Multi-Agent Swarms: The Architecture Behind AioClaw

Why a single LLM call is not orchestration

An AI agent that answers one prompt is a feature. An agentic system that decomposes a goal, dispatches sub-tasks to specialized agents, shares state and recovers from failure is an orchestration platform. At AI Orchestration LLC we built AioClaw as the control room for that second class of system.

The AioClaw architecture in five layers

1. Conversation layer — a single chat surface that lets a human and any LLM (OpenAI, Anthropic, Google, Mistral, local Llama 3) speak the same protocol.
2. Context graph — every message, file and tool call is stored as a node so downstream agents inherit full lineage instead of re-prompting from scratch.
3. Agent registry — typed agents with declared capabilities, cost ceilings and required scopes.
4. Task scheduler — a queue-backed dispatcher that fans out work, enforces concurrency limits per provider and retries on transient errors.
5. Serverless runtime — all execution runs on ephemeral functions so there are no idle GPUs and no infra to babysit.

Managing context between agents

The hardest problem in swarms is not "which model is smartest" — it is state synchronization. AioClaw treats context as a first-class object with three slices:

• Global context — the user goal, constraints and shared artifacts.
• Role context — only what an agent needs to do its job (principle of least context).
• Episodic memory — compressed summaries written back after each task so the next agent does not re-read 40k tokens.

Human-in-the-loop without slowing the swarm

AioClaw exposes checkpoints: agents can pause and request approval, but the rest of the graph keeps running on independent branches. This is how we get the speed of automation with the safety of review.

Takeaways for engineering teams

• Treat orchestration as infrastructure, not as prompt engineering.
• Persist context as a graph, not as a chat log.
• Run on serverless to keep agent cost proportional to actual work.