AI Support Engineering
Not every production issue comes from an alert. Many come from customers — "Why is my API returning 500s?", "Our dashboard hasn't updated in 3 hours", "We're seeing timeouts on the export endpoint." Autoheal investigates these automatically.
How It Works
A customer reports an issue through your support platform (Pylon, Jira, Slack, or the Autoheal UI).
Autoheal immediately begins investigating without waiting for an engineer to triage. The agent queries your observability stack, checks for related alerts, and reviews recent changes.
The agent posts its findings back to the support conversation — metrics, logs, root cause hypothesis, and suggested resolution. Your support team has answers without escalating.
Support engineers can ask the agent follow-up questions to get more detail or investigate adjacent issues.
What Gets Investigated Automatically
Autoheal can investigate any customer-reported issue that involves your production systems:
500s, timeouts, rate limiting, authentication failures — the agent checks service health, error logs, and recent deployments.
Slow responses, degraded throughput, latency spikes — the agent pulls metrics, traces, and resource utilization data.
Missing data, stale data, sync failures — the agent checks database health, pipeline status, and job history.
Features not working as expected — the agent checks recent deployments, feature flags, and configuration changes.
Integration with Support Platforms
| Platform | Capability |
|---|---|
| Pylon | Automatic investigation of new tickets, findings posted as internal notes |
| Jira | Webhook-triggered investigations on issue creation, results added as comments |
| Slack | @Autoheal in support channels to investigate customer issues conversationally |
Benefits for Support Teams
Faster Resolution
Instead of the typical escalation cycle — support triages → assigns to engineering → engineer investigates → responds — Autoheal investigates immediately. Many issues are resolved before engineering even sees them.
Consistent Quality
Every ticket gets the same thorough investigation regardless of who's on shift. The agent checks the same sources, follows the same diagnostic patterns, and references the same skills.
Reduced Escalations
When the agent provides detailed investigation findings (metrics, logs, probable cause), your support team can often resolve issues directly or provide customers with specific, technical answers without waiting for engineering.
Knowledge Capture
Investigation findings for support tickets feed back into the Production Context Graph just like incident investigations. Recurring customer issues inform future investigations and can trigger proactive fixes.
Getting Started
- Connect your support platform (Pylon, Jira, or use Slack)
- Add relevant integrations for the services your customers use
- Build Production Context Graph entries for common customer-reported issues
- Test with a few real tickets to calibrate the agent's investigation depth