How to Build an AI Agent for GitHub and Slack

The workflow problem

Engineering teams live in Slack during the workday but manage their code in GitHub. The gap between these two systems creates constant context switching. A developer opens a pull request and then has to post in Slack asking for a review. Reviewers see the Slack message, switch to GitHub, find the PR, read the changes, leave comments, then switch back to Slack to continue the conversation. When CI fails, someone notices in GitHub and manually posts a message in the team channel. When a deployment goes out, the on-call engineer sends a “deployed v2.4.1” message that half the team misses.

The existing GitHub-Slack integration provides basic notifications, but they are noisy and context-free. A channel subscribed to a busy repository receives dozens of notifications per hour: every push, every comment, every CI status update. Teams either mute the channel entirely or drown in alerts. There is no intelligence deciding which events matter, who should see them, or what context to include.

The cost is measured in engineering time and incident response speed. When a production deployment fails and the alert appears as one notification among fifty, the mean time to detection increases. When a critical PR sits without review because the team did not see the notification in a crowded channel, the development velocity drops. These are not hypothetical concerns; they are the daily reality for engineering teams at any reasonable scale.

Why an AI agent, not just automation

GitHub’s built-in Slack integration sends every event to a channel with identical formatting. An AI agent fundamentally changes the signal-to-noise ratio by understanding which events are important and how to present them.

When a PR is opened, the GitHub integration posts a one-line notification with the PR title. An AI agent reads the PR diff, identifies that it touches the authentication module, recognizes that this is a security-sensitive area, and posts a detailed message tagging the security team lead and the auth module owner with a summary of what changed and why it matters. It includes the number of files changed, whether tests were added, and whether CI passed.

The agent also enables conversational interactions. Instead of switching to GitHub to check CI status, a developer types “what’s the status of my open PRs?” in Slack and the agent responds with a summary of each PR, its review status, and any failing checks. A team lead can ask “which PRs have been open for more than three days?” and get an actionable list. A DevOps engineer can type “deploy staging” and the agent triggers a GitHub Actions workflow while posting progress updates in the channel.

For incident response, the agent correlates deployment events with error spikes. It does not just say “deployment succeeded.” It monitors the deployment’s GitHub Actions workflow, and if the workflow includes integration tests that fail post-deploy, it immediately escalates in Slack with the failure details and a link to the relevant logs.

How it works with ACP

ACP provides your agent with scoped access to GitHub’s repository data and Slack’s messaging capabilities, enabling it to bridge code management and team communication.

GitHub tools available to the agent:

• Read pull request details: diffs, comments, review status, CI checks
• Create and respond to PR comments and reviews
• Read repository metadata: branches, tags, releases, contributors
• Trigger GitHub Actions workflows with parameters
• Read and create issues
• Access commit history and compare branches
• Read CI/CD status checks and workflow run results

Slack tools available to the agent:

• Post messages in designated channels with rich formatting (blocks, attachments)
• Read messages in channels the agent is invited to
• Send direct messages to specific users
• React to messages and create threaded replies
• Read channel membership for routing decisions
• Update and delete the agent’s own messages

The agent acts as an intelligent bridge between code activity and team communication. Here is a representative interaction:

Event: Developer Priya opens a pull request titled “Refactor payment processing to support multi-currency” with 847 lines changed across 12 files.

Agent analysis: The agent reads the PR diff and identifies changes to payment_processor.py, currency_converter.py, and models/transaction.py. It detects that the PR modifies database migration files and touches the payment module, which is tagged as security-critical in the repository’s CODEOWNERS file.

Slack notification in #engineering-payments:

“New PR from Priya: Refactor payment processing to support multi-currency (847 lines, 12 files) Key changes: Payment processor refactored for multi-currency support. Includes new database migration. Touches security-critical payment module. Suggested reviewers: @marcus (CODEOWNERS: payment), @security-team (migration + payment changes) CI status: Running… Review this PR”

Follow-up (30 minutes later): CI completes with 2 failing tests. The agent posts a threaded reply: “CI failed: 2 tests in test_currency_conversion.py – test_eur_to_usd_rounding and test_jpy_zero_decimal. These appear to be existing tests that need updating for the new multi-currency logic, not regressions.”

Conversational command: Marcus types in Slack: “What other PRs are open against the payment module?” The agent queries GitHub and responds with a list of three other open PRs, their ages, and review statuses.

Governance and security

Code repositories and team communications both contain sensitive data. ACP ensures the agent operates within clearly defined boundaries.

GitHub OAuth scopes: The agent uses repo scope for accessing repository data (PRs, issues, code, CI status) and actions scope only if workflow triggering is enabled. It does not have admin:org, delete_repo, or admin:repo_hook scopes. The agent cannot modify repository settings, manage team memberships, or delete branches. If you want the agent to only read without commenting, you can restrict it to repo:status and public_repo scopes.

Slack OAuth scopes: The agent uses chat:write for posting messages, channels:read for understanding channel context, and optionally commands for slash command interactions. It does not have admin scopes and cannot modify workspace settings, manage users, or access private channels unless explicitly invited. Messages the agent reads are limited to channels it has been added to.

Code access boundaries: The agent can be configured to access only specific repositories rather than all repos in an organization. For companies with separate repositories for infrastructure, secrets management, or internal tools, you can exclude those entirely from the agent’s scope. The agent never has access to GitHub Secrets or environment variables stored in Actions.

Audit trail: Every GitHub API call (PR read, comment posted, workflow triggered) and every Slack message sent is logged with timestamp and context. If the agent triggers a deployment workflow, the audit log records who initiated the command in Slack, which workflow was triggered, and the result. This provides full accountability for chat-initiated operations.

Example use cases

• Intelligent PR notifications: Instead of broadcasting every PR to the whole team, the agent routes notifications to the relevant channel based on which files changed. Payment PRs go to #engineering-payments, frontend PRs go to #frontend, and infrastructure PRs go to #devops. Each notification includes a diff summary and suggested reviewers.

• Review reminders with context: PRs that have been open without review for more than 24 hours trigger a reminder in the appropriate channel. The agent includes why the review matters: “This PR blocks the v3.1 release and has been waiting for review for 2 days.”

• Deployment tracking: When a release is tagged or a deployment workflow runs, the agent posts a status update in #deployments with the version number, changelog summary (generated from commit messages since the last release), and the person who triggered it. If post-deployment health checks fail, it escalates immediately.

• Chat-based repository queries: Engineers ask questions in Slack and get answers without leaving chat: “What commits went into today’s release?” or “Show me all open issues labeled bug with priority high.” The agent queries GitHub and formats the response in Slack.

• On-call handoff context: At the start of each on-call rotation, the agent posts a summary of recent deployments, open high-priority issues, and any PRs that touched critical systems in the past week. This gives the incoming on-call engineer immediate situational awareness.

Getting started

Set up your GitHub-Slack agent in minutes:

1. Sign up at cloud.agenticcontrolplane.com. The free tier supports the integrations you need.

2. Connect GitHub and Slack. On the Data Sources page, authorize both platforms. For GitHub, select which repositories the agent can access. For Slack, choose which workspace and channels to enable.

3. Describe your agent. Tell ACP: “Monitor PRs in our main repositories. Post intelligent notifications in the relevant team channel based on which code areas changed. Include diff summaries and suggest reviewers from CODEOWNERS. Allow team members to query PR and issue status from Slack.” Your engineering command center is live.