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Table Of Contents
Design Multi-Agent AI Systems Using MCP and A2A
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In this chapter, we took a deep dive into the operational flow of agentic AI systems driven by large language models. We introduced the core agent loop – sense, think, act – and illustrated how it operates in frameworks such as AutoGen, where the LLM dictates the agent’s behavior through tool calls. We explored how agents use memory, goals, and state management within the constraints of the LLM’s context window, and how these responsibilities are handled by the surrounding AI framework or system.
We also examined the importance of planning and reasoning mechanisms, both in single-agent and multi-agent systems. With the rise of specialized reasoning models and the growing complexity of agent workflows, structuring interactions and decomposing tasks become essential for robust behavior. Then, we turned our attention to tool use, which is a critical enabler of agentic capability. After all, even the most intelligent LLM can observe or impact the...