Aient
Aient is an AI-powered production reliability platform that detects runtime issues, traces root causes to specific code, and automatically opens pull requests with tested fixes. It continuously monitors OpenTelemetry telemetry, groups related errors into impact-scored problems, and verifies fixes in production after deployment by tracking release commit SHAs. The platform integrates with GitHub, Linear, Slack, and Microsoft Teams to keep investigations connected across the tools engineering teams already use.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Production incidents are detected through disconnected alerting, logging, and tracing tools, leaving engineers to manually piece together what happened from fragmented context. This reconstruction work—identifying the affected service, correlating telemetry, and locating the relevant code—consumes time and delays fixes, especially for silent failures that health checks cannot detect.
Solution
Aient connects code, telemetry, and investigation history in one platform that finds root causes of production issues and proposes verified fixes. It groups error signals into impact-scored problems, reads traces, logs, and metrics alongside the codebase to identify likely causes, and automatically files tickets with that context attached. The platform opens pull requests containing the root-cause explanation and tests that reproduce the bug before the fix is applied. After deployment, Aient tracks releases by full commit SHA and monitors production for recurrence, closing problems as resolved only when independent verification confirms the fix holds.
Target Audience
Primary users are engineering teams and engineering leads at software companies who need to reduce time spent on incident investigation and ensure fixes are verified in production, particularly those with multi-service architectures where cross-service failures are hard to trace.
Features
- Automatic problem grouping by impact score with triage states from new through verifying to resolved
- AI-driven investigation that correlates traces, logs, metrics, and source code to identify likely root causes
- Three-tier model cascade for triage: a fast model handles initial classification, a more capable model handles complex cases with full code context, and a critic model reviews critical or low-confidence results
- Automated pull request generation with tests that reproduce the bug before the fix, using the project's own test suite
- Release verification by commit SHA that checks for problem recurrence after deployment and reopens investigations if evidence returns
- Persistent AI chat per problem where engineers can ask follow-up questions and override triage feedback to improve future prioritization
- Integrations with GitHub, Linear, Slack, Microsoft Teams, Vercel, and OpenTelemetry, plus an MCP server and CLI for querying from coding agents and terminals