Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents

Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan·November 05, 2024

Summary

The paper introduces cognitive observability as a new type of observability for complex software systems, particularly agentic software like AutoCodeRover, which uses foundation models for autonomous program improvement. It proposes a novel framework, Watson, to observe reasoning paths, focusing on operational and cognitive aspects. The text discusses methods for ensuring alignment and reasoning consistency in AI agents, particularly in the context of AutoCodeRover. It also addresses issues in the Django web framework, including equality comparison, model initialization, and subclassing problems. The study highlights the importance of reasoning paths in Agentware systems for identifying and addressing issues, improving decision-making, and fostering continuous system improvement.

Key findings

5

Tables

1

Background
Overview of complex software systems
Explanation of agentic software systems
Importance of observability in software systems
Introduction to cognitive observability
Definition and differentiation from traditional observability
Relevance to complex systems like AutoCodeRover
Objective
Research focus
The need for cognitive observability in agentic software
The role of AutoCodeRover in autonomous program improvement
Framework development
Introduction to the Watson framework
Objectives of the Watson framework in observing reasoning paths
Method
Data Collection
Methods for collecting data on reasoning paths
Data sources for AutoCodeRover and similar systems
Data Preprocessing
Techniques for preparing data for analysis
Handling issues in Django web framework data
Ensuring Alignment and Reasoning Consistency
Challenges in AI agents
Issues with equality comparison in AI systems
Model initialization and subclassing problems
Strategies for alignment and consistency
Approaches to ensure coherent reasoning in AI agents
Importance of Reasoning Paths in Agentware Systems
Identifying and addressing issues
Role of reasoning paths in detecting system anomalies
Improving decision-making
Utilization of reasoning paths for better AI decisions
Continuous system improvement
The impact of reasoning paths on evolving software systems
Conclusion
Summary of findings
Recap of the importance of cognitive observability
Future directions
Potential areas for further research
Implications for the development of Agentware systems
Basic info
papers
software engineering
artificial intelligence
Advanced features