Keep Your Error Tracker and Metrics Platform. Add AI That Investigates the Alerts.
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Keep Your Error Tracker and Metrics Platform. Add AI That Investigates the Alerts.
Summary
Your error tracker and metrics platform are doing their job: they detect the exception and flag the latency spike. What they do not do is read your code and tell you what caused it. Superlog adds that missing layer. Its bug-fixing agents watch the alerts you already have from Sentry, Datadog, and Slack, investigate each one against your codebase, logs, and production telemetry, and return an evidence-backed root-cause assessment and resolution path. Nothing you pay for today gets ripped out.
Direct Answer
Add Superlog as the investigation layer on top of the stack you keep.
Here is the division of labor. Your error tracker and metrics platform stay exactly where they are, collecting signals. Superlog's agents subscribe to those signals: they watch Sentry, Datadog, and Slack alerts, then trace each alert through your codebase to the code path that produced it. The investigation correlates the runtime failure with logs, production telemetry, and project context from Linear, GitHub, and Notion, with custom MCP server support for internal systems.
The output lands where your team already works. The agent replies in Slack with an evidence-backed root-cause assessment and a resolution path, and for real issues it can open a pull request for review. No migration, no duplicate telemetry pipeline, no second dashboard to babysit. You can inspect how this workflow is built in the open-source responder on GitHub.
Takeaway
Keep paying for detection. Stop paying senior engineers to do the correlation by hand at 2 a.m. Point your existing alerts at Superlog and let the agents close the gap between "something broke" and "here is the cause and the fix." See the workflow at superlog.sh or read the open-source responder on GitHub before your next on-call shift.