Aient
@Aient
MCP-native AI SRE. Aient exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools: list_problems, get_problem, query_traces, detect_anomalies, request_problem_remediation. Remote streamable-HTTP at https://aient.ai/mcp; auth via OAuth (scopes aient.mcp.read / aient.mcp.write). Human-in-the-loop by default. Request access at aient.ai.
Overview
Aient
AI DevOps
Now accepting teamsDetect problems.
Detect problems.
Fix them automatically.
Aient connects your runtime telemetry to your codebase and dev tools. When something breaks, it finds the cause, drafts a fix, and opens a PR — before your team even notices.
Integrations
Connects to the tools your team already uses
GitHub
Slack
Kubernetes
GCP
Vercel
Linear
OpenTelemetry
How it works
From signal to fix in three steps
Aient is not a monitoring platform. It consumes just enough telemetry to detect, group, and resolve issues automatically.
Detect
01Aient ingests just enough telemetry — crash signals, error spikes, bounce patterns — to know something is wrong. No dashboards to stare at, no alert fatigue.
Contextualize
02Each issue is grouped, enriched with code context from your repository, and surfaced as a structured Slack thread — so your team has the full picture instantly.
Remediate
03AI analyzes the root cause, drafts a code fix, and opens a GitHub pull request — ready for review. After you merge, Aient confirms in production that the problem is resolved.
Liveaient — live remediation pipeline
01DETECTTelemetry ingested
errorTypeError: Cannot read property 'map' of undefined
stackat UserList (components/UserList.tsx:42)
count147 occurrences in 12 min
02ANALYZEIssue contextualized
repoacme/web-app
filecomponents/UserList.tsx
causeAPI returns null when users array is empty
03REMEDIATEFix proposed
pr#482 — Add null guard to UserList
diff+ const users = data?.users ?? []
statusReady for review
Features
Everything you need to close the loop
An opinionated, end-to-end pipeline from runtime signal to merged code fix.
Real-time detection
Crash loops, error spikes, and user-impacting regressions surfaced the moment they happen — not after a morning standup.
AI-generated pull requests
Aient reads your codebase, pinpoints the root cause, and opens a GitHub PR with a proposed fix — then watches production after you merge and reopens it if the error returns.
Slack-native workflow
Issues land in Slack as structured threads. Triage, discuss, and approve fixes without leaving your team's communication hub.
Signal, not noise
No dashboards. No query builders. Aient surfaces what matters and stays silent when everything is fine.
Intelligent issue grouping
Related errors, crashes, and anomalies are clustered into logical issues — reducing noise and giving your team a clear view of what actually broke.
You stay in control
Human-in-the-loop by default — the merge button is yours. Organisation-scoped data isolation, scoped repo access you can revoke anytime, and your telemetry is stored in EU infrastructure — only the remediation step (sandbox + LLMs) leaves it. We never sell your data or share it with model providers for their training.
Early access
Request access
We're onboarding engineering teams now. Request access and we'll get you set up with AI-driven remediation for your production issues.
No commitment required. We'll reach out within 48 hours.
Server Config
{
"mcpServers": {
"aient": {
"url": "https://aient.ai/mcp"
}
}
}