Monolithic AI applications are out. Collaborative Multi-Agent Systems (MAS) are in! 🚀 As AI challenges grow more complex, trying to cram everything into a single, giant agent becomes a maintenance nightmare. That's why the Agent Development Kit (ADK) is designed for building sophisticated applications by composing multiple, specialized agents that collaborate towards a larger goal.
Think of it like building a high-performing team, where each member (agent) has a specific role, but they all work together. This approach brings huge advantages: enhanced modularity, specialization, reusability, and much cleaner control flows.
Here’s what you need to know about building MAS with ADK:
▶️ The Agent Types
- LLM Agents: The brains, powered by models like GPT or Gemini, capable of reasoning and generating.
- Workflow Agents: The orchestrators like SequentialAgent, ParallelAgent, and LoopAgent that manage execution flow among sub-agents.
- Custom Agents: Your own specialized agents with non-LLM logic for unique tasks.
▶️ Core Primitives for Composition
- Agent Hierarchy: Structuring parent-child relationships for clear delegation.
- Shared Session State: Passive communication where agents read/write to a common context.
- LLM-Driven Delegation: Dynamic routing where an LLM agent intelligently hands off tasks to the best-suited sub-agent.
- Explicit Invocation (AgentTool): Treating another agent as a callable tool, integrating its capabilities directly.
▶️ Common Multi-Agent Patterns
From Coordinator/Dispatcher to Sequential Pipelines, Parallel Fan-Out/Gather, Hierarchical Task Decomposition, Review/Critique, Iterative Refinement, and even Human-in-the-Loop — ADK's primitives let you implement powerful, established collaboration patterns.
This is the future of building robust, scalable AI applications. Understanding how to compose these systems is becoming a critical skill for any aspiring AI developer. What's the most complex task you think a Multi-Agent System could tackle? Share your thoughts below!
