I was working on a support bot recently, and it was supposed to handle a wide range of user queries, from simple FAQs to more complex troubleshooting issues. The initial idea was to use a single giant prompt that would cover all possible scenarios, and the LangGraph model would magically figure out the context and respond accordingly. Sounds simple, right? Well, it didn't quite work out that way.
The bot would often get confused and provide irrelevant or even contradictory responses. For instance, if a user asked about the return policy and then followed up with a question about shipping, the bot would sometimes respond with a shipping answer that didn't take into account the previous conversation about returns. It was as if the bot had forgotten the context of the conversation. This was frustrating, not just for the users, but also for me, as I had to spend hours debugging and trying to figure out what was going wrong.
The problem, I realized, was that I was trying to cram too much functionality into a single agent. The giant prompt was trying to cover too many bases, and the model was getting overwhelmed. This is where the concept of specialist agents comes in. Instead of having one giant agent that tries to do everything, it's often better to have multiple specialist agents, each focused on a specific task or domain.
In the case of the support bot, I could have one agent that handles FAQs, another that handles returns and refunds, and another that handles shipping and logistics. Each agent would have its own prompt, tailored to its specific domain, and would be responsible for handling a specific set of user queries. This approach has several advantages. For one, it allows each agent to focus on a specific task, without getting overwhelmed by the complexity of the entire system. It also makes it easier to update and maintain the system, as changes can be made to individual agents without affecting the entire system.
Here's an example of how I might implement this using LangGraph and MCP:
import langgraph as lg
from mcp import ModelContextProtocol
# Define the specialist agents
faqs_agent = lg.StateGraph()
faqs_agent.add_node("start", "Welcome to our FAQs! What can I help you with?")
faqs_agent.add_node("returns", "Our return policy is...")
returns_agent = lg.StateGraph()
returns_agent.add_node("start", "Returns and refunds...")
shipping_agent = lg.StateGraph()
shipping_agent.add_node("start", "Shipping and logistics...")
# Define the MCP prompts for each agent
faqs_prompt = MCP.tools.prompt("What is your question about our product?")
returns_prompt = MCP.tools.prompt("What is your return or refund question?")
shipping_prompt = MCP.tools.prompt("What is your shipping or logistics question?")
# Use the MCP protocol to interact with the agents
mcp = ModelContextProtocol()
mcp.add_agent(faqs_agent, faqs_prompt)
mcp.add_agent(returns_agent, returns_prompt)
mcp.add_agent(shipping_agent, shipping_prompt)
# Test the system
user_input = "What is your return policy?"
response = mcp.interact(user_input)
print(response)
In this example, we define three specialist agents, each with its own prompt and state graph. We then use the MCP protocol to interact with the agents, routing the user's input to the relevant agent based on the prompt.
One practical gotcha to watch out for when using specialist agents is to make sure that the prompts are specific enough to route the user's input to the correct agent. If the prompts are too general, the system may get confused and route the input to the wrong agent. For instance, if the prompts for the returns and shipping agents are too similar, the system may mistakenly route a shipping question to the returns agent.
As we continue to build more complex agentic AI systems, we'll need to consider how to coordinate and manage multiple specialist agents, and how to ensure that they work together seamlessly to provide a cohesive user experience. Tomorrow, we'll explore some of the challenges and opportunities of building these kinds of systems, and how LangGraph and MCP can help us overcome them.
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