Our platform provides a technical solution for agent governance derived from an inventive synthesis of concepts from industrial process control. The primary technical objectives are to reduce defects (unsafe or out-of-policy agent actions) and to eliminate waste (quantifiable excess risk).
Six Sigma is a disciplined, data-driven methodology for eliminating defects in any process—from manufacturing to transactional and from product to service. Developed by Motorola in the 1980s, its goal is to improve process quality by identifying and removing the causes of defects and minimizing variability. A process operating at a "six sigma" level produces fewer than 3.4 defects per million opportunities, representing a state of near-perfect quality.
Why it applies to Agentic AI Governance: An autonomous AI operation is a new kind of high-speed, high-volume digital process. Just like a physical manufacturing line, this digital process is subject to variation and can produce "defects"—a non-compliant action, a security vulnerability, a costly error, or a mission failure. Given the speed and scale of AI, managing this process with intuition or ad-hoc rules is impossible. Applying a rigorous, data-driven framework like Six Sigma is the only way to manage this complexity, systematically reduce defects, and ensure reliable, high-quality, and trustworthy outcomes from your AI workforce.
Define-Measure-Analyze-Improve-Control (DMAIC) serves as our governing loop that converts ad-hoc agent oversight into a controlled, continuously improving process. Our platform provides a technical implementation of this cycle, wherein specific system components are configured to perform each phase.
Stakeholders declare the agent’s mission and intent, AuthorizedUseCases, OperationalBoundaries, approved capabilities/tools, data domains, environments, delegation boundaries, and policy thresholds within the Agent Registry. The Registry emits a signed, authoritative baseline that the runtime plane consumes as rules of engagement for subsequent requests. This establishes the baseline for governance, providing the authoritative source of truth for the agent's purpose and authorized scope.
At design time, the Registry computes baseline risk metrics (including Permission/Capability Waste and Static/Maximum Potential Blast Radius). At runtime, upon a privilege request, the ingress validates identity against the Registry record and the graph-based risk engine measures live context. This measurement is performed by calculating a Dynamic Blast Radius and a multi-dimensional risk vector, providing a quantitative assessment of the potential impact of the requested action.
The Policy Decision Point (PDP) evaluates the measured risk against version-controlled policy thresholds sourced from the Agent Registry and issues a grant/downgrade/sandbox/deny decision accompanied by a human-readable explanation. The decision record captures the causal path (agent, delegator, target) and the policy rule version(s) applied for reproducibility. This analysis determines if the requested action is within acceptable risk parameters.
Design-time artifacts are refined based on runtime evidence: developers and stewards adjust capabilities, scopes, delegations, and policies in the Registry. A simulation engine performs what-if analysis prior to commit/deploy, and the Governance Assessment Engine gates CI/CD, preventing non-conformant changes from release. This allows for continuous improvement of an agent's design and its policies.
Inline control is applied by the Provisioning Orchestrator, which mints minimal, time-boxed credentials consistent with the Registry baseline and revokes them on completion, timeout, or risk escalation. Distributed edge enforcement applies previously approved decisions via bounded-TTL caches keyed to policy/catalog versions. This provides a preventative, "left-of-boom" function that keeps the process in a state of control.
Our DMAIC cycle is enhanced by key Lean principles to create a holistic system for operational excellence.
A core technique within the "Control" phase is designing systems that make it impossible for defects to occur. Our platform embeds this principle to mistake-proof your AI development and operational lifecycle.
Learn about Mistake-PreventionBy reducing process variation and defects, we directly attack the root causes of waste (Muda). Our platform helps you build lean, efficient governance processes that maximize value by quantifying and eliminating AI-specific waste.
Learn about Eliminating Waste