AI Incident Triage Assistant

What does this AI Agent workflow deliver?

Rapid triage of incoming bug reports or incidents by combining customer communications with internal knowledge to create actionable tickets.

• Automatic creation of GitHub issues with comprehensive details: the agent extracts user reports from Intercom and formats clear reproduction steps, error details, and user metadata into the issue description.
• Cross-referencing of known issues: the agent searches the Notion knowledge base (and past GitHub issues) to see if the problem has occurred before, and links any relevant troubleshooting notes or fixes.
• Instant developer alerts via Slack for high-priority incidents, including an AI-generated summary of the issue and a suggested severity level or potential root cause.

Outcome:

• Faster response times to critical bugs – developers get well-documented tickets almost immediately after a user reports an issue, reducing back-and-forth between support and engineering.
• Improved accuracy in bug reports as the AI ensures key information is captured and standardized, leading to quicker diagnosis and resolution.
• Less manual work for support teams (no need to rewrite customer issues into tickets) and for engineers (no need to chase details), minimizing human error and effort.
• Enhanced team collaboration: an AI agent serves as the always-available bridge between customer support and dev teams, ensuring urgent issues are never delayed—even outside of normal working hours.

Why does it matter?

Pain Point (Engineering & Support)	AI Agent Solution (Incident Triage Assistant)
Support tickets often lack the detail engineers need.	The agent uses AI to read the Intercom conversation and pull out critical details like error messages, user actions, and environment info. It then creates a standardized GitHub issue with all this info neatly organized.
Engineers waste time triaging duplicate or known issues.	By cross-referencing Notion docs and past issues, the agent flags if an incident is similar to a known problem and links relevant fixes or workarounds, saving diagnostic time.
Delayed response to critical bugs reported off-hours.	The AI agent works around the clock, instantly alerting on-call engineers via Slack for any severe issue. The Slack alert provides a concise summary and the GitHub issue link so the team can jump in quickly.
Communication gaps between support and dev teams.	The agent ensures nothing is lost in translation: every user report is translated into a technical bug report for engineers. It can even draft an update in Intercom (for support to review) so the user knows the issue is being addressed, keeping all sides in the loop.

Step-by-step setup:

1. Connect support and dev tools: Integrate Intercom (or your support ticket/chat system), GitHub, Slack, and Notion in Unitron AI. This ensures the agent can access customer conversations, the knowledge base documentation, and the issue tracker.
2. Set trigger for new incidents: Configure the agent to activate when a conversation is marked as a bug/issue in Intercom or when a support rep uses a specific tag/keyword. You can also have it listen for certain keywords from customers (like “bug,” “error,” “not working”) to catch issues automatically.
3. Information extraction with AI: Once triggered, the agent gathers all relevant info. It uses AI to parse the user’s Intercom messages, extracting key details: steps the user took, what went wrong, any error codes or screenshots. It may also pull basic user data (e.g. account tier, app version) from Intercom or the CRM to include helpful context.
4. Check for known issues: The agent searches your Notion knowledge base and recent GitHub issues for similar reports. If it finds a match (e.g. a known bug or FAQ entry), it notes the reference (like “Similar to ISSUE-123 or Known Issue #5”) to include in the ticket or Slack alert.
5. Create a GitHub issue: The agent automatically creates a new issue in the appropriate GitHub repository. It populates the title and description with a clear summary, detailed reproduction steps, expected vs. actual results, and any attachments (logs, images). It also includes any links to related issues or knowledge base articles from the previous step. Labels (like “bug” or severity level) and an assignee can be added based on rules (e.g. critical bugs tagged to a senior engineer).
6. Alert the engineering team: The moment the GitHub issue is created, the agent posts a message in your Slack (for example, in a #incidents channel). The alert contains a concise summary of the issue, the suspected severity or impact, and a link to the GitHub issue. If the AI detects that the issue is critical (multiple users affected or a major feature broken), it can @mention the on-call engineer or tech lead to ensure immediate attention.
7. Feedback loop and resolution: As engineers work on the issue, the agent can update the Intercom conversation with status updates. For instance, it might draft a message like “Our engineering team is investigating your issue (reference ticket #123). We’ll keep you posted,” for the support rep to review and send. When the issue is resolved and the GitHub ticket is closed, the agent can prompt a final update to the user. Over time, every resolved issue can be added to the Notion knowledge base, and the AI learns from this growing repository – becoming even more efficient at recognizing and triaging future incidents.