Multi-Agent Orchestration in Hermes: How Mixture-of-Agents Produces Better Results

Most AI assistants follow a simple pattern: user asks, model answers. Hermes Agent uses Mixture-of-Agents (MOA) — multiple specialist agents collaborating on complex tasks, producing results no single model could achieve alone.

The MOA Pattern

In MOA, a task is decomposed into perspectives, each handled by a specialized advisor agent, and then synthesized by an aggregator:

User Goal: "Is this codebase production-ready?"

Advisor 1 (Security)     → finds 3 CVEs
Advisor 2 (Performance)  → finds 2 bottlenecks
Advisor 3 (Architecture) → questions the schema
Advisor 4 (Testing)      → notes missing coverage

Aggregator → synthesizes a ranked report

Each advisor runs with a different system prompt, toolset, and sometimes a different underlying model. They operate in parallel — no one waits for another.

Why MOA Produces Better Results

Multiple specialist perspectives outperform a single generalist because there are no blind spots — a security-focused agent catches things a generalist might dismiss. Each advisor focuses its limited context on one dimension. If two advisors disagree, the aggregator flags the discrepancy. Advisors with stronger evidence get more weight.

Prompt Caching in MOA

Recent updates restored prompt caching for the aggregator and advisors — a critical optimization. The system prompt is shared across the entire agent tree. Caching it means each advisor pays the prompt-processing cost only once, making MOA runs up to 4x faster.

When to Use MOA

Simple questions are faster with a single agent. MOA shines on code audits (separate advisors for security, performance, style, correctness), architecture reviews (different perspectives on the same design), decision analysis (pros/cons from multiple angles), and quality gates (pre-PR checks combining linting, testing, and security scanning).

Configuring MOA

moa:
  enabled: true
  advisors:
    security:
      model: deepseek-v4
      temperature: 0.3
    performance:
      model: claude-haiku-4.5
      temperature: 0.5
    architecture:
      model: claude-sonnet-4.5
      temperature: 0.7
  aggregator:
    model: claude-opus-4.7

MOA transforms Hermes from a single AI assistant into a coordinated team of specialists — all working on your problem simultaneously.