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.

← All repos

github

json-schema-org/JSON-Schema-Test-Suite

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

33 open issues

#1022 jsonschema_suite flatten crashes with AttributeError: 'str' object has no attribute 'rglob'
#1013 tests regarding RFC 3987 incomplete
#1009 jsonschema_suite check fails with UnicodeDecodeError on Windows (non-UTF-8 default locale)
#948 email / idn-email: scope questions - 4.5.3.1 length limits and control / noncharacter codepoints
#941 Out-of-order schemas
#898 Regular expression pattern specification and testing clarifications
#894 Root level $ref and $id in Draft 7
#885 Possibly inconsistent `$schema` for optional `format` tests ?
#863 [Coverage] output-tests/ is missing structure/ subdirectory documented in README
#838 Add test to ensure dotted-decimal IPv4 addresses are rejected by format: hostname
#834 Testing interaction of default with required
#829 Add Unicode-focused tests for string length and pattern handling
#805 Managing tests through the feature life cycle
#797 Cover URI schemes in $id besides URL and URN
#699 Add a `specification` property to test cases + tests
enhancement
good first issue
#698 Add an `id` property to all test cases
enhancement
good first issue
#657 Revamped output testing
missing test
enhancement
#653 Tests for bundling
missing test
enhancement
#589 A suite of vexatious JSON Schemas
missing test
enhancement
#571 Add a post- spec release checklist
documentation
enhancement
#554 Document the metaschema URI used for draft-next tests for authors of new tests and implementers wanting to run them
documentation
enhancement
#549 Document how to use Dependabot to keep a test suite submodule up to date
enhancement
#536 multipleOf contains incorrectly optional (bignum dependent) tests
bug
#495 Tests for format should make better use of vocabularies and optional/ is too overloaded with meaning
needs decision
#444 test needed to show that transclusion of $ref into allOf changes the evaluation outcome
missing test
#439 Document the test inclusion guidance/criteria
documentation
enhancement
#423 Test suite CI should ensure tests' schema IDs are unique
enhancement
#378 beef up bignum tests
missing test
#321 Add test for multipleOf to ensure that implementation does not have an issue regarding scale
#300 Test for valid use of empty fragment in "$id"
missing test