Evaluating Agent Effectiveness

I was working on a support bot that used a LangGraph agent to troubleshoot customer issues. The bot was supposed to guide the customer through a series of questions to identify the root cause of their problem, but I noticed that it was often getting stuck in an infinite loop, asking the same questions over and over. The issue wasn't with the graph itself, but with the way the agent was evaluating its progress towards the conversation goal. It was focusing too much on the short-term objective of answering the current question, rather than the long-term goal of resolving the customer's issue.

This is a classic problem in agentic AI, where the agent gets bogged down in the details of the current task and loses sight of the bigger picture. To solve this, I needed to find a way to evaluate the agent's effectiveness through indirect objectives, rather than just its immediate actions. That's where MCP's Resource primitive came in.

The Resource primitive in MCP allows you to define abstract resources that the agent can use to track its progress towards a goal. These resources can be anything from a simple counter to a complex data structure, and they can be used to evaluate the agent's performance over time. In my case, I defined a resource called "CustomerProgress" that tracked the customer's progress through the troubleshooting process.

Here's an example of how I used the Resource primitive in Python:

import langgraph as lg
from mcp import Resource, add_resource

# Define the CustomerProgress resource
class CustomerProgress(Resource):
    def __init__(self):
        self.progress = 0

    def update(self, new_progress):
        self.progress = new_progress

# Create a LangGraph agent
agent = lg.Agent()

# Add the CustomerProgress resource to the agent
add_resource(agent, CustomerProgress())

# Define a state graph for the troubleshooting process
graph = lg.StateGraph()
graph.add_node("start", "Welcome to the troubleshooting process!")
graph.add_node("question1", "What is your issue?")
graph.add_node("question2", "Can you provide more details?")
graph.add_node("resolution", "I think I have found the solution to your issue.")

# Add conditional edges to the graph based on the customer's responses
graph.add_conditional_edges("question1", "question2", lambda x: x == "yes")
graph.add_conditional_edges("question2", "resolution", lambda x: x == "yes")

# Define a checkpointer to update the CustomerProgress resource
def checkpoint_progress(state):
    if state == "question1":
        agent.resources["CustomerProgress"].update(0.2)
    elif state == "question2":
        agent.resources["CustomerProgress"].update(0.5)
    elif state == "resolution":
        agent.resources["CustomerProgress"].update(1.0)

# Add the checkpointer to the graph
graph.add_checkpoint(checkpoint_progress)

# Run the agent
agent.run(graph)

In this example, the CustomerProgress resource is used to track the customer's progress through the troubleshooting process. The checkpoint_progress function updates the resource based on the current state of the graph, and the add_checkpoint method adds this function to the graph as a checkpoint. The agent can then use this resource to evaluate its progress towards the conversation goal.

One practical gotcha I learned while working with the Resource primitive is that it's easy to get caught up in defining complex resources and forget to actually use them to evaluate the agent's performance. Make sure to define clear objectives and metrics for your resources, and use them to inform your agent's decision-making process.

As I continue to work on my support bot, I'm excited to explore more ways to use MCP's resources and LangGraph's state graphs to create a more effective and efficient conversation flow. Tomorrow, I'll be diving deeper into the world of agentic AI and exploring new ways to optimize my agent's performance.