For AI companies & research labs
Model safety is not institutional governance
You’ve invested in alignment research, red-teaming, and safety evals. Your models are responsible. But your AI agents — the ones sending emails, writing code, managing infrastructure, and talking to customers — operate within an organisational context that no safety paper covers. Corporate governance infrastructure for AI-native organisations is the missing layer. Who authorised this deployment? What constraints apply to this domain? Who gets notified when an agent hits an edge case?
The problem
AI companies face a unique version of governance debt. You build AI agents that act autonomously — and then deploy them internally with no more governance than a Slack channel and a shared doc. Your safety team focuses on model behaviour. Nobody owns organisational governance: which agents can do what, under whose authority, with what constraints.
As regulatory pressure intensifies — the EU AI Act, NIST AI RMF, sector-specific requirements — “we have RLHF” is not a governance answer. Regulators want to see decision trails, constraint enforcement, escalation chains, and institutional accountability. Model cards don’t provide that.
How Constellation solves it
Governance infrastructure for AI-native organisations
Constellation sits between your AI agents and the actions they take. Every agent action passes through a governance gate that checks institutional constraints in real time. Not model-level safety — organisational rules: authority boundaries, domain restrictions, escalation requirements, approval sequences.
MCP governance gate
Plugs into any MCP-compatible agent. Before any action executes, constraints are checked in <200ms. Pass, flag, or escalate — the agent never exceeds its authority.
Progressive delegation
Start agents in shadow mode — they propose actions but don’t execute. As trust builds, progressively delegate more authority. Full audit trail at every stage.
Agent-specific constraints
Different agents get different boundaries. Your coding agent has different authority than your customer-facing agent. Constraints are scoped by domain, audience, and risk level.
Governance traces as regulatory evidence
Every constraint check, every escalation, every delegation decision is logged. When regulators ask “how do you govern your AI systems?” you have infrastructure, not documents.
What changes
Model safety and institutional governance become complementary layers. Your safety team handles alignment. Constellation handles authority, boundaries, and accountability. Regulators see infrastructure, not promises.
Key concepts
Govern your agents before regulators make you
You’re building AI that acts autonomously. Constellation ensures it acts within boundaries your organisation defines — not boundaries a regulator imposes after something goes wrong.