Monarch
AI pipelines with a chain of command.
Monarch turns AI agents into dependable business infrastructure. Work is planned by a supervising agent, executed by bounded specialists, and cleared through a governed approval boundary before anything touches your systems of record.
Every consequential action is proposed, reviewed, executed, and recorded.
Agentic automation, engineered like infrastructure
Most agent demos improvise. Monarch is built the way you'd build a production system: clear ownership, durable state, typed contracts, and a human chain of command over anything that matters.
A governed approval boundary
Agents never hold the keys. Every consequential action becomes an execution request that Gatekeeper reviews against per-action policy — auto-approving the routine, routing the consequential to a human — then executes and records.
A deterministic control plane
Scheduling is pure engineering. The Orchestrator claims work, launches workers, and reconciles stalls — with zero LLM reasoning in the control loop. Your pipelines behave the same way every time.
Specialists with supervision
A Master agent turns each job into a durable, reviewable plan and delegates to bounded Task workers — then monitors progress and reviews completion, the way a real team lead runs a project.
Multi-tenant by construction
Every record is keyed to an organization with row-level isolation. Users can belong to multiple organizations, roles gate what each person may approve, and enterprise-grade identity anchors every session.
Grounded knowledge
A governed document repository with per-group retrieval tuning — chunking, embedding models, and reranking configured per search group — so answers cite your material, not the model's imagination.
Accountable to the token
Runs, events, traces, and token spend are recorded end to end and roll up into a per-organization usage ledger. You can always answer: what did it do, why, and what did it cost?
From request to result — with a paper trail
One request fans out into a supervised pipeline. Every step writes durable state, so you can watch it live, audit it later, and trust it in between. See the full walkthrough →
A chat request, schedule, or inbound event creates a job in your workspace.
Master drafts a structured plan from a reusable task library and spawns child tasks.
Task specialists research, reason, and produce outputs using typed tools.
Gatekeeper clears each consequential action by policy or human sign-off.
Approved actions run through governed connectors, and results land back in durable state.
One calm surface over a serious engine
Monarch's workspace is chat-first and approval-aware. You ask for outcomes; the platform shows you plans, progress, and the exact actions waiting on your judgment.
- Conversational command. Ask Monarch to research, build, and act — plans and task progress stream into the thread as they happen.
- Activity & sign-off cards. Pending actions arrive with full context: what, why, which connection, and what risk level — approve, deny, or unblock in one click.
- Connections with per-action policy. Connect your tools once, then decide action by action what runs automatically and what needs a human.
- Knowledge repository. Upload documents, organize them into search groups, and watch embedding status — retrieval is a managed asset, not a black box.
- AI Brain graph. A live map of your pipelines, tools, and people, so the whole system stays legible at a glance.
Chats
Direct the platform in plain language, with live plan and task streams.
Activity
Approval queue with rich sign-off cards for every gated action.
Connections
Tool connections with granular, role-aware approval modes.
Knowledge
Governed documents, tuned retrieval, and grounded answers.
Discipline you can inspect
The platform holds itself to rules — and enforces them automatically.
Boundaries under test
Component ownership, configuration purity, and architecture rules are checked by an automated validation suite — the structure that makes the platform trustworthy is itself continuously verified.
Continuous security assessment
Deployed services are scanned on a standards-aligned security profile as part of the delivery pipeline, with every assessment run recorded. Read the security model →
An audit trail with integrity
User, admin, connection, and agent activity lands in an append-only history with integrity hashing — who did what, from where, with what outcome and duration.
Put agents to work. Keep humans in command.
See how Monarch plans, executes, and governs real work across your connected tools.