Engineering decisions don't happen in IDE sidebars. They happen in Slack threads, in the same place your team is already talking, debugging, already working through issues together.
That disconnect is a problem.
The conversation about what to build happens in one place, and the actual building happens somewhere else entirely. Context gets lost. Decisions get re-explained. Someone inevitably copies an issue into an AI chat somewhere and pastes the answer back, and everyone pretends that's a normal workflow.
Kilo just shipped something that actually addresses this: a Slack integration that turns those threads into working code. The team behind it has been using the bot internally for months, and it's fundamentally changed how they build and ship.
TL;DR: Kilo for Slack lets you @mention an AI coding agent directly in your team's conversations. It reads the full thread context, connects to your GitHub repos, and can answer questions or open PRs without anyone leaving Slack. No more re-explaining bugs to your IDE after you already hashed them out with your team. Try Kilo for Slack.
The Context-Switching Tax
Here's a scenario that's probably familiar.
Someone reports a bug in a team Slack channel. Three engineers chime in with theories. A developer scrolls through the thread, absorbs the context, then alt-tabs to their IDE. They open their AI coding agent of choice and start explaining the situation again from scratch. They paste the error. They describe what's been tried. They provide the context that was already in that Slack thread, except now they're typing it again.
The fix gets implemented. A PR goes up. Then, it's back to Slack to share the link and summarize what changed.
This happens dozens of times a week on active engineering teams. And every time, there's a friction tax. The context transfer, the re-explanation, the mental gear-shifting between "discussion mode" and "implementation mode."
It's not a huge tax for a single instance, but it adds up. And more importantly, it creates an artificial boundary between where decisions get made, and where work gets done.
What Kilo for Slack Actually Does
Here's the new workflow. Take the same Slack thread: the bug has been discussed, theories have been floated, and there's rough consensus on what needs to happen. Instead of context-switching to an IDE, you can just tag @Kilo:
@Kilo based on this thread, can you implement the fix
for the null pointer exception in the Authentication service?
The bot reads the entire thread, and since it's connected to the team's GitHub repos via the Kilo platform, it spins up a cloud agent. A few minutes later, there's a PR link right there in Slack.
No copy-pasting. No alt-tabbing. No explaining the same thing twice. The context that already existed in the conversation becomes the input for the implementation.
For simpler questions, it works the same way:
@Kilo how is error handling implemented in the payment module?
It reads your codebase and responds in the thread. Teammates can see the answer, and you can ask follow-ups and give implementation instructions. The knowledge stays in Slack, where it can be referenced later.
How the Kilo Team Actually Uses This
Kilo is aggressive about dogfooding their own tools. The team has been using Kilo for Slack internally since before the public launch, and it's become the default way a lot of changes get made.
The patterns that emerged are more varied than expected:
1. Real-Time Bug Fixes
Possibly the most obvious use case, and the one that gets hit constantly. An error shows up in production. Someone flags it in Slack. The team discusses what might be causing it. And then instead of someone volunteering to "take a look at it," they just tag @Kilo.
The key thing: the bot isn't starting from scratch when it reads that issue. It has the full context of the conversation, and access to the entire codebase. It can read what the team suspects, and what's been ruled out. It knows what the expected behavior should be. It's working with the same information a human developer would have after reading the thread:
@Kilo I'm seeing this error in production: [stack trace].
Based on what we discussed above, can you create a PR with a fix?
The PR lands in the thread. Someone reviews it. If it looks good, it gets merged. The whole cycle happens without anyone formally "picking up" the task.
2. Quick Code Changes From Discussions
This is something that has become a day-to-day experience for many people at Kilo. A conversation happens about a feature or behavior. Someone says "we should probably change X to Y." In a traditional workflow, that becomes a mental note, or a ticket, or something that gets handled "later."
With the Slack bot, "later" becomes "right now." The person who had the idea just tags Kilo and describes what should change. The context of why it should change is already in the thread:
@Kilo please change "2025" to "2026" through all of the announcement
files in our kilo-org/kilocode repo
For a team moving fast, this matters. The friction on small changes is what causes them to pile up. Removing that friction means the codebase stays cleaner and more current.
3. Documentation and Content Updates
This one applies to people in less-technical or non-technical roles. The Kilo team uses the bot for all sorts of changes across the entire Kilo platform constantly. Landing page copy, handbook entries, documentation updates, README improvements.
The pattern is the same: a discussion happens in Slack about what needs to change, and then the bot implements it. No one needs to context-switch into "writing mode" or open a different tool:
@Kilo the getting started guide is missing the new
authentication flow. Can you update it based on what
we discussed in this thread?
For content that lives in a repo (which, if you're doing docs-as-code, is most of your content), this workflow is a huge time-saver. Especially for people who feel overwhelmed diving into a development workflow just to make a simple landing page change. The PR process gives you the same review mechanism used for code, and the Slack thread provides the ease-of-access.
4. Implementing Features From Spec Discussions
Sometimes a thread evolves from "should we do this?" to "here's roughly how it should work" to "okay, let's actually build it." The bot can handle that transition:
@Kilo please implement the caching improvements
we discussed in this thread
This works best when the thread contains enough specificity. The bot is good at inferring intent, but clearer context leads to better output. The Kilo team has gotten into the habit of being explicit in discussions, especially when the conversation might become the spec.
5. Cross-Repo Coordination
Real engineering work usually spans multiple repositories. Frontend, backend, shared libraries, infrastructure configs. Unlike some other Slack integrations, Kilo automatically infers which repo is being referenced. Multiple repos can be mentioned in the same thread and it figures out what goes where:
@Kilo the API change we discussed needs updates in both
the backend service and the frontend client. Can you
create PRs for both?
No manual configuration per channel. No switching context to specify which repo to use. It reads the thread, understands what's being referenced, and acts accordingly.
Why This Is Different From Other Slack Bots
A lot of AI Slack integrations feel like novelties - they're general purpose AI's trying to masquerade as a specialist in every category. They answer questions, maybe generate some code snippets, but the moment something non-trivial comes up, it's back to copy-pasting into an IDE.
Kilo's approach is architecturally different in ways that matter.
Multi-Turn Conversations
Most AI Slack bots are designed for one-shot interactions. Ask a question, get an answer. Follow-ups essentially start a new conversation.
Kilo builds on the entire thread. It maintains context across multiple exchanges. A back-and-forth discussion can happen, the approach can be refined, clarifying questions can be asked, and then implementation can be triggered. That context carries through.
This mirrors how human conversations work. No one re-explains the entire situation every time they add to a discussion. The bot shouldn't require that either.
Multi-Repository by Default
Cursor's Slack integration requires configuring a single repository per workspace or channel. That's fine for simple setups, but it breaks down quickly when engineering work spans multiple repos.
Kilo infers the relevant repository from the conversation. If files or services that live in different repos get mentioned, it handles that. No upfront configuration. No switching between channels to work with different codebases.
This seems like a small thing until you've worked on a project where the frontend, backend, and infrastructure all live in separate repos. Then it becomes essential.
Actual Execution, Not Just Chat
This is the fundamental difference. Kilo for Slack isn't a Q&A bot. It's an execution layer.
When asked to implement something, it spins up a cloud agent, creates a branch, makes the changes, and opens a PR. It's doing the work, not just talking about the work.
And because it's using Kilo's cloud agents, no local machine is involved. The repo doesn't need to be cloned locally. The implementation happens in the cloud and the result shows up as a PR ready for review.
Continuous Context With PRs
Once a PR exists, the bot can continue to work on it. If review feedback comes in, Kilo can be asked to address it in the same thread. The conversation about the PR and the implementation of changes happen in the same place:
@Kilo the reviewer asked for better error handling
in the auth flow. Can you update the PR?
This is closer to how pair programming works. There's a continuous conversation about what's being built, and the code evolves in response to that conversation.
The Technical Details
For those curious about how this actually works under the hood:
When @Kilo gets mentioned in a channel or DM, the bot reads the thread context. It accesses connected GitHub repositories (set up once in the Kilo dashboard). Based on the request, it either responds with information or kicks off a cloud agent to make changes.
The Cloud Agents are the same ones available from the Kilo CLI or dashboard. They run in Kilo's infrastructure, create branches, make commits, and open PRs against repos. The results get posted back to the Slack thread.
Pricing is usage-based, with the same per-token cost as using the model directly through Kilo - meaning that you're only charged at exactly the prices set by model providers.
Setting It Up
Setup takes about two minutes:
- Create a Kilo account (free to start)
- Connect GitHub repos in the Integrations tab at app.kilo.ai
- Add the Slack integration from the same Integrations page
- Start mentioning or DM-ing @Kilo in the workspace
The bot can be DM-ed directly for private questions, or mentioned in any channel where it's been added for team-visible interactions.
The GitHub connection is the important part - and takes about 10 seconds and 2 clicks. The bot needs access to repos to answer questions about the codebase and to create PRs.
What To Use It For
Some patterns have emerged for where this shines:
- Quick fixes and small changes. The overhead of opening an IDE, finding the right file, making a change, and pushing a PR is high relative to the work itself. The Slack workflow collapses all that overhead.
- Changes that emerge from discussions. When the "what" and "why" are already captured in a thread, it feels natural to just add the "do it" at the end.
- Documentation and content. Anything that lives in a repo but isn't strictly code. READMEs, guides, configuration files, landing page copy.
- Multi-repo coordination. When a change needs to touch multiple repositories, managing that from a single Slack thread is cleaner than bouncing between IDE windows.
- Mobile and async situations. A PR can be kicked off from a phone. The work happens in the cloud. Review happens later.
When the IDE Still Wins
This isn't a replacement for a development environment. It's a complement.
- Complex features with lots of back-and-forth. Rapid iteration, local testing, and real-time refinement still want the IDE or Kilo CLI.
- Deep debugging sessions. Stepping through code, inspecting state, and understanding behavior requires full tooling.
- Major refactors. Large-scale architectural changes benefit from the complete context an IDE provides.
The mental model: Slack-first for changes that emerge from conversations, IDE-first for changes that require deep engineering.
The Broader Platform
Kilo for Slack is part of Kilo's larger, end-to-end agentic engineering platform. Kilo is already accessible in VS Code, JetBrains, the Kilo CLI, and Kilo's Cloud Agents. The Slack integration is another entry point. The same platform, the same 500+ model choices, and the same quality, just accessible from a different surface.
When you're done building in Kilo, you can also enable AI-powered Code Reviews, one-click deployments, and even share your sessions across your team with Kilo Sessions.
For teams evaluating AI coding tools, this is worth thinking about. It's not only about which tool generates the best code - which is undeniably important - but it's also about which tool fits into the actual workflow with the least friction. If a team lives in Slack, having AI that can participate in those conversations without requiring context-switches is a meaningful improvement. It's one less boundary between where decisions happen and where they get implemented.
The place where teams discuss code can now be the place where code gets written. The discussion and the implementation happen together. The context flows naturally from one to the other.
That's the kind of workflow improvement that compounds over time.
Kilo is an open-source AI coding agent with over 1 million users. It's available in VS Code, JetBrains, and CLI, Cloud Agents, live-preview App Builder, one-click deployments, automated code reviews, and now Slack integration. Learn more at kilo.ai.