Slack-First Engineering: Why Your AI Agent Should Live in Chat
Most AI coding agents live in the IDE. You open Cursor, Claude Code, or Codex, describe what you want, and the agent works in the context of your local development environment. This model works well for individual developers but breaks down for teams. The agent's work is invisible to everyone except the person who triggered it. There is no shared context, no built-in approval workflow, and no audit trail for the rest of the organization.
We believe the most impactful AI coding agents will live in Slack, not because Slack is a better coding environment (it is not), but because Slack is where engineering teams already coordinate work.
The visibility problem
When an engineer uses an AI coding agent in their IDE, the interaction is private. The agent's reasoning, its research process, its implementation plan, and its code changes exist only on that engineer's machine until they push a PR. For small tasks, this is fine. But for anything that affects the team, the lack of visibility creates problems.
Other engineers do not know what is being worked on. The tech lead cannot see whether the agent's approach aligns with team conventions. New team members miss the opportunity to learn from watching the agent's problem-solving process. And when something goes wrong, there is no shared record of how the agent arrived at its decisions.
Slack-first engineering solves this by making agent work visible by default. When Memi works on a task, every step of the process is posted in a thread that anyone in the channel can follow. The research phase, the plan, the progress updates, the test results, and the final PR are all visible. This transparency is not just nice to have; it changes how teams work with AI.
Natural approval workflows
One of the most underappreciated aspects of Slack-first AI agents is the natural approval workflow. When Memi receives a task, it researches the problem and posts a plan in the thread. It then waits for human approval before writing any code. This is not a separate approval system bolted on after the fact; it is a natural consequence of the conversational interface.
The engineer who requested the task can review the plan. The tech lead can weigh in. A domain expert can flag potential issues. All of this happens in the same thread, in the same tool everyone already uses, with no context switching required. If the plan needs adjustments, you just reply in the thread and Memi revises.
Compare this to IDE-based agents, where the approval workflow is typically “look at the diff and decide if you want to accept it.” By the time you see the diff, the agent has already invested compute in a potentially wrong approach. Slack-first approval catches misalignment before any code is written, saving both time and compute costs.
Accessibility beyond the IDE
Not everyone on an engineering team lives in an IDE. Product managers, designers, QA engineers, and engineering managers all contribute to the software development process, but few of them have Cursor or Claude Code installed. Slack, however, is universal.
A Slack-first AI agent is accessible to the entire team. A product manager can post “@memi add a feature flag for the new onboarding flow” without needing to know which IDE the team uses. A QA engineer can ask “@memi write E2E tests for the checkout page” without setting up a local development environment. This democratizes access to AI-assisted development and removes the bottleneck of routing every request through an engineer with the right tooling.
The context advantage
Slack is where engineering decisions are discussed, technical trade-offs are debated, and project context accumulates. When an AI agent lives in Slack, it has access to this conversational context. It can read the thread where the team discussed the authentication approach. It can reference the channel where deployment issues were triaged. It can pull in the context from the Linear issue that was linked in the message.
IDE-based agents, by contrast, only have access to the code and whatever context the user explicitly provides in the prompt. They miss the rich conversational context that explains why the code looks the way it does. This is not a minor gap. Understanding the “why” behind code decisions is often more important than understanding the code itself.
Building a Slack-first culture
Adopting a Slack-first AI agent is not just a tooling decision; it is a cultural shift. Teams that embrace it find that work becomes more transparent, communication becomes more structured, and institutional knowledge accumulates faster. The Slack threads created by AI agent interactions become a searchable archive of how and why things were built.
We have seen teams develop new habits around Slack-first engineering. They create dedicated channels for different types of agent work: #memi-features for new features, #memi-bugs for bug fixes, #memi-refactor for cleanup tasks. They use thread approvals as lightweight code review gates. They reference past Memi threads when onboarding new team members.
The hybrid approach
Slack-first does not mean Slack-only. The best engineering workflows combine Slack-native agents for team-visible tasks with IDE-based agents for individual coding sessions. Use Memi for features that need team input and approval. Use Cursor or Claude Code for rapid prototyping and local exploration. Let Memoire's shared memory layer keep everything in sync across both interaction models.
The key insight is that the interaction model should match the nature of the work. Solo exploration belongs in the IDE. Team work belongs in Slack. And both should contribute to a shared memory that makes every subsequent session more effective.
The era of the isolated AI coding assistant is ending. The future belongs to AI agents that are embedded in the communication fabric of engineering teams, visible to everyone, accountable to the team, and getting smarter with every interaction.