By now, everyone is aware of generative AI fueled by large language models (LLMs) and generative pre-trained transformers (GPTs). The next level of innovation is agentic AI and the autonomous AI agents that drive it. Using Model Context Protocol (MCP) to facilitate agent-to-agent communication, these systems are revolutionizing how enterprises automate tasks and orchestrate complex workflows.
Powered by LLMs, vector databases, retrieval augmented generation (RAG) pipelines and additional tools, these AI agents are expanding extensively, giving rise to multi-agent systems, cross-agent protocols, and context-sharing standards. But these autonomous agents also introduce new challenges in monitoring, debugging, and security.
Go to the Dynatrace BLOG POST where we’ll examine in detail the fundamentals of AI agents, models, and the emerging standards that help them communicate, like Agent2Agent (A2A) and Model Context Protocol (MCP).
Key takeaways:
- Autonomous AI agents are the backbone of agentic AI. These services combine to deliver adaptable automated tasks.
- AI agents depend on LLMs and orchestration logic. These technologies maintain the agent’s state, session memory, context, and reasoning strategies.
- Agents depend on protocols, such as A2A and MCP, to effectively communicate. Models and agents need these protocols to manage multi-agent communication.
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Links:
Understand the latest AI revolution: Agentic AI, Model Context Protocol (MCP) and all that jazz blog post
Dynatrace MCP server on GitHub
