About
The operating system for AI agents in regulated environments
Maetra is the control plane for AI agent governance — discover, classify, govern, secure, and audit AI agents in one place.
As AI agents take real actions inside enterprises, regulators now require provable oversight of those actions. Maetra gives engineering, security, compliance, and governance teams both the visibility and the controls to run AI agents safely and legally.
What we do
From 2025 onwards, two things are true at once. AI agents are taking real actions inside companies — sending emails, moving money, generating code that gets deployed. At the same time, regulators including the EU AI Act and NIST AI RMF now require provable oversight of those actions. Most companies have neither the visibility nor the controls to operate safely or legally.
As enterprises move from AI pilots to production deployments, the gap between what they are running and what they can demonstrably govern keeps widening. Maetra closes that gap. It turns repository scans, agent inventory, compliance evidence, approval workflows, security signals, and audit history into a single working control plane for AI governance.
The result is an operations surface where a team can trust what was detected, inspect the evidence behind it, understand how data flows, and decide what governance action is needed — from discovery through to a regulator-ready audit trail.
The platform
Five modules, built to work independently and together:
- Discover — find every AI agent before it finds production. Scans connected repositories to inventory official, shadow, and unreviewed agents, surfacing new and changed agents as they appear.
- Comply — classify once, map across every framework. A multi-framework engine covering the EU AI Act, GDPR, NIST AI RMF, ISO 42001, the Colorado AI Act, and SOC 2, generating documentation from the same underlying agent record.
- Govern — route consequential actions to the right humans. Human approval orchestration with L1–L5 autonomy levels, quorum policies, and signed decisions delivered in Slack.
- Secure — monitor prompts, tool calls, and runtime behavior in real time. Detects prompt injection, data exfiltration, privilege escalation, policy violations, and cross-tenant leakage.
- Audit — evidence and activity logging, always on. An immutable, hash-chained event log that is full-text searchable and exportable as PDF, JSON, CSV, or to your SIEM — included with every plan.
Who it's for
Maetra is built for the teams responsible for AI systems in production: engineering teams deploying agents, security teams that need runtime protection and threat detection, compliance teams that need multi-framework classification and evidence, and governance teams that need approval workflows and audit trails. It is purpose-built for regulated organisations, including financial services, healthcare, public sector, and defense.
How we think
- Evidence before assertion. Every claim about an agent is backed by source, metadata, or audit context. Nothing is asserted that cannot be shown.
- Restraint. The product is a calm, precise operations surface — dense enough for repeated daily use, without decoration for its own sake.
- Preserve history. Scans and agent behavior change over time, so versioned views make every change inspectable.
- Diagram from data. Visualisations explain real runtime flows and evidence relationships. They come from the data, not from decoration.
Bring order to autonomous AI
Start a free 14-day trial, or get a demo to see the control plane on your own agents.