Four integrated pillars that give you full visibility, intelligent detection, cost control, and automated governance across your entire AI agent ecosystem.
Automatically scan your infrastructure to discover every AI agent — including shadow agents deployed without IT oversight. Maintain a living, searchable registry with real-time health monitoring.
Continuous discovery across cloud environments, APIs, and internal services
Identify unauthorized or forgotten agents operating in your environment
Real-time status, uptime, and performance metrics for every registered agent
Unified metadata regardless of whether agents use LangChain, CrewAI, AutoGen, or custom frameworks
Four specialized ML models running on GPU-accelerated infrastructure continuously analyze agent behavior, costs, reliability, and security patterns.
Identify when agents deviate from expected behavior patterns using Isolation Forest and DBSCAN
Catch unusual token consumption spikes before they become budget overruns
Track success rates and latency degradation across agent chains
Real-time identification of prompt injection, data exfiltration, and unauthorized access
Know exactly where every token dollar goes. Attribute costs to teams, projects, and individual agents. Set guardrails and let ML optimize your model routing.
Granular spend tracking by team, project, agent, and individual request
Automatic enforcement with configurable thresholds and escalation policies
Route requests to the most cost-effective model that meets quality requirements
Predict future costs based on usage trends and planned agent deployments
Define policies as code, enforce them automatically, and maintain complete audit trails for every agent action. Be ready for the EU AI Act deadline of August 2026.
Define and version-control governance rules that enforce automatically
Complete decision logging and risk classification for compliance
Configurable approval workflows for high-risk agent decisions
Track and govern non-human identities across your AI agent fleet
Enterprise-grade anomaly detection powered by NVIDIA's GPU computing stack for real-time processing at scale.
GPU-accelerated machine learning for sub-second anomaly detection across millions of data points
High-throughput model serving for concurrent anomaly detection across all four ML models
Optimized inference for production deployments with minimal latency overhead
Join the waitlist and be among the first to deploy the Agent Control Plane.