Multi-Cognitive Orchestration

Hematite manages heterogeneous agent pools — LLMs, rule engines, search indexes, vector databases — through a unified orchestration layer. Each session is routed to the most appropriate cognitive source based on adaptive scoring.

Agent Pool Lifecycle

Every agent in the pool moves through a defined lifecycle:

  1. LOAD — Agent is initialized with its configuration, context, and available tools. The kernel registers it in the routing table with its capabilities and domain specializations.
  2. EXECUTE — Agent receives a task via the router. It processes the prompt through its cognitive pipeline (context retrieval → generation → validation) and returns structured output.
  3. CONVERGE — When multiple agents contribute to a session, their outputs are aggregated through the collective decision framework. Position papers are generated, convergence gates evaluate agreement levels.
  4. HANDOFF — If a task requires domain-specific processing beyond the current agent's scope, it is handed off to a specialized agent via the handoff bus (producer → tracker → validator).
  5. TERMINATE — Session completes. Agent metrics are recorded for self-improvement. The agent returns to the pool or is retired based on health scores.

Adaptive Routing Engine

The router selects endpoints using a weighted scoring formula that balances load and latency:

score = (concurrent / max) × 0.7 + (latency / 1000) × 0.3

A lower score indicates a healthier endpoint. The router maintains a cache of recent scores to avoid redundant health checks.

Cross-Domain Handoffs

When an agent processes a prompt spanning multiple AIMCIS domains, it can trigger handoffs to specialized agents:

// Example: CRTV + INFO domain session
POST /api/v1/sessions
{
  "agent_type": "general",
  "prompt": "Generate a blog post about AI governance...",
  "domain": ["CRTV", "INFO"],
  "handoff_policy": "auto"
}

The orchestrator automatically routes the prompt through CRTV (creative) and INFO (information integrity) agents, aggregates their outputs, and returns a unified result.

Self-Improvement Loop

The noetic-ratchet crate tracks per-agent metrics across executions — response time, token efficiency, user satisfaction signals — and adjusts routing weights over time. Agents that consistently perform well receive higher priority in the scoring engine.

Key insight: The platform gets better with every operation. Routing decisions evolve based on observed performance, not static configuration.

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