Webinar: Introduction to Debugging Agents with Okareo
Video
Matt Wyman
,
CEO / Co-Founder
January 24, 2025
Agents are everywhere. But what really is an agent and how do I approach building one that works? This webinar is an introductory dive into the weeds agentic patterns, debugging, common pitfalls, and best practices for debugging AI agents. There is even a case study of a small but realistic survey agent learning about dogs.
Understanding Agentic Patterns
This webinar discusses four common agentic patterns that dictate their behavior and decision-making processes. Understanding these patterns is crucial for debugging:
Reflection: Encourages agents to analyze past actions to improve future decisions. Debugging here requires the ability to clearly articulate measurable goals for analysis to ensure meaningful insights.
Tool Use: Includes selecting, calling, and responding to APIs or local functions/methods. Poor documentation of capabilities and parameters commonly leads to failure in tool selection and in appropriate response to results.
Planning: Encompasses generating tasks, creating plans, and executing them. Failures often stem from a lack of coordination during development and a lack of structure feedback in production with subject matter experts (SMEs).
Multi-Agent Coordination: Involves agents working together to achieve complex goals. Success requires clarity on objectives, success criteria, resources, and constraints.
Debugging: A Dual Approach
Effective debugging involves both offline and online methods:
Offline Debugging: Simulations and instrumented harnesses allow developers to test behaviors under controlled conditions. This is particularly useful for identifying patterns and simulating attacks.
Online Debugging: Runtime evaluations capture real-world behavior, using metrics and traces for analysis. This approach is ideal for detecting outliers and monitoring performance or releases, particularly canaries.
Connecting online and offline debugging creates the opportunity to dynamically build drift and anomaly detection without significant upfront work. Furthermore, using synthetic data based on production behavior enables ongoing evaluation of simulated conditions for future revisions.
Case Study: The Survey Agent
This webinar uses a chat agent designed to collect survey data - in this case with the top of "Dog". The agent's goals include:
Establishing a voice for the conversation based on the topic.
Collecting three facts from users on the topic.
Deflecting unwanted conversation.
Closing the conversation when the plan is complete
Avoiding unauthorized data sharing.
The debugging process revealed several challenges:
Planning Failures
Ambiguous Answers
Infinite Questioning
Data Leaks
Key Takeaways
The art of debugging AI agents lies in combining technical expertise with a structured approach:
Think like a Scientist: Adopt a hypothesis-driven approach to debugging, iterating based on data and observations.
Build like a Developer: Focus on robust architecture and clear design principles that provide flexibility - e.g. tools.
Feedback like an SME: Collaborate with domain experts to align agent behavior with business goals.
Conclusion
Debugging AI agents is both a science and an art. By leveraging agentic patterns and a combination of offline and online evaluations, developers can create a lifecycle of improvement where production usage and alerting links to simulation, experimentation and improved risk levels. As agents continue to revolutionize industries, mastering the art of debugging ensures these powerful tools are built reliably and accountably.
Agents are everywhere. But what really is an agent and how do I approach building one that works? This webinar is an introductory dive into the weeds agentic patterns, debugging, common pitfalls, and best practices for debugging AI agents. There is even a case study of a small but realistic survey agent learning about dogs.
Understanding Agentic Patterns
This webinar discusses four common agentic patterns that dictate their behavior and decision-making processes. Understanding these patterns is crucial for debugging:
Reflection: Encourages agents to analyze past actions to improve future decisions. Debugging here requires the ability to clearly articulate measurable goals for analysis to ensure meaningful insights.
Tool Use: Includes selecting, calling, and responding to APIs or local functions/methods. Poor documentation of capabilities and parameters commonly leads to failure in tool selection and in appropriate response to results.
Planning: Encompasses generating tasks, creating plans, and executing them. Failures often stem from a lack of coordination during development and a lack of structure feedback in production with subject matter experts (SMEs).
Multi-Agent Coordination: Involves agents working together to achieve complex goals. Success requires clarity on objectives, success criteria, resources, and constraints.
Debugging: A Dual Approach
Effective debugging involves both offline and online methods:
Offline Debugging: Simulations and instrumented harnesses allow developers to test behaviors under controlled conditions. This is particularly useful for identifying patterns and simulating attacks.
Online Debugging: Runtime evaluations capture real-world behavior, using metrics and traces for analysis. This approach is ideal for detecting outliers and monitoring performance or releases, particularly canaries.
Connecting online and offline debugging creates the opportunity to dynamically build drift and anomaly detection without significant upfront work. Furthermore, using synthetic data based on production behavior enables ongoing evaluation of simulated conditions for future revisions.
Case Study: The Survey Agent
This webinar uses a chat agent designed to collect survey data - in this case with the top of "Dog". The agent's goals include:
Establishing a voice for the conversation based on the topic.
Collecting three facts from users on the topic.
Deflecting unwanted conversation.
Closing the conversation when the plan is complete
Avoiding unauthorized data sharing.
The debugging process revealed several challenges:
Planning Failures
Ambiguous Answers
Infinite Questioning
Data Leaks
Key Takeaways
The art of debugging AI agents lies in combining technical expertise with a structured approach:
Think like a Scientist: Adopt a hypothesis-driven approach to debugging, iterating based on data and observations.
Build like a Developer: Focus on robust architecture and clear design principles that provide flexibility - e.g. tools.
Feedback like an SME: Collaborate with domain experts to align agent behavior with business goals.
Conclusion
Debugging AI agents is both a science and an art. By leveraging agentic patterns and a combination of offline and online evaluations, developers can create a lifecycle of improvement where production usage and alerting links to simulation, experimentation and improved risk levels. As agents continue to revolutionize industries, mastering the art of debugging ensures these powerful tools are built reliably and accountably.
Agents are everywhere. But what really is an agent and how do I approach building one that works? This webinar is an introductory dive into the weeds agentic patterns, debugging, common pitfalls, and best practices for debugging AI agents. There is even a case study of a small but realistic survey agent learning about dogs.
Understanding Agentic Patterns
This webinar discusses four common agentic patterns that dictate their behavior and decision-making processes. Understanding these patterns is crucial for debugging:
Reflection: Encourages agents to analyze past actions to improve future decisions. Debugging here requires the ability to clearly articulate measurable goals for analysis to ensure meaningful insights.
Tool Use: Includes selecting, calling, and responding to APIs or local functions/methods. Poor documentation of capabilities and parameters commonly leads to failure in tool selection and in appropriate response to results.
Planning: Encompasses generating tasks, creating plans, and executing them. Failures often stem from a lack of coordination during development and a lack of structure feedback in production with subject matter experts (SMEs).
Multi-Agent Coordination: Involves agents working together to achieve complex goals. Success requires clarity on objectives, success criteria, resources, and constraints.
Debugging: A Dual Approach
Effective debugging involves both offline and online methods:
Offline Debugging: Simulations and instrumented harnesses allow developers to test behaviors under controlled conditions. This is particularly useful for identifying patterns and simulating attacks.
Online Debugging: Runtime evaluations capture real-world behavior, using metrics and traces for analysis. This approach is ideal for detecting outliers and monitoring performance or releases, particularly canaries.
Connecting online and offline debugging creates the opportunity to dynamically build drift and anomaly detection without significant upfront work. Furthermore, using synthetic data based on production behavior enables ongoing evaluation of simulated conditions for future revisions.
Case Study: The Survey Agent
This webinar uses a chat agent designed to collect survey data - in this case with the top of "Dog". The agent's goals include:
Establishing a voice for the conversation based on the topic.
Collecting three facts from users on the topic.
Deflecting unwanted conversation.
Closing the conversation when the plan is complete
Avoiding unauthorized data sharing.
The debugging process revealed several challenges:
Planning Failures
Ambiguous Answers
Infinite Questioning
Data Leaks
Key Takeaways
The art of debugging AI agents lies in combining technical expertise with a structured approach:
Think like a Scientist: Adopt a hypothesis-driven approach to debugging, iterating based on data and observations.
Build like a Developer: Focus on robust architecture and clear design principles that provide flexibility - e.g. tools.
Feedback like an SME: Collaborate with domain experts to align agent behavior with business goals.
Conclusion
Debugging AI agents is both a science and an art. By leveraging agentic patterns and a combination of offline and online evaluations, developers can create a lifecycle of improvement where production usage and alerting links to simulation, experimentation and improved risk levels. As agents continue to revolutionize industries, mastering the art of debugging ensures these powerful tools are built reliably and accountably.
Agents are everywhere. But what really is an agent and how do I approach building one that works? This webinar is an introductory dive into the weeds agentic patterns, debugging, common pitfalls, and best practices for debugging AI agents. There is even a case study of a small but realistic survey agent learning about dogs.
Understanding Agentic Patterns
This webinar discusses four common agentic patterns that dictate their behavior and decision-making processes. Understanding these patterns is crucial for debugging:
Reflection: Encourages agents to analyze past actions to improve future decisions. Debugging here requires the ability to clearly articulate measurable goals for analysis to ensure meaningful insights.
Tool Use: Includes selecting, calling, and responding to APIs or local functions/methods. Poor documentation of capabilities and parameters commonly leads to failure in tool selection and in appropriate response to results.
Planning: Encompasses generating tasks, creating plans, and executing them. Failures often stem from a lack of coordination during development and a lack of structure feedback in production with subject matter experts (SMEs).
Multi-Agent Coordination: Involves agents working together to achieve complex goals. Success requires clarity on objectives, success criteria, resources, and constraints.
Debugging: A Dual Approach
Effective debugging involves both offline and online methods:
Offline Debugging: Simulations and instrumented harnesses allow developers to test behaviors under controlled conditions. This is particularly useful for identifying patterns and simulating attacks.
Online Debugging: Runtime evaluations capture real-world behavior, using metrics and traces for analysis. This approach is ideal for detecting outliers and monitoring performance or releases, particularly canaries.
Connecting online and offline debugging creates the opportunity to dynamically build drift and anomaly detection without significant upfront work. Furthermore, using synthetic data based on production behavior enables ongoing evaluation of simulated conditions for future revisions.
Case Study: The Survey Agent
This webinar uses a chat agent designed to collect survey data - in this case with the top of "Dog". The agent's goals include:
Establishing a voice for the conversation based on the topic.
Collecting three facts from users on the topic.
Deflecting unwanted conversation.
Closing the conversation when the plan is complete
Avoiding unauthorized data sharing.
The debugging process revealed several challenges:
Planning Failures
Ambiguous Answers
Infinite Questioning
Data Leaks
Key Takeaways
The art of debugging AI agents lies in combining technical expertise with a structured approach:
Think like a Scientist: Adopt a hypothesis-driven approach to debugging, iterating based on data and observations.
Build like a Developer: Focus on robust architecture and clear design principles that provide flexibility - e.g. tools.
Feedback like an SME: Collaborate with domain experts to align agent behavior with business goals.
Conclusion
Debugging AI agents is both a science and an art. By leveraging agentic patterns and a combination of offline and online evaluations, developers can create a lifecycle of improvement where production usage and alerting links to simulation, experimentation and improved risk levels. As agents continue to revolutionize industries, mastering the art of debugging ensures these powerful tools are built reliably and accountably.
