Stilla: The AI Teammate You @Mention in Chat and It Ships the Work
Most work does not die in the doing, it dies in the gap between the conversation and the doing. Someone says "we should ticket that," a decision gets made in a thread, a follow-up email is owed, and by Thursday it is all archaeology. Stilla lives inside the chat where those moments happen. Mention it like a colleague, and the conversation becomes the ticket, the pull request, the report, the email, done.
The short version
Stilla is a multiplayer AI agent that joins your team's chat and connects to the tools where work lands, Linear, GitHub, your CRM, email and more. It tracks what was said, what needs doing and what is already in motion, then turns conversations into finished output: tickets filed, code reviewed and PRs opened, summaries posted, follow-ups sent, drafts written. Every action can start as a proposal your team approves, or run autonomously inside guardrails you set, and a shared memory means it never loses the thread across tools.
Work where the conversation already is
The interface is the part teams underestimate. There is no new app to adopt: someone types the mention mid-thread, "@Stilla ticket this bug with the details above," and the ticket appears with the context filled in from the discussion. Ad copy, a weekly report, a customer reply, a code fix, the pattern is identical. Because the whole team sees the same agent in the same channels, the work it does is visible, correctable and shared, not siloed in one person's private chatbot.
Proposals first, autonomy earned
Stilla's trust model fits how teams actually adopt AI. By default, actions arrive as proposals, here is the PR I would open, the email I would send, and a human clicks approve. As confidence grows, you loosen the guardrails task by task until routine work runs on its own and only judgment calls surface. That reviewable trail is what makes it safe to hand real responsibilities, not just drafts, to an agent.
A memory the whole team shares
Under the chat interface sits the compounding asset: shared memory across every conversation and connected tool. Stilla remembers the decision from three weeks ago, the state of the ticket it filed, the promise made to the customer, so its output lands with context no fresh chatbot session could have. For product and engineering teams living across Slack, Linear and GitHub, it behaves like the one teammate who somehow read everything. An MCP server lets you wire in additional tools through the open protocol when your stack goes beyond the built-ins.
Where it shines
- Works by mention inside the chat you already use
- Turns threads into tickets, PRs, reports and emails
- Proposal-and-approve flow before any autonomy
- Shared memory across tools and conversations
- Code review with full codebase context
- Extensible through an open MCP server
Worth knowing
- Priced per organization, best value for real teams
- Deepest fit is product and engineering stacks
- Guardrails deserve setup time before autonomy
Common questions
Does everyone on the team use the same agent?
Yes, that is the multiplayer part. One Stilla joins your workspace, sees the shared channels you allow, and anyone can hand it work by mentioning it.
Can it really write and review code?
Yes, it reviews changes, suggests fixes, opens pull requests and responds to review comments with context from your repository. Approval gates keep merges in human hands until you decide otherwise.
What about our data?
The company states customer data is not used to train models, with contractual enforcement, and offers regional hosting options. Review the current security terms against your own requirements before rollout.
Bottom line
Stilla bets that the best interface for an AI worker is the place your team already talks, and the bet pays off daily in tickets that file themselves and threads that end in shipped work instead of intentions. Invite it into a couple of channels, keep proposals on, and count what it closes in week one.