Integrate, manage and secure your whole AI estate

Automatically discover and catalog every agent, session, MCP server, skill and model running across your infrastructure — multi-host and multi-cloud, with no proxy to babysit.

Inspect observed session actions, models, tokens, costs, and event history, with governed work items and handoffs. Goals depend on the reporting source; computed task progress and the complete operating workflow are not yet verified.

Track the health, uptime and SLAs of your agents and MCP servers, with alerts on outages or degradation and a live dependency map.

Granular control over who and what can act — role- and attribute-based access, per-agent identity, human-in-the-loop approvals and a policy engine — with every action traceable to a person.

View observed agent relationships and use bounded workflows for work, runtime, messages, and handoffs. Coverage gaps remain explicit; the combined coordination and acknowledgment workflow is not yet verified end to end.

Curate and reuse approved agents, MCPs and skills as versioned company templates, with governed self-service.

Tamper-evident audit, secrets and egress controls, and guardrails against prompt injection, jailbreaks and data leaks — with reconstructable incident forensics.

Probe your agents safely — adversarial prompt-injection, jailbreak and exfiltration tests, scored against the OWASP agentic-risk checklist.

Run operator-authored mock scenarios and ordered replay when a history source is available. Operating-system isolation requires a configured isolation backend and separate attestation; mock execution does not provide it.

Resolve routing policies and ordered fallback candidates. The measured execution path requires a configured Claude Messages-compatible client; this does not verify every provider, local model, or console execution path.

Track AI spend by team, agent, model and project, set budgets and alerts, and forecast — with routing and caching policy to manage spend.

Answer the question that matters — is my agent still doing the right thing? — with evals, regression testing, output-quality monitoring and drift detection.

Govern what your agents know and retrieve — knowledge bases and RAG, governed retrieval, data lineage and residency, versioned prompts and persistent memory.

Map your AI program to the frameworks enterprises ask about — the EU AI Act, NIST AI RMF, ISO 42001, SOC 2 / ISO 27001 and GDPR — with exportable audit evidence and agent risk classification. It helps you demonstrate controls; it does not grant a certification.

Govern your agents’ tools — a catalog of MCP servers, skills and plugins, what is connected to whom, configuration, versioning and health.

Govern the agent and MCP lifecycle — provision, update and retire — and wire them into your real network, servers and data stores, with versioning, rollback and GitOps. Direct actuation is governed and deny-closed: live for a subset today, provisioned on-demand for the rest.

Olivares talks to the tools you already run — chat, on-call, your SIEM, ITSM systems, developer portals, webhooks and email.

Manage Olivares itself as code — a full API, a Terraform provider, a declarative CLI and event webhooks.

Organization hierarchy and delegated administration for service providers and large enterprises, with real tenant isolation and per-org usage.

High-level views for leadership — KPIs across cost, usage, risk and compliance, with scheduled, exportable reports.

Manage conversational and realtime voice agents — sessions, state, transcription, latency and governance.