World's Fair 2026
Why Your Agent Disagrees With Itself (And What To Do About It) - Diane Lin, Datadog
Overview
This talk addresses the common issue of AI agents exhibiting inconsistent behavior, often disagreeing with their own previous outputs or decisions. It explores the underlying causes of this self-disagreement and provides practical strategies for engineers to mitigate these problems, leading to more reliable and predictable agent performance. The core thesis is that understanding and managing agent internal state and decision-making processes is crucial for building robust AI systems.
Who should watch
- AI Engineers
- Product Managers
- Developers building agentic systems
- Anyone experiencing unpredictable AI agent behavior
- Builders focused on agent reliability and quality
Key takeaways
- Agent self-disagreement stems from various factors including prompt sensitivity, internal state changes, and the stochastic nature of LLMs.
- Techniques like structured prompting, explicit state management, and output validation can help enforce consistency.
- Implementing robust evaluation frameworks is essential to identify and measure agent disagreement.
- Consider agent reasoning chains and decision-making processes to debug inconsistencies.
- Tools and libraries that facilitate agent development often include features to manage state and improve reliability.
- The development of agent skills and their integration into workflows can be a source of disagreement if not managed carefully.
Notable quotes
Diane Lin discusses how agents can sometimes contradict their own prior reasoning.
The talk offers methods to improve agent consistency and reduce self-disagreement.
Unofficial community note. Prefer the recording for nuance.