What is an AI Agent?
Unlike traditional AI that simply responds to prompts, an agentic AI is a system that can proactively perceive its environment, make plans over multiple steps, and execute actions using tools to achieve a given goal. This creates a continuous cycle of operation, allowing for a much higher degree of autonomy.
Gathers information
Creates a strategy
Uses tools to execute
The Six Core Challenges
While the concept is powerful, making agents reliable is incredibly difficult. Below are the primary areas of active research and development. Click on a card to learn more.
Select a challenge above
Details about the selected challenge will appear here. This includes a breakdown of why it's a difficult problem and the common failure points researchers are trying to solve.
Challenge Landscape
Not all challenges are equal. This visualization compares the estimated difficulty of solving each problem against the current rate of research progress.
The Path Forward
Solving these challenges is the key to unlocking the next wave of AI capabilities. Progress requires a multi-faceted approach, focusing on foundational model improvements, better agent architectures, and robust evaluation.
Smarter Models
Improving the core reasoning, planning, and code generation abilities of the underlying Large Language Models (LLMs).
Better Architectures
Designing agent frameworks that can self-correct, manage memory more effectively, and learn from past mistakes.
Robust Evaluation
Creating challenging, real-world benchmarks that can accurately measure agent capabilities and expose their weaknesses.