Regulated
audit-ready trials and explainable AI
Regulators want transparency, boards want ROI, and your operations teams want to know why an agent did what it did. The Control Tower answers all three. It extends governance beyond models and infrastructure to seven dimensions, from data and prompts to cost, security, and agents, with a decision ledger recording the rationale behind every critical agent action.
Lineage, quality, access control, and bias detection across every data flow, feeding agents.
Version control, performance benchmarks, drift detection, and explainability for every production model.
Versioning, safety scanning, prompt injection defense, and reusability across teams and use cases.
Provenance tracking, security scanning, and quality gates for AI-generated and AI-supporting code.
Real-time consumption tracking, budget alerts, and optimization across cloud and tooling spend.
PII detection and masking, RBAC posture, threat monitoring, and guardrails across every agent, model, and tool call.
Behavior monitoring, benchmarking, anomaly detection, and lifecycle governance for every deployed agent.
audit-ready trials and explainable AI
scaling through reusable governance scaffolding
portfolio level ROI visibility across use cases
evaluation and drift detection
Traditional platforms monitor models and infrastructure only. The Control Tower extends governance across seven dimensions including prompts, agents, and cost, and operates at strategic, tactical, and operational levels, not just technical.
A portfolio-level governance plane ties every agent to consumption, performance, and business outcome metrics. Boards see AI spend, value delivered, and cost-per-decision in one view, with drill-down to individual agent economics and optimization opportunities.
Deployment is non-disruptive. Existing agents, models, and third-party tools connect through MCP and standard APIs, inheriting governance policies without rebuilds. Enterprises typically reach baseline observability in weeks, not quarters.
The decision ledger applies human oversight only where it matters. High-risk actions require approval with full context: rationale, confidence score, accountable owner. Routine decisions execute autonomously, keeping automation velocity intact.