Mastering Error Messaging Agents: A Deep Dive
Explore advanced best practices for designing effective error messaging agents in AI-powered interfaces.
Executive Summary
In the realm of AI-powered interfaces, the design of error messaging agents has become paramount. As we advance into 2025, these agents play a critical role in enhancing user experience by providing clarity, context, and actionable guidance. This article delves into the current best practices for designing effective error messages, emphasizing the need for context-awareness, clarity, transparency, and emotional intelligence.
Integrating modern frameworks such as LangChain and AutoGen with vector databases like Pinecone and Weaviate is essential for crafting responsive error handling agents. These technologies enable error messages to reflect the agent's context and actions, providing users with a clear understanding of what went wrong and why.
Key Implementation Strategies
The future of error messaging agents lies in the seamless integration of Multi-turn conversation handling, robust memory management, and agent orchestration patterns. Utilizing the MCP protocol for structured communication and tool calling schemas helps enhance the system's transparency and usability.
Consider the following Python code snippet, illustrating memory management with LangChain:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
The architecture diagram (not displayed) elucidates the integration of error messaging agents with memory components and vector databases, facilitating efficient error context retrieval and user guidance. By adopting these practices and technologies, developers can create intelligent and user-centric error messaging agents that significantly improve the interaction quality of AI systems.
Introduction
In the ever-evolving landscape of digital interfaces, the role of error messaging agents has become increasingly vital. Defined as intelligent systems designed to communicate issues and guide users through resolution processes, these agents are more than mere digital road signs; they serve as critical components in user experience, ensuring smooth interaction with technology by providing clarity, context, and actionable guidance.
As we step into 2025, the integration of error messaging agents into AI-powered interfaces is not only about pinpointing what went wrong. These agents are expected to possess emotional intelligence, accommodate accessibility needs, and offer actionable insights. The modern best practices advocate for error messages that are context-aware, specific, transparent, and provide clear recovery paths.
To illustrate these concepts, consider the following Python example using the LangChain framework:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent = AgentExecutor(
memory=memory,
tools=[...], # Define tool calling patterns
)
The architecture of these systems often involves integrating vector databases like Pinecone to manage large data sets efficiently, enabling error messaging agents to deliver precise and context-sensitive messages.
Consider the architecture diagram: a series of interconnected modules representing the agent, memory management, vector databases, and user interfaces. This orchestration not only supports real-time decision-making but also ensures robust multi-turn conversation handling.
For example, implementing the MCP protocol can enhance transparency and error recovery, as shown in the following snippet:
const mcpProtocol = require('mcp-protocol');
mcpProtocol.onError((error) => {
console.log(`Error encountered: ${error.message}. Suggesting recovery steps...`);
});
In summary, error messaging agents play an indispensable role in the digital ecosystem of 2025, offering not just solutions but understanding and guidance through sophisticated, user-centered design.
Background
The evolution of error messaging in AI systems has been a journey towards enhancing user experience by providing more context-aware, clear, and actionable messages. Initially, error messages in computing were terse and cryptic, offering little information about what went wrong or how to fix it. As AI systems have become more sophisticated, particularly with the advent of advanced natural language processing models, the need for improved error messaging has grown. Today's AI-driven interfaces require error messages that not only diagnose issues but also assist users in resolving them efficiently.
A critical challenge in designing effective error messages is ensuring they are context-aware. For instance, in environments utilizing AI agents for task automation, understanding the agent's state, the intended action, and the point of failure is crucial. This is particularly true in systems using frameworks like LangChain or AutoGen, where agents interact with complex workflows and databases.
Example Implementation: Consider a scenario where an AI agent orchestrates tasks using a tool calling pattern integrated with a vector database such as Pinecone. The agent must handle errors gracefully, providing the user with clear guidance on how to proceed.
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.tools import Tool
from pinecone import PineconeClient
# Initialize Pinecone client
pinecone_client = PineconeClient(api_key='your-api-key')
# Set up conversation memory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Define a simple agent
class MyAgent:
def handle_error(self, error_message):
# Provide actionable advice
return f"Error encountered: {error_message}. Please check your API key."
# Implementing MCP protocol
agent_executor = AgentExecutor(
agent=MyAgent(),
memory=memory
)
# Example of error handling in a tool calling pattern
def call_tool():
try:
# Simulate a database interaction
response = pinecone_client.query("example_vector")
except Exception as e:
return agent_executor.agent.handle_error(str(e))
The above code snippet demonstrates how an agent can handle errors by providing context and offering actionable advice to the user. The integration with Pinecone exemplifies how modern AI systems interact with vector databases and use memory management to facilitate multi-turn conversations. Such practices strive to enhance the transparency, clarity, and usefulness of error messages in AI-powered interfaces.
As AI continues to advance, designing error messages that are informative, transparent, and user-centric remains a pivotal task, with a focus on ensuring that messages are accessible and actionable.
Methodology
The study on error messaging agents aimed to uncover best practices for designing effective error messages in AI-powered interfaces. Our research employed a multi-faceted approach, combining qualitative analysis, empirical testing, and technical experimentation. The goal was to derive actionable insights that developers can utilize to enhance user experience with error-handling mechanisms.
Research Methods
We began by conducting a comprehensive literature review to identify key challenges and solutions in error messaging. This was followed by interviews with developers and UX designers to gain practical insights. Additionally, we performed usability tests on existing error messaging systems to observe real-world effectiveness and gather qualitative data.
Criteria for Evaluating Error Messaging Solutions
Our evaluation criteria included:
- Context-awareness: The ability of the error message to reflect the specific context and actions of the AI agent.
- Clarity and specificity: The use of plain language to convey what went wrong and how users can address the issue.
- Transparency: Providing users with visibility into the agent's reasoning and actions preceding the error.
- Actionable advice: Offering clear recovery steps to guide users in resolving issues.
Technical Implementation
The technical implementation involved building a prototype using the LangChain framework to demonstrate error messaging capabilities in AI agents. Below is a Python code snippet illustrating how to implement memory management for multi-turn conversation handling:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent = AgentExecutor(
memory=memory,
error_message_template="An error occurred: {error_detail}. Please try again."
)
We integrated a vector database using Pinecone for efficient storage and retrieval of conversation contexts:
from pinecone import Index
index = Index("error-messaging-context")
# Storing context in the vector database
context_vector = {"chat_history": memory.get()}
index.upsert(items=[("context_id", context_vector)])
For error transparency, the agent's reasoning and tool calling patterns were logged, allowing developers to trace the decision-making process:
// Example of a tool calling pattern in JavaScript
async function handleErrorMessage(error) {
console.log("Error encountered:", error.message);
// Log reasoning and decision steps
await logAgentReasoning(error);
}
// Logging function
async function logAgentReasoning(error) {
// Detailed logging of the error context and resolution steps
console.log("Logging reasoning for:", error.message);
}
The architecture employed depicted a modular design, emphasizing agent orchestration patterns to allow for scalable error messaging solutions.
Implementation of Effective Error Messaging Agents
To implement effective error messaging in AI-powered applications, developers need to follow a structured approach incorporating best practices of clarity, context-awareness, and actionable advice. This section outlines the steps to integrate error messaging agents using modern AI frameworks and tools.
Steps to Implement Effective Error Messaging
- Define Error Scenarios: Start by identifying possible error conditions your application might encounter. Categorize these errors based on their severity and impact on user interaction.
- Design Context-Aware Messages: Utilize context from the application's state and user inputs to craft messages that explain not only what went wrong, but why. This requires access to the agent's internal state and decision-making process.
- Integrate with AI Frameworks: Use frameworks like LangChain or AutoGen to build error messaging logic. These frameworks provide tools for managing dialogue history and agent reasoning.
- Implement Memory and State Management: Use memory management to track conversation history and state, ensuring error messages are relevant to the current context.
- Provide Actionable Guidance: Ensure each error message includes clear steps the user can take to resolve the issue or retry the operation.
Tools and Platforms for Integration
To effectively implement error messaging agents, developers can leverage various tools and platforms:
- LangChain: A framework for building AI applications with memory and agent orchestration capabilities.
- Pinecone: A vector database for storing and retrieving contextual information that aids in generating context-aware error messages.
- MCP Protocol: Use MCP (Message Control Protocol) for structured messaging between components, ensuring clarity and consistency in error reporting.
Implementation Examples
Below are examples demonstrating key concepts in implementing error messaging agents:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
# Memory management for tracking conversation context
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Agent execution with error handling
agent_executor = AgentExecutor(
agent_name="ErrorHandlingAgent",
memory=memory
)
# Example error message generation
def generate_error_message(context, error):
return f"Error: {error}. Context: {context}. Please try rephrasing your request."
The architecture diagram (not shown here) would illustrate an AI agent interacting with a vector database (e.g., Pinecone) and using a memory buffer to manage conversation history, enabling the generation of context-aware error messages.
Advanced Implementation Patterns
For more complex applications, consider implementing multi-turn conversation handling and agent orchestration patterns. This ensures error messages are consistent across various interaction stages and maintain clarity and guidance.
Case Studies
The design and implementation of error messaging agents have evolved significantly, leveraging advanced frameworks and practices to enhance clarity, context-awareness, and actionable guidance. Here, we explore two real-world examples: one successful implementation and one illuminating failure, offering insights for developers aiming to harness these technologies.
Successful Implementation: E-commerce Chatbot
A prominent e-commerce platform integrated an error messaging agent using the LangChain framework. The goal was to improve customer support interactions by providing precise and context-aware error messages. A critical aspect was integrating a vector database for context retrieval, utilizing Pinecone.
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
from langchain.models import VectorSearch
from langchain.vectorstores import Pinecone
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
vector_search = VectorSearch(store=Pinecone(), index_name="error_messages")
agent = AgentExecutor.from_config(
agent_config={
"tools": ["ActionTool"],
"memory": memory,
"vector_search": vector_search
}
)
agent.run("What went wrong?")
This implementation allowed the agent to maintain conversation context, enhancing the clarity and specificity of error messages. The integration with Pinecone facilitated quick retrieval of relevant error scenarios, enabling more precise guidance.
Lessons Learned: Banking Assistant Failure
An AI-powered banking assistant attempted to utilize an error messaging agent with CrewAI and Chroma for context management. However, the implementation struggled with multi-turn conversation handling due to inadequate memory management and error propagation strategies, leading to customer dissatisfaction.
import { ConversationMemory, Agent } from 'crewai';
import { Chroma } from 'crewai/vectorstores';
const memory = new ConversationMemory({
memoryKey: 'session_data'
});
const chroma = new Chroma({ collection: 'banking_errors' });
const agent = new Agent({
tools: ['TransactionTool'],
memory,
vectorStore: chroma
});
agent.process('transaction failure').catch(error => {
console.error('Error processing transaction', error);
});
The primary lesson was the importance of robust memory management to handle complex, multi-turn interactions effectively. The team learned that clear error propagation and actionable advice could not be sacrificed for speed. Future iterations focused on detailed MCP protocol implementations and improved tool calling patterns, ensuring transparency and recoverability in agent interactions.
Conclusion
These case studies underscore the significance of employing advanced frameworks like LangChain and CrewAI for error messaging agents, alongside strategic vector database integrations with Pinecone or Chroma. By learning from both successes and failures, developers can enhance the effectiveness and user experience of AI-driven interfaces.
Metrics for Success
Measuring the success of error messaging agents is crucial to ensure efficient and user-friendly interactions. The following key performance indicators (KPIs) and measurement techniques will help developers optimize these systems, using both quantitative and qualitative metrics.
Quantitative Measures
- Error Resolution Rate: This KPI measures the percentage of errors successfully resolved by the agent. High resolution rates indicate effective error messaging.
- User Engagement Metrics: Track metrics such as error message click-through rates or time to resolution, providing insights into user interactions with error messages.
- Incident Frequency: Monitor the frequency of specific errors to identify recurring issues that require attention.
Qualitative Measures
- User Feedback: Collect user feedback on error messages to gauge clarity, usefulness, and emotional intelligence.
- Contextual Relevance: Ensure error messages provide context by detailing potential causes and actionable solutions, evaluated through user reviews and testing.
Implementation Examples
For developers implementing error messaging agents, using frameworks like LangChain and integrating with vector databases such as Pinecone or Weaviate can enhance functionality and user experience.
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
import pinecone
# Initialize Pinecone
pinecone.init(api_key='your-api-key', environment='us-west1-gcp')
# Set up conversation memory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Example agent executor setup
executor = AgentExecutor(
agent_name='ErrorHandlingAgent',
memory=memory
)
Integrate MCP protocol and memory management to enhance multi-turn conversation handling, ensuring error messages are contextually aware and provide actionable guidance.
// Example JavaScript implementation using LangChain
const { MemoryManager } = require('langchain');
const { vectorDBIntegration } = require('weaviate');
const memoryManager = new MemoryManager({
memoryKey: 'chatHistory',
retainMessages: true
});
// Integration with Weaviate for vector searches
vectorDBIntegration({
apiKey: 'your-api-key',
onSuccess: (result) => {
console.log('Integration Successful:', result);
},
onError: (error) => {
console.error('Error integrating with Weaviate:', error);
}
});
By leveraging these methodologies, developers can craft error messaging agents that not only offer clarity and context but also effectively guide users towards resolution, reinforcing the overall user experience.
Best Practices for Designing Error Messaging Agents
In the evolving landscape of 2025, error messaging agents play a crucial role in enhancing user experience by providing clear, context-aware, and actionable feedback. Here, we explore best practices to achieve this, focusing on clarity, context-awareness, transparency, actionable advice, and accessibility, particularly within agentic and AI-powered interfaces.
1. Context-awareness
Error messages should not be isolated from the agent's context and actions. Clearly explain what the agent was attempting and specify where the problem occurred. This transparency aids users in understanding the system's behavior and potential limitations.
from langchain.agents import AgentExecutor
from langchain.prompts import PromptTemplate
def error_handling_prompt(error):
return f"Agent attempted operation X but encountered error: {error}. Ensure input Y is correct."
agent = AgentExecutor(prompt_template=PromptTemplate(error_handling_prompt))
2. Clarity and Specificity
Use simple, precise language to inform users about what went wrong and suggest actionable next steps. Avoid jargon and vague descriptions to prevent user frustration.
function displayErrorMessage(error) {
const message = `Error: ${error.message}. Please verify your input and try again.`;
console.log(message);
}
3. Transparency
Make the agent's reasoning visible. When an error occurs, describe the steps taken by the agent leading up to the failure. This helps in setting user expectations and builds trust in the system.
interface AgentResponse {
success: boolean;
steps: string[];
error?: string;
}
const agentResponse: AgentResponse = {
success: false,
steps: ['Initialized context', 'Called API'],
error: 'Invalid API response'
};
if (!agentResponse.success) {
console.log(`Failed after steps: ${agentResponse.steps.join(', ')}, Error: ${agentResponse.error}`);
}
4. Actionable Advice
Every error message should guide users towards a resolution or next step. Suggestions like "Try rephrasing your request" or "Check your internet connection" not only inform but also empower users to resolve issues independently.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
def suggest_fix(error):
return f"Encountered {error}. Consider rephrasing or checking connection settings."
print(suggest_fix("network timeout"))
5. Accessibility
Ensure error messages are accessible to all users, including those with disabilities. Use simple language, sufficient contrast, and screen-reader compatible text.
function accessibleErrorMessage(error) {
const message = `Error: ${error.message}. Please check input and retry.`;
document.getElementById('error-output').innerText = message;
}
6. Multi-turn Conversation Handling
Incorporate multi-turn conversation handling to maintain context across user interactions, ensuring the agent can provide relevant and informed error messages.
from langchain.vectorstores import Pinecone
vector_store = Pinecone(index_name="chat_history")
By integrating these best practices into the design of error messaging agents, developers can significantly improve the usability and reliability of AI-powered interfaces, ultimately leading to a more satisfying user experience.
Advanced Techniques
Designing effective error messaging agents in 2025 requires a combination of emotional intelligence, standardized API formats, and advanced technical implementations. This section explores these techniques to enhance error messaging, with a focus on practical examples and comprehensive code snippets.
Emotional Intelligence in Messaging
Integrating emotional intelligence into error messages helps foster a user-friendly interaction environment. An empathetic tone ensures users receive not only technical guidance but also a more human-centric experience. This can be implemented using AI frameworks like LangChain to manage conversational context and tone.
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent = AgentExecutor(memory=memory)
By storing conversation history, LangChain provides context-aware responses that can adjust based on the mood detected through user interactions. This technique is crucial for maintaining coherence in multi-turn conversations and delivering empathetic error messages.
Standardized Formats for APIs
Implementing standardized formats ensures consistent error messaging across different interfaces. Using schemas and tool-calling patterns facilitates seamless integration of error messaging agents into various systems. Below is an example of an API error response format using a JSON schema:
const errorResponseSchema = {
type: "object",
properties: {
errorCode: { type: "string" },
message: { type: "string" },
resolution: { type: "string" },
},
required: ["errorCode", "message"]
};
This schema standardizes the way errors are reported, ensuring clarity and actionable guidance across all API endpoints.
Integration with Vector Databases
To enhance agent capabilities, integrating with vector databases like Pinecone or Weaviate can significantly improve the context-awareness of error messages. By storing interaction vectors, agents can reference past interactions to provide more relevant error resolutions.
from pinecone import Client
client = Client(api_key="your-api-key")
index = client.Index("error-messaging-index")
# Example of storing and retrieving context
vector = [0.23, 0.45, 0.78]
index.upsert([(user_id, vector)])
# Retrieve context to refine error messages
response = index.query(vector, top_k=1)
MCP Protocol Implementation
The Message Control Protocol (MCP) allows for precise management of message exchanges, crucial for debugging and error resolution. Implementing MCP ensures that error messages are delivered with the correct protocol metadata.
interface MCPMessage {
id: string;
type: string;
payload: any;
}
function handleError(message: MCPMessage) {
if (message.type === 'error') {
console.error(`Error ID: ${message.id}, Details: ${message.payload}`);
}
}
Memory and Agent Orchestration
Managing agent memory and orchestrating complex interactions are critical for delivering precise and contextually aware error messages. Using frameworks like CrewAI, developers can distribute memory workloads across agents, ensuring coherent and continuous user experiences.
from crewai.memory import DistributedMemory
distributed_memory = DistributedMemory()
# Store and retrieve memory to manage error context
distributed_memory.store("session_id", "error_context", {"state": "in_progress"})
context = distributed_memory.retrieve("session_id", "error_context")
These advanced techniques establish a robust foundation for developing error messaging agents that are not only technically proficient but also emotionally intelligent and user-friendly.
Future Outlook for Error Messaging Agents
The landscape of error messaging agents is poised for transformative advancements, driven by emerging trends and technologies. Future developments in this field will likely focus on enhancing clarity, context-awareness, and emotional intelligence. These aspects are crucial for creating agentic interfaces that can handle complex multi-turn dialogues effectively.
Emerging Trends and Technologies
In 2025, the integration of frameworks like LangChain and CrewAI will streamline the development of error messaging agents. These frameworks enable developers to create context-aware systems that offer precise and actionable guidance. For instance, LangChain's AgentExecutor
can manage error messaging within dynamic task flows:
from langchain.agents import AgentExecutor
from langchain.memory import ConversationBufferMemory
from langchain.tools import Tool
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent = AgentExecutor.from_tools(
tools=[Tool('error_handler')],
memory=memory
)
Additionally, vector databases like Pinecone facilitate error pattern recognition and adaptive response generation. This helps in providing more precise error messages by learning from historical data.
The Future of Error Messaging Agents
The future will see error messaging agents evolve into sophisticated systems capable of orchestrating multiple agent roles. By implementing the MCP protocol, developers can create seamless multi-agent interactions:
import { MCPAgent } from 'crewai'
import { MemoryManager } from 'crewai/memory'
const agent = new MCPAgent()
const memoryManager = new MemoryManager(agent)
agent.on('error', (error) => {
memoryManager.store(error)
console.log('Error processed with context:', error.context)
})
Tool calling patterns will also mature, allowing agents to autonomously select the most relevant tools. This creates more transparent and user-friendly experiences:
interface ToolCallSchema {
name: string
params: { [key: string]: any }
}
function callTool(tool: ToolCallSchema) {
// Logic to call the appropriate tool
}
callTool({ name: 'NetworkChecker', params: { retry: 2 } })
In conclusion, the future of error messaging agents is bright, with advancements in AI, memory management, and multi-turn conversation handling paving the way for more effective and empathetic user interactions.
Conclusion
In wrapping up our exploration of error messaging agents, it's evident that the landscape for these systems in 2025 prioritizes a balance between technical sophistication and user accessibility. Key insights highlight the crucial components of clarity, context-awareness, and actionable guidance.
One of the major takeaways is the importance of context-awareness. For an error message to be truly effective, it must not only convey what went wrong but also offer insights into why the issue occurred. This involves detailing the agent's actions and the specific juncture where the failure happened, which enhances user understanding and trust.
Clarity and specificity remain foundational to effective communication. Developers are encouraged to use precise language and avoid jargon, providing users with clear, actionable steps to resolve issues. Coupling this with transparency, agents should clearly articulate their decision-making processes and limitations.
For developers, integrating frameworks like LangChain and CrewAI can significantly enhance the creation of intelligent, context-aware error messaging agents. The following Python code snippet illustrates how memory management and tool calling patterns can be implemented using LangChain:
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
agent_executor = AgentExecutor(
memory=memory,
agent_orchestrator=AgentOrchestrator(),
tools=[Tool(name="syntax_checker", function=check_syntax)]
)
Moreover, integrating vector databases like Pinecone or Weaviate can support agents in maintaining contextual awareness and improving response accuracy. The architectural design for such integrations often involves a flow where the agent queries the database to retrieve context-specific data before crafting an error response.
Ultimately, by adhering to these best practices and leveraging modern frameworks, developers can build error messaging agents that not only inform but also assist users in navigating technological complexities effectively.
Frequently Asked Questions
- What are error messaging agents?
- Error messaging agents are AI-driven systems designed to interpret and convey error messages effectively. They aim to provide context, clarity, and actionable guidance to help developers and end-users manage and resolve issues.
- How can I implement an error messaging agent using LangChain?
-
You can use LangChain to build an error messaging agent with memory management and multi-turn conversation handling. Here's a basic example:
This setup utilizes LangChain’s memory capabilities to manage chat history and provide context-aware error responses.from langchain.memory import ConversationBufferMemory from langchain.agents import AgentExecutor memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) agent = AgentExecutor(memory=memory)
- How can I integrate a vector database like Pinecone?
-
Integrating a vector database helps in storing and retrieving embeddings for improved error context. Here's a pattern using Pinecone:
This allows for efficient semantic search and retrieval of context-relevant error messages.import pinecone # Initialize Pinecone pinecone.init(api_key="your-api-key") index = pinecone.Index("error-messages") # Store and query vectors index.upsert([("id1", vector)]) retrieved = index.query(vector, top_k=5)
- What is the MCP protocol and how is it implemented?
-
The MCP (Message Control Protocol) facilitates structured communication between agents. Here's a simplified implementation snippet:
This setup helps in managing message flow and processing errors effectively.const MCP = require('mcp-protocol'); const client = new MCP.Client(); client.on('error', (error) => { console.log('Error:', error.message); });
- What are some best practices for designing error messages?
- Best practices include maintaining context-awareness, ensuring clarity, providing transparency about the agent's reasoning, and offering actionable advice. This helps users understand what went wrong and how they can fix it.