A governance-first architecture for enterprise-grade AI systems where auditability, predictability, and accountability matter as much as innovation.
An AI planner analyzes a business objective and produces a structured sequence of steps. Each step is executed by a scoped AI executor that sees only its specific step. A deterministic workflow engine enforces boundaries and manages progression.
Enables AI-driven productivity while limiting exposure. The system cannot expand its own scope because control over workflow progression is not delegated to AI.
An optional layer that adds structured validation. A second AI component reviews the executor's output against the defined step contract, providing a decision (complete or needs revision) with a confidence level.
Reduces costly rework and prevents flawed outputs from propagating through the system. Increases trust in AI-assisted processes without slowing innovation.
An optional layer that adds intelligent workflow adaptation. Evaluates outputs of both executor and reviewer, including confidence levels and iteration history. Recommends: continue iterating, advance, trigger replanning, or escalate for human intervention. The workflow engine retains final authority.
Improves workflow robustness. Helps the system adapt intelligently while maintaining governance and control. Reduces instability without surrendering authority to AI.
AI systems that are genuinely capable and adaptive, operating within explicit boundaries enforced by deterministic systems. Not "human in the loop" for every decision, but governance that is structural, not optional.
Traditional agent designs allow AI to both plan and control execution over time. This creates risk of scope expansion, unintended actions, and decisions that no one authorized.
AI systems often self-certify completion. Errors may not be detected until downstream impacts occur, by which point the damage is done and difficult to trace.
Fully autonomous agents make decisions that are difficult to trace or reproduce. When regulators or auditors ask "why did the system do this?", organizations struggle to answer.
Most organizations have AI policies on paper but lack the operational mechanisms to enforce them. Governance becomes an afterthought rather than a design principle.
Analyzes a business objective and produces a structured sequence of steps. After generating the plan, the planner exits. It does not control what happens next.
A scoped AI component that performs the work for a single step. It sees only what it needs for its specific step, not the full task, future steps, or information beyond its context contract.
An independent AI component that reviews the executor's output against defined criteria. Provides a decision (complete, needs revision, or cannot complete) with a confidence level and specific findings.
Monitors the overall workflow, evaluating patterns across steps: confidence trends, iteration counts, execution times, and failure conditions. Recommends actions but does not have final authority.
The deterministic core of the framework. Enforces step boundaries, manages state, controls information flow between steps, and determines all workflow progression. Follows explicit policy rules, not AI recommendations.
The framework treats failure handling as a core design element. Problems are detected early, responses are predictable, and escalation happens before small issues become large ones.
A step has been retried too many times, indicating the AI cannot resolve the issue through iteration alone. Prevents infinite loops and wasted resources.
Reviewer confidence remains below threshold across multiple attempts, indicating the AI cannot reliably complete this step.
Information accumulation approaching system limits, which can degrade performance or cause crashes.
Execution taking longer than expected, which may indicate a stuck process or external dependency failure.
The reviewer identifies contradictory criteria within a step, requiring human judgment to resolve the conflict.