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.

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github

valkey-io/valkey

Pick an open issue and Operate will investigate its root cause.

530 open issues

#4209 [TEST-FAILURE] diskless timeout replicas drop during rdb pipe in tests/integration/replication.tcl
test-failure
#4208 [TEST-FAILURE] HRANDFIELD - CASE 4: The number of elements inside the hash is greater than 3 times the number of requested elements in tests/unit/hashexpire.tcl
test-failure
#4207 [SECURITY] `RDB_OPCODE_SLOT_IMPORT` short job_name causes server-side `heap out-of-bounds read` during startup RDB loading
bug
#4203 [BUG] RDB load asymmetrically applies encoding conversions
bug
#4202 hashtable: consolidate common helper callbacks
#4200 [SECURITY] Module timer self-stop causes double free in `moduleTimerHandler()`
bug
#4198 [SECURITY] Server-side `heap-use-after-free` in ready-key blocked-client iteration (`BLOCKED_MODULE` / `CLIENT KILL` reentrancy)
bug
#4189 [NEW]Proposal / discussion: multi-dimensional hot-key & big-key detection (CPU / network / size / memory) on top of #3708
enhancement
#4187 Add valkey-arduino to the client list: community client for Arduino-ecosystem boards
enhancement
#4178 ACL evaluation in multi/exec at EXEC time
#4174 Maintainability: get_dbid_args functions
#4173 Forkless: Consider if we should have configuration for the cloning pool
#4153 [TEST-FAILURE] ccov CI job fails and discards coverage on any flaky test failure (14% of unstable pushes)
test-failure
#4151 [BUG] Client eviction can disconnect more clients than necessary when a client's close is deferred (async free)
bug
#4150 [BUG] "must be positive" error for count arguments that accept 0 (shared getPositiveLongFromObjectOrReply)
#4149 [TEST-FAILURE] COPY Preserves TTLs in tests/unit/hashexpire.tcl
test-failure
#4144 [TEST-FAILURE] Connecting as a replica in tests/integration/valkey-cli.tcl
test-failure
#4143 [BUG] Tracking table items not cleaned after client disconnect
bug
#4139 [TEST-FAILURE] Test dual-channel: primary tracking replica backlog refcount - start with backlog in tests/integration/dual-channel-replication.tcl
test-failure
#4138 [TEST-FAILURE] New replica inherits importing slot in tests/unit/cluster/slot-migration.tcl
test-failure
#4137 [TEST-FAILURE] New replica inherits migrating slot in tests/unit/cluster/slot-migration.tcl
test-failure
#4136 [TEST-FAILURE] Replica of importing node returns TRYAGAIN after READONLY and ASKING in tests/unit/cluster/slot-migration.tcl
test-failure
#4135 [TEST-FAILURE] Replica of migrating node returns TRYAGAIN after READONLY in tests/unit/cluster/slot-migration.tcl
test-failure
#4134 [TEST-FAILURE] Replica of migrating node returns ASK redirect after READONLY in tests/unit/cluster/slot-migration.tcl
test-failure
#4133 [TEST-FAILURE] Replica redirects key access in migrating slots in tests/unit/cluster/slot-migration.tcl
test-failure
#4132 [TEST-FAILURE] Migration source is auto-updated after failover in source shard in tests/unit/cluster/slot-migration.tcl
test-failure
#4131 [TEST-FAILURE] Migration target is auto-updated after failover in target shard in tests/unit/cluster/slot-migration.tcl
test-failure
#4130 [TEST-FAILURE] Node #10 should eventually replicate node #5 in tests/unit/cluster/slave-selection.tcl
test-failure
#4117 Perf: 20% main thread time spent on per-command overhead in `call()`
#4114 [PERF] IOThreadMain per-iteration overhead causes io-threads>4 regression