Day 21/30: Avoiding Agent Loops

I still remember the day our support bot, which was supposed to be a showcase of agentic AI in action, started acting like it was stuck in some kind of bizarre loop. Customers would ask a question, and instead of providing a helpful response, the bot would just repeat the same canned phrases over and over, never actually addressing the issue at hand. It was as if the bot had gotten stuck in a never-ending conversation with itself, unable to escape the cycle of meaningless chatter.

After digging into the logs, we discovered the root cause: our bot was designed as a multi-agent system, with different agents responsible for handling different aspects of the conversation. The problem arose when these agents started talking to each other in a way that created a kind of feedback loop. Agent A would ask Agent B a question, which would prompt Agent B to ask Agent C, which would then ask Agent A, and so on. Before long, the whole system was bogged down in a sea of circular conversations, with no clear way to break the cycle.

We realized that we needed to implement some kind of guardrail to prevent this kind of behavior. That's where LangGraph and the Model Context Protocol (MCP) came in. By using these tools, we could create a more structured and intentional conversation flow between our agents. Specifically, we used the StateGraph API from LangGraph to define the possible states of our conversation, and the add_conditional_edges method to specify the conditions under which an agent could transition from one state to another.

Here's a simplified example of how we might implement this using Python:

import langgraph as lg

# Define the possible states of our conversation
states = ['initial', 'question_asked', 'response_received', 'followup_asked']

# Create a StateGraph to manage the conversation flow
graph = lg.StateGraph(states)

# Define the conditions for transitioning between states
graph.add_conditional_edges([
    ('initial', 'question_asked', lambda context: context['user_input'] is not None),
    ('question_asked', 'response_received', lambda context: context['agent_response'] is not None),
    ('response_received', 'followup_asked', lambda context: context['user_followup'] is not None)
])

# Define the actions to take in each state
def ask_question(context):
    # Code to ask the user a question goes here
    pass

def provide_response(context):
    # Code to provide a response to the user goes here
    pass

def ask_followup(context):
    # Code to ask a followup question goes here
    pass

# Create a dictionary to store the actions for each state
actions = {
    'initial': ask_question,
    'question_asked': provide_response,
    'response_received': ask_followup
}

# Use the StateGraph to manage the conversation flow
def conversation_manager(context):
    current_state = graph.get_current_state(context)
    action = actions[current_state]
    action(context)

# Test the conversation manager
context = {'user_input': 'Hello'}
conversation_manager(context)

In this example, we define a StateGraph with four possible states: initial, question_asked, response_received, and followup_asked. We then use the add_conditional_edges method to specify the conditions under which the conversation can transition from one state to another. Finally, we define a dictionary of actions to take in each state, and use the StateGraph to manage the conversation flow.

One practical gotcha we learned from this experience is the importance of carefully defining the conditions for transitioning between states. If these conditions are too broad or too narrow, the conversation flow can become stuck or start to loop indefinitely. By taking the time to carefully consider the possible states and transitions of our conversation, we can avoid the kind of multi-agent groupchat spiral that plagued our support bot.

As we continue to build out our agentic AI system, we're excited to explore new ways to use LangGraph and MCP to create more sophisticated and intentional conversation flows. Tomorrow, we'll be diving into another key challenge in building agentic AI systems, one that requires us to think carefully about the relationships between our agents and the world around them.