I still remember the day our support bot, built on top of LangGraph, started producing bizarre responses. A user would ask for help with a simple issue, and the bot would veer off into a completely unrelated topic. The problem wasn't that the bot was giving incorrect answers, but rather that it seemed to be forgetting the context of the conversation altogether. We'd see the bot respond to a question about password reset with a lengthy explanation of our company's history. It was as if the bot had lost its train of thought mid-conversation.
After digging into the logs, we realized that the issue stemmed from the bot's inability to self-correct. It would make a mistake, but then it wouldn't recognize that mistake or take steps to rectify it. This is where the reflection pattern comes in, particularly with the use of a critic node. The idea is to have the agent periodically reflect on its own actions and decisions, evaluating whether they align with its goals and the current context.
In our case, we implemented a critic node that would assess the bot's responses and check if they were relevant to the conversation. If the critic node determined that a response was off-topic or incorrect, it would trigger a self-correction mechanism, allowing the bot to recover from its mistake. This involved adding a new node to our LangGraph that would serve as the critic, and then using the add_conditional_edges method to connect this node to the rest of the graph.
Here's a simplified example of how we implemented the critic node:
import langgraph as lg
# Define the graph
graph = lg.StateGraph()
# Add nodes for the conversation states
start_node = graph.add_node("start")
question_node = graph.add_node("question")
response_node = graph.add_node("response")
# Add the critic node
critic_node = graph.add_node("critic")
# Define the critic function
def critic_function(state, response):
# Simplified example: check if the response contains a certain keyword
if "password" in response and state == "question":
return True
return False
# Add conditional edges based on the critic function
graph.add_conditional_edges(
response_node,
[critic_node],
lambda state, response: critic_function(state, response)
)
# Add a self-correction mechanism
def self_correct(state, response):
# Simplified example: return a generic apology message
return "Sorry, I made a mistake. Let me try again."
graph.add_conditional_edges(
critic_node,
[question_node],
lambda state, response: True,
self_correct
)
This code snippet illustrates how we used the critic node to evaluate the bot's responses and trigger self-correction when necessary. Of course, in a real-world implementation, the critic function and self-correction mechanism would be more sophisticated.
One practical gotcha we encountered while implementing the reflection pattern was ensuring that the critic node didn't become too overly critical. If the critic node was too sensitive, it would trigger self-correction too frequently, leading to a bot that seemed indecisive or uncertain. We had to fine-tune the critic function to strike the right balance between correcting mistakes and allowing the bot to confidently provide responses.
As we continue to develop our agentic AI systems, we're realizing that teaching an agent to self-correct is just the beginning. Tomorrow, we'll explore how to take this concept further, enabling our agents to not only reflect on their actions but also adapt and learn from their experiences.
