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Multi-agent orchestration uses a hierarchical queen-led structure to coordinate specialized agents (researchers, coders, testers, architects) with shared memory and consensus mechanisms. This ensures coordinated decision-making, task distribution, and persistent knowledge across all agents working toward a common objective.

Byzantine consensus is a decision-making protocol that ensures reliable outcomes even when some agents fail or provide faulty data. It guarantees robust coordination by requiring agreement across agents, preventing any single faulty agent from derailing the entire swarm's decisions.

Collective memory systems use persistent storage layers (LRU cache, SQLite WAL) to consolidate and retrieve knowledge across sessions. This enables agents to build on previous learnings, avoid duplicating work, and improve efficiency through memory-based retrieval and event logs.

Yes. Multi-agent swarms work for full-stack projects by assigning specialized roles—Strategic Queen, Coder, Tester, Architect—to handle different aspects of development. The system automates task distribution, consensus on technical decisions, and maintains shared context across the entire delivery lifecycle.

The queen agent provides strategic direction and hierarchical coordination, distributing tasks to worker agents based on project objectives. It orchestrates effort across specialized roles, ensures consensus on decisions, and manages the collective memory layer for coordinated outcomes.

Byzantine consensus requires communication overhead and majority agreement, which can slow decisions if many agents disagree. It performs best when faulty agents are minority; performance degrades if too many agents fail or provide conflicting data simultaneously.