art by: J. Sridharan, Dubai
Agentic AI Orchestrator Protocols in Simple Terms
Earlier in Nov 2024, Anthropic open sourced MCP (Model Context Protocol) - an open standard that enables developers to build secure, two-way connections between their GenAI applications and the tools and data sources. MCP is an open-source protocol that simplifies connections between AI systems and various data sources to help deliver faster innovation in context-aware Agentic AI applications.
What are Agentic AI Orchestrators
Agentic AI is seen as the second big evolution of GenAI, and
Agentic AI orchestrators are seen to be the enablers of this evolution. As LLMs
continue to evolve providing multi-modal and extended built-in capabilities - in
bare terms, LLMs are good in the predictability of the next word, which makes
them great poem reciters and essay writers or converting text to visuals or translating
languages and many more standalone tasks. But there is a fundamental limitation – LLMs cannot
take any intelligent action unless they are integrated with tools and data
sources – for example: if you ask an LLM any information that it was not trained
on (for example: current stock market trends) - unless it is connected to a web
search engine – it cannot provide you with an accurate answer.
- Task to accomplish,
- The model/s or Agent/s,
- Tools it needs to accomplish the task
- Answer the agent provides.
Without standardized protocols, the following are few of the key challenges to accomplish TATA.
- Custom built implementations required significant engineering effort to plumb tools and data sources. In addition, consider the re-engineering efforts when sources change.
- When connecting multiple agents, inconsistent prompt logic with different methods for accessing and federating tools and data will provide inefficient answers.
- The Scale problem - "n times m problem" - where a a large number of client applications interacting with a mesh of servers and tools will result in a complex web of integrations, duplicity, each requiring specific integration efforts.
MCP allows AI Agents to use tooling, resources and even
prompt libraries in a standardized manner, thus extending the Agentic AI capabilities significantly to
build more meaningful GenAI applications.
Just to keep the MCP architecture simple, MCP uses a client-server architecture, primarily at a high level, the key components being an MCP client, the MCP server and the MCP communication protocol. Developers expose their data through lightweight MCP servers. For example, Anthropic has released a few popular MCP server codes already such as for Google maps, or Slack. By connecting to these MCP servers, you can easily build an Agentic AI MCP client following the MCP protocols.
MCP Architecture
MCP
uses a client-server architecture that
contains the following components and is shown in the following figure:
- Host: An MCP host is a program or AI tool that requires access to data through the MCP protocol, such as Claude Desktop, an integrated development environment (IDE), or any other AI application.
- Client: Protocol clients that maintain one-to-one connections with servers.
- Server: Lightweight programs that expose capabilities through standardized MCP, allows access to data sources tools and even prompt libraries.
- Local data sources: Your databases, local data sources, and services that MCP servers can securely access.
- Remote services: External systems available over the internet through APIs that MCP servers can connect to.
MCP, thus by providing an open-source protocol and a universal standard that simplifies connections between AI systems and various data sources - will deliver agility in building efficient and context-aware AI applications. Consequently, this will enable AI agents to autonomously perform complex tasks.
The success and widespread adoption of protocols like MCP depends upon industry participation and standardization efforts on interoperability and portability, and adherence to common standards, allowing AI applications to operate across different platforms and jurisdictions, crucial for global companies and responsible AI.
MCP will help build trust by ensuring AI systems are transparent, reliable and secure. The clarity provided by the MCP protocol guidelines will reduce compliance complexity, will lower barriers to innovation and will foster faster development of AI products.