board from defining to ready, building, and done, agents read specs over MCP, and lightweight tasks cover what's left for humans. Fully agentic teams often find they no longer need a separate tracker. Nothing has to be ripped out on day one, though: you can keep Jira or Linear for as long as it's useful, and send change requests to Jira with their status visible in Kairo.
We built our own spec system in Notion, with templates and databases.
Then you already feel the problem Kairo solves. But a template can't settle ambiguities before handoff, and a database can't read your repo. You've built the process; Kairo is the product it's reaching for. You can upload your existing PRDs as context from day one.
Can we adapt Kairo's AI to how we work?
Yes. Kairo comes with pre-defined skills for recurring work, such as defining a change request, planning it, or reviewing an implementation against its acceptance criteria. You can also create your own skills and AI assistants, and generate requirements documents from your own template.
Which coding agents does Kairo work with?
Any agent that speaks MCP. Kairo's MCP server exposes each change request's definition, plan, and linked documents, so Claude Code, Copilot, Codex, Cursor, or any other MCP client can read them directly.
What does Kairo connect to?
GitHub and GitLab for repositories, files, branches, pull requests, and issues. Figma for design context and Slack for context from conversations that already happened there. Jira, to send change requests to your delivery tracker and follow their status from Kairo. You can also upload existing documents, and connectors for Confluence, SharePoint, Notion, and Microsoft Teams are coming soon. On the other side, Kairo's MCP server gives external coding agents access to your specs.
Can our customers send us feedback through Kairo?
Yes. Invite customers, stakeholders, or domain experts as Contributors. They submit features, improvements, and fixes as suggested change requests and follow the status of each one, without access to the rest of your workspace. Your team accepts or declines each suggestion, and overlapping requests are merged into the existing change request.
Where does Kairo run, and what happens to our data?
Kairo is built and hosted in Europe with GDPR-compliant data handling. Your data is never used to train any AI model, ours or anyone else's. Integrations connect through secure OAuth and service accounts, and people and AI assistants only reach the projects they belong to.
Put your next feature through the Kairo workflow
Join the pilot and see the difference between a ticket and a spec that knows your codebase.