One Engineer connects to Kubernetes, Splunk, Datadog, GitHub, GitLab, Linux/SSH VMs, Slack, Jira, and private systems to find root cause, propose a fix, and prepare rollout-safe remediation actions.
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Link code, the release-ready AWS cloud estate, clusters, logs, tickets, security, on-call, and private systems. AWS discovery is agentless; private credentials can stay on your Private Bridge.
Ask for an RCA. It correlates deploys, pod events, logs, metrics, incidents, tickets, and code history.
It prepares the fix, MR, rollback, restart, scale action, or ticket update. Your policy controls what runs autonomously.
Not autocomplete in your editor. An autonomous operator that works across the tools you already use.
GitHub, GitLab, merge requests, CI traces, and release context.
Kubernetes plus Linux servers and VMs over SSH for events, logs, restarts, and scale actions.
Splunk, Datadog, and Prometheus signals, turned into an evidence-backed RCA.
Prisma Cloud / Twistlock findings for images, hosts, runtime incidents, and compliance.
Slack, Jira, PagerDuty, ServiceNow, Confluence, and runbook context in one investigation.
MCP and webhooks for custom APIs, optional Private Bridges, and internal operations workflows.
Optional Private Bridges keep credentials inside your network. No direct inbound path from One Engineer to private clusters, hosts, or service endpoints is required.
It runs with autonomous, governed, and auditable actions where policy allows. Protected systems can require approval, every action is tenant-scoped, and private-endpoint secrets stay on-prem or in your Private Bridge host.
Read-only connectors and approval-gated production changes. Every write is auditable, tenant-scoped, and policy-bounded. Secrets are encrypted at rest or kept on optional Private Bridges, and are never used to train models. SSO on Team and up.
Start free. Upgrade when the agent is pulling its weight.
Try autonomous RCA on your own repos and systems.
For an engineer who wants production answers faster.
Shared connectors, SSO, and team-controlled approvals.
Run One Engineer in your cloud, datacenter, or dedicated environment.
No. Your code, logs, and connection secrets are never used to train models. Secrets are encrypted at rest.
Yes. One Engineer can run autonomous investigations and remediation actions where your organization policy allows it. Protected production systems can still require approval, and every action is audited.
For private or air-gapped endpoints only, install a lightweight Private Bridge inside your network. It polls One Engineer outbound over mTLS, keeps credentials local, rotates certificates, and returns redacted results. Standard AWS discovery does not require it; Azure and GCP onboarding are not available in this release.
Yes. Enterprise customers can run One Engineer in a dedicated cloud, datacenter, or isolated environment, with supported open-model or in-house model endpoints and dedicated support.
Credits measure the work the agent does — a quick answer uses a little, a big multi-step task uses more. Each plan includes a monthly credit allowance, and every conversation shows the credits it used.
Self-serve from the in-app billing portal at any time — no emails, no friction.