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General

2501 runs autonomous AI agents on your infrastructure. You point it at machines, give it a few rules about how your environment works, and it handles tickets from your ITSM - investigating, fixing, and reporting back in the ticket itself.
Yes. ServiceNow is the default gateway and what most deployments run, but custom-built gateways are available for other ticketing systems - contact your account team. You can also drive 2501 from the Command Center UI directly.
Yes - a built-in free tier caps your tenant at 50 lifetime tasks and 10 hosts (gateways are unlimited). Once you apply a license, those caps are replaced by your contract’s. See Licensing.

Agents and safety

Three layers, used together:
  1. Permissions - give agents the least-privileged credentials that still let them work.
  2. Blacklists - programmatic blocks for specific command patterns, always on.
  3. Read-Only Agents - set a specialty to investigate-only for read-only operation by default. An explicit @2501:remediate ticket tag overrides that setting for the job.
Investigate is read-only - the agent diagnoses and reports without changing anything. Remediate lets the agent act. Without a tag, a ticket’s tasks remediate unless their specialty is investigate-only. Set per ticket with @2501:investigate or @2501:remediate, or set a whole specialty to investigate-only. See Read-Only Agents.
A Specialty shapes how the agent thinks about a domain - vocabulary, priorities, approach. An Operational Rule tells it what is true in your environment and what that implies. Specialty: “Kubernetes Operator”. Rule: “On this cluster, rollbacks go through helm rollback, never re-applying manifests.”Rules outrank specialties in conflicts. See Prompting an Operational Rule.
The results of every task go into the planning of the next turn and into the final answer, so a job’s prompts grow with each task. 20 tasks keep them within the model’s context, and a patch on 20 hosts still fits in one turn. If a job reaches the limit while the ticket still needs work on more hosts, the job ends as partial. See Agentic Flow.

Models and providers

Native: OpenAI, Anthropic, Mistral, DeepSeek, Cohere, OpenRouter, TogetherAI. Compatible: any OpenAI- or Anthropic-shaped API via openai-compatible / anthropic-compatible, and Azure OpenAI (including APIM). Self-hosted endpoints (vLLM, Ollama) work through the compatible types. See Providers.
Yes. Each agent picks its main and secondary engine from the tenant catalog. The tenant has its own defaults for gateway routing and the AI Assistant. See Engine & Agents.
The main engine does the work (commands, file edits). The secondary engine plans, watches, and judges every command for compliance and read-only enforcement. Separating execution from oversight improves both accuracy and safety. See Engine & Agents.

Deployment

Either. The same signed chart installs on Kubernetes (any distribution: vanilla, OpenShift, k3s, EKS, etc.), applied with Helm, ArgoCD or kubectl, and on Docker Swarm, rendered into one compose.yaml for docker stack deploy. See Overview.
Apply the chart of the newer build with the same values.yaml, after reading the Upgrade actions of every release you cross and reviewing the diff. See Upgrade.
The installation’s own keys, its database connection and the first LLM API key are secrets you create in your cluster or Swarm; values.yaml only names them. Further LLM providers and their keys are configured in Command Center. Per-host credentials (SSH, WinRM, gMSA, vault paths, PAM accounts) live in Command Center → Credentials, encrypted at rest. Agents only access them programmatically - values are never displayed in the UI after creation. See Credentials.

Knowledge and rules

PDF, DOCX, CSV, and Markdown. Max 200 MB per file. PDFs with diagrams need a multimodal model on the tenant to capture the visual content. See Knowledge.
Manual rules always win. The auto-extracted rule is replaced when you re-upload the same document, but your hand-written rules are untouched. See Working with Knowledge.
Each task’s detail page in Command Center shows a matching trace - which operational rules were applied, which were skipped, and why each skip happened. Use it to debug rules that are too broad or too narrow.