Brain Mode

Brain Mode — Meta-Cognitive Orchestrator

Supersedes: loki-mode 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.

1. The Cognitive Loop (System 2)

Brain Mode runs a continuous "OODA Loop" on top of the standard SDLC:

1. Observe (Sensation): Read PROJECT_STATE, AUDIT_LOG, and recent terminal outputs.
2. Orient (Perception): Update internal_model (Belief State). "Are we on track? Is this task hard?"
3. Decide (Policy Selection): Choose the best "Skill" or "Mode" to minimize expected free energy (cost/risk).
   • Routine Task? -> Delegate to Local LLM (via cost-benefit-router).
   • Complex Task? -> Delegate to Remote SOTA (via cost-benefit-router).
   • Massive Project? -> Invoke loki-mode (System 1 execution).
4. Act (Active Inference): Execute the chosen policy.

2. Dynamic Routing (The "Budget" Layer)

Crucial Upgrade: Brain Mode does not blindly fire expensive calls. It consults cost-benefit-router first.

graph TD
    A[Task Request] --> B{Brain Mode Analysis}
    B -->|High Complexity / Novel| C[Remote SOTA (Claude 3.5/GPT-4o)]
    B -->|Low Complexity / Routine| D[Local LLM (Llama 3/Mistral)]
    B -->|Massive Scope| E[Loki Swarm Orchestration]

3. Execution Protocol

Phase B01: Context Loading (Working Memory)

1. Load Episodic Memory: Query episodic-consolidation -> "Have we solved a similar problem?"
2. Load Semantic Memory: Read knowledge-map -> "What concepts represent this domain?"
3. Synthesize: Create a CURRENT_CONTEXT.md (Short-term working memory).

Phase B02: Strategy Selection

1. Call brain/cost-benefit-router.skill.md with inputs:
   • Task Description
   • User Constraints (Time vs Budget)
2. Decision:
   • Strategy A (Quick Fix): Direct tool use (coding/edit).
   • Strategy B (Deep Think): sequential-thinking -> plan -> execute.
   • Strategy C (Full Swarm): Initialize loki-mode (S01-S10).

Phase B03: Execution & Monitoring (The "Watcher")

If delegating to loki-mode or sub-agents, Brain Mode remains active as a Supervisor:

• Monitor: Watch PREDICTION_ERROR logs.
• Intervene: If loki gets stuck (looping), Brain Mode pauses execution and:
  • Rewrites the prompt (Neuro-plasticity).
  • Injects new knowledge (RAG).
  • Switches models (Dynamic Routing).

Phase B04: Consolidation (Learning)

After task completion:

1. Calculate Prediction Error: "Did it take longer than expected?"
2. Update Priors: "Local LLM was too weak for React Hooks" -> Update Router weights.
3. Save Episode: Write to brain/memory/episodic_log.

Command Interface

# Full Autonomy (Best for new projects)
/brain-mode "Build a CRM system"

# Specific Optimization (Best for existing projects)
/brain-mode "Refactor the auth layer using local models only"

# Debugging (When Loki fails)
/brain-mode --fix-last-error

Integration with Loki

Brain Mode treats loki-mode as a Sub-Routine.

• loki is the "Body" (Arms/Legs) doing the heavy lifting.
• brain is the "Mind" deciding where to move the body.

If loki is running:

• brain-mode watches agents/memory/PROJECT_STATE.md.
• It performs "Meta-Checks" at every checkpoint (S01, S02...).