An LLM-native decision tracking system that captures unexpected engineering outcomes as 'pressure events' to build a persistent learning foundation. It enables AI assistants to manage cases, log surprises, and promote recurring insights into global or project-specific knowledge bases.
The decision system for agentic engineering: keeps your project's decisions, rationale, and rejected paths in plain files and surfaces them to AI coding agents before they plan or change code.
Auto-captures decision context from multi-agent workflows to preserve the 'why' behind every choice. Enables task traceability, reasoning retrieval, and continuous improvement across planning and implementation sessions.
Persistent decision memory and contradiction detection for AI coding agents. Enforces architectural consistency across sessions — the agent cannot code until it loads prior decisions. Human resolves conflicts on a dashboard or in chat.
Enables AI agents to fork plans into counterfactual worlds, score them with rubrics and simulations, detect contradictions, measure regret, and merge a winner with a full audit trail.
