Operate is a self-hosted AI SRE: an AI agent platform that detects, investigates, and resolves production issues. It runs a four-agent pipeline (Context, Root Cause, Verification, and Resolution) that connects to your logs, database, and codebase, then produces an evidence-backed root cause and a draft pull request for human review.

An AI SRE is an AI system that does the work of a site reliability engineer: watching production for errors and regressions, investigating alerts, finding root causes, and proposing fixes. Operate is a self-hosted AI SRE, so it runs inside your own infrastructure with read-only access and a mandatory human approval gate.

How does Operate find the root cause of a production issue?

When triggered from chat (Slack, MS Teams, and other tools) or by an automated alert, Operate pulls traces from log providers like Datadog, CloudWatch, GCP Cloud Logging, and SolarWinds, queries your MongoDB, PostgreSQL, or MySQL database in read-only mode, and reads your codebase via git including blame and diff. A Context Agent gathers signals, a Root Cause Agent proposes a cause with a confidence score, a Verification Agent independently checks the reasoning with a different model, and a Resolution Agent drafts a fix as a pull request.

Does Operate ship code automatically without a human?

No. Operate stops at a draft pull request. A human engineer must review and merge; nothing ships without approval. Every agent step is auditable so reviewers can see the evidence and reasoning behind each finding.

How is Operate deployed?

Operate is deployed as a Docker image into your existing infrastructure with no new cloud accounts. It runs on port 8080 under the /operate path, requires MongoDB and Redis, and uses an App ID from operate.bettrsw.com. It connects via read-only adapters to logs, databases, and observability stacks, and clones your repo at startup via an init container so agents have full git history.

Which integrations does Operate support?

Operate's context layer spans 60+ integrations across 14 categories: source control (GitHub, GitLab), logs (Datadog, CloudWatch, GCP Cloud Logging, SolarWinds), databases (MongoDB, PostgreSQL, MySQL, read-only), error tracking (Sentry, Rollbar), APM (New Relic, Grafana, OpenTelemetry), CI/CD (GitHub Actions, Jenkins, Vercel), feature flags (LaunchDarkly, Statsig), cloud infrastructure (AWS, GCP, Azure, Kubernetes), queues (Kafka, SQS, RabbitMQ), incident management (PagerDuty, incident.io), issue tracking (Jira, Linear, Asana), support (Zendesk, Intercom), status pages, and docs (Confluence, Notion). Triggers: Slack, MS Teams, and other chat tools for on-demand RCA, plus the observability stack for proactive monitoring. AI providers: Anthropic Claude, OpenAI/Azure Codex, enterprise models, or self-hosted open-source LLMs.

How does Operate avoid hallucinated root causes?

Operate uses a two-model verification step. The Root Cause Agent proposes a cause with specific evidence; the Verification Agent, running on a different model to avoid correlated errors, independently evaluates the reasoning. Speculative or unsupported conclusions are rejected before reaching the team.

Why not just point an LLM at our logs ourselves?

An LLM has no inherent knowledge of your logs, schemas, services, or known-bad patterns. Operate provides the connectors, permissions, query layer, repo context, multi-agent verification, and operational runtime that turn a generic model into a reliable production-investigation system.

Engineering teams that lose 30–40% of capacity to unplanned investigation: support escalations, error alerts, performance regressions. With Operate, support and product teams can trigger an RCA from Slack, MS Teams, or whatever chat tool they use, and only loop in engineers when a decision needs to be made.

We love open source

Root cause analysis for public repositories

Operate reads the code of open-source projects and traces the root cause of their open issues. Free, no sign-up. Explore what it is finding below, or point it at a repo you care about.

Investigating 40 open-source projects

whiskeysockets/Baileys spring-projects/spring-boot square/okhttp keploy/keploy ankur-bag/framer-bot element-hq/element-weband 34 more

Recent investigations

whiskeysockets/Baileys #2707 [BUG] Critical Issue - Error 463 (Reachout Timelock) Causing Account Bans36m ago
element-hq/element-web #34051 Flaky playwright test: `left-panel/room-list-panel/room-list-unread-toast.spec.ts: shows a toast for a collapsed section that hides a notifying room`2h ago
element-hq/element-web #34340 Cannot invite due to missing room keys(?)2h ago
keploy/keploy #4377 [bug]: Next test-set ID incorrectly uses numeric suffix from custom metadata names2h ago
whiskeysockets/Baileys #2699 [BUG] Private 1:1 messages not delivered for restored previously banned WhatsApp account, while groups work2d ago
whiskeysockets/Baileys #2714 Media retry (updateMediaMessage) fails GCM decrypt for media in LID-migrated chats — phone answers with a re-keyed payload3d ago
spring-projects/spring-boot #50935 MongoDatabaseFactory initially used 'test' instead of database specified in spring.data.mongodb.uri4d ago
spring-projects/spring-boot #50946 Upgrade Gradle versions used by CI on Java 265d ago