Core Concepts
Decision Governance
The Problem
In modern enterprise systems, AI agents retrieve context, reason over it, and recommend or execute actions. This works well in development.
In production, three requirements emerge that most stacks do not address:
- Decisions must be attributable - who or what made each decision, and why
- Policies must be enforced at the runtime level, not in prompts
- Audit trails must be reconstructable - not reassembled from scattered logs
Without an explicit governance layer, teams discover that decision trails are spread across prompt logs, API traces, application logs, and database records. Reconstructing the reasoning behind a decision after the fact is fragile.
What Loop Engine Adds
Loop Engine sits between AI reasoning and operational execution. Its job is to govern the decision lifecycle.
1Enterprise Knowledge Sources2 ↓3RAG / Agent Reasoning Layer4 ↓5Loop Engine - Decision Governance6 ↓7Workflow / Execution SystemsEvery transition through a loop:
- Names the actor responsible (human, automation, or AI agent)
- Evaluates guard policies before allowing the transition
- Attaches evidence - what information the actor used
- Emits a structured event with full attribution
This creates a decision record at runtime, not a best-effort reconstruction later.
What This Is Not
Loop Engine is not a workflow engine. It does not schedule jobs, manage retries, or handle durable execution. Systems like Temporal or Prefect own those concerns.
Loop Engine is not an agent framework. It does not orchestrate tool calls or manage model context. LangGraph, OpenAI Assistants, and your own agent runtime own that layer.
Loop Engine governs the boundary between reasoning and action. It enforces who can do what, under what conditions, with what evidence.