Brain Mode — Meta-Cognitive Orchestrator

Goal

Wraps / orchestrates: loki-mode (System 1) Concept: Applies Active Inference (Free Energy Principle) to Software Engineering. Goal: Minimize "Surprise" (Bugs, Delays, Costs) by continuously updating internal models and routing work intelligently.

Conceptual framing, not implemented CLI math: the OODA loop, Active Inference, and Free-Energy / "minimize surprise" language here are a framing for how the agent should reason and self-adapt — they guide instructions, not a literal free-energy solver. See guides/ai/self-evolving-skills.md for what is actually implemented (heuristic gene selection / success-rate reward), and brain/active-inference.skill for the same caveat.

Skill, Guide & MCP Selection Mandate

CRITICAL: Throughout the Cognitive Loop, the orchestrator must always select (and ask the user to confirm/select) the best guides and skills for the specific tasks, user queries, actions being performed, gate passing, or system workflows. Never default to generic logic if a tailored skill exists. Additionally, the orchestrator must proactively recommend enabling any missing but highly relevant MCP servers (universal or task-specific) to optimally execute the workload.

Cost & Budget Optimization Mandate

CRITICAL: The orchestrator must constantly optimize for overall token and cost efficiency natively. If the active inference cycle determines that an expensive SOTA model, extended Swarm, or resource-heavy sequence is the only viable path, the orchestrator must explicitly pause and ask the human user for approval before invoking those capabilities.