The context layer for enterprise AI
Team up with AI agents.
Stay in charge.
Process Metronome makes AI agents ready for your business. Grounded in a governed graph of your facts, constraints, and processes, they plan against reality, decide within bounds, and act autonomously — reporting back like regular teammates.
Product walkthrough — coming soon
See how agents run inside your operational graph
Autonomous within what?
Autonomous agents are real. They can act, dispatch, and execute without you prompting them. That is not the question anymore.
Today’s agent platforms give you two bad options. Full autonomy — the agent decides, acts, and bills you for it, with no structural boundary between what it should do and what it can do. Or human-in-the-loop — you review every step, approve every action, and wonder why you needed AI at all.
The root cause is the same in both cases: the agent has no model of your system. It does not know your constraints, your dependencies, your current load, or who owns what decision. So either you supervise it constantly, or you let it run and lose the thread entirely.
Ownership and autonomy should not be a trade-off. The agent should run. You should stay in control. That only works if the agent operates inside a structure that defines what it knows, what it can propose, and what requires a human decision.
That structure is the context layer. It is what Metronome builds.
You define the structure once. Agents run the loop inside it.
Every agent runs the same loop: plan, decide, act. Metronome provides the harness that keeps each pass through the loop grounded, bounded, and owned.
Agents plan against your reality
Describe what matters once: your projects, your commitments, your team’s capacity. Not in a form wizard — in the natural language of your work. Metronome builds the knowledge graph underneath, and every agent plans against that live structure instead of a stale export or a retrieved approximation.
Screenshot — graph construction
Decisions route to the right owner
Push signals, not prompts: a new brief, a status change, a deadline shift. The agent reads the updated graph, identifies what that signal changes downstream, and proposes the next action. Decisions that matter reach you; you confirm or dismiss. Everything else is bounded delegation the structure has already made safe.
Screenshot — signal & proposal
Actions are dispatched, audited, yours
The agent acts one step at a time, inside the envelope the graph defines, and the rest happens silently: monitoring, recalculating, flagging, waiting. Without a chat interface asking for your attention. Every action — human or AI — lands in the same audit trail, and as your context grows the agent’s boundaries grow with it.
Screenshot — monitoring & audit
One person, one graph.
Conflicts surfaced before they cascade. Reprioritisation proposed with the full context of your commitments. Recurring work sustained automatically when something shifts.
A collaborating team.
Cross-project dependencies surfaced automatically. Overcommitment detected at intake, not at deadline. Downstream impact visible before you commit a change. Every team member sees their work in the context of the whole plan.
Same architecture. Different scale. No upgrade required to add people — just seats.
What the AI does
- Conflicts surfaced before they cascade — not after the deadline has passed
- Reprioritisation proposed with the full context of your graph — not a generic suggestion
- Overcommitment detected at intake across the team — not discovered at deadline
- Cross-project dependencies surfaced and monitored automatically
- Recurring commitments sustained — capacity recalculates when something shifts
- One audit trail — every decision traceable, every AI action owned by you
No chatbot sidebar. No prompt engineering. The AI operates inside your workflow — on the actual task, in the actual step. Accept its proposal and the change happens. Dismiss it and nothing moves.
Chatless. Frugal. Discreet.
Need compliance, safety, and audit controls at enterprise scale? See Enterprise →