Multi-Agent Collaboration Base: Three-Layer Architecture Methodology

Problem

When multiple AI roles need to work on one task, the practical issue is not just making models talk, but writing clear boundaries for each role: what identity it has, which tools it can call, and what capabilities it owns. This skill provides a minimal Python 3.8+ example that splits an agent into identity, tools, and skills, then uses AgentOrchestrator to register named roles and execute a shared task sequentially, making it useful for discussing a multi-agent collaboration skeleton.

How It Works

• Agent layer: the Agent class stores name, identity, tools, and skills, and execute(task) prints the role, tools, and capability trace for the current step.
• Orchestration layer: AgentOrchestrator keeps an agents dictionary, registers roles with register_agent, and run_collaboration iterates over given agent_names to execute the same task and collect results.
• Example scenario: customer service, operations, and technical roles process a user complaint about slow app login, demonstrating how identity, tools, and capabilities can be combined.

Boundaries

The provided implementation is a process demo template, not a production-ready runtime: tool calls are mainly printed, tasks run sequentially, and the docs do not show real APIs, state persistence, error retry, permission control, or concurrent scheduling. For real business use, add real tool interfaces, result validation, task decomposition, and rollback policies.