This case study explores the development and deployment of an LLM-powered agent that plays Pokemon, created at Anthropic. The project serves as both an engaging public demonstration of LLM capabilities and a valuable internal tool for evaluating model improvements and understanding agent behavior in long-running tasks. The system was developed by David Hershey at Anthropic, initially as a side project in June 2022, but evolved into a significant demonstration of LLM agent capabilities. The project arose from a desire to experiment with agent frameworks while working with Anthropic's customers who were building their own agents. Pokemon was chosen as the domain both for its inherent entertainment value and its potential as a structured environment for testing model capabilities. Technical Implementation: The agent framework is intentionally simple, focusing on three core tools: * Button press functionality (A, B, Start, Select, directional inputs) * A knowledge base system that allows the model to store and update information over long time periods * A navigation system that enables the model to specify screen locations it wants to move to The system processes information by: * Taking screenshots after each button press * Receiving game state information like current location * Maintaining a running context through summarization and knowledge base updates One of the key technical challenges addressed was managing context over long time periods. With over 16,000 actions taken over multiple days, the system needed a way to maintain relevant information without exceeding the model's context window. This was accomplished through a knowledge base system that allows the model to store and update important information, combined with periodic summarization of recent actions. Model Evolution and Performance: The project has tested multiple versions of Claude, showing clear progression in capabilities: * Claude 3.5 (June 2023): Could get out of the house and meander around * Claude 3.6 and new 3.5 (October 2023): Achieved getting starter Pokemon and some basic progress * Latest version: Successfully beating gym leaders and making meaningful progress The progression in model capabilities is particularly interesting as it demonstrates improvements in: * Long-horizon decision making * Ability to learn from experience * Maintaining coherence over extended periods * Processing and acting on visual information LLMOps Insights: The project provides several valuable insights for LLMOps practitioners: * The importance of simple, focused tools over complex frameworks * The value of iterative testing across model versions * The benefit of having concrete, measurable objectives (like beating gym leaders) for evaluating model performance * The challenge of maintaining context and state over long-running tasks The case study also highlights broader lessons about evaluating LLM capabilities. While many standard evaluations focus on single-turn interactions, Pokemon provides a framework for assessing more complex behaviors like: * Learning and adaptation over time * Strategic decision-making * Memory and context management * Task persistence and recovery from failures One particularly valuable insight is how seemingly small improvements in model capabilities can lead to qualitatively different behaviors. The latest model version didn't just perform incrementally better - it crossed a threshold where it could successfully complete complex sequences of actions that previous versions couldn't manage at all. The project also demonstrates the value of public demonstrations in LLMOps. Despite being a game, the Pokemon agent provides a tangible, engaging way to show how LLMs can handle complex, sequential decision-making tasks. This has helped bridge the gap between technical capabilities and public understanding of what LLMs can do. Production Considerations: The system has been running continuously for extended periods, handling thousands of interactions while maintaining performance. This required careful attention to: * Robust error handling * Efficient context management * Clean tool interfaces * Effective prompt design The project also exemplifies the iterative nature of LLMOps development, with each model version requiring adjustments and optimizations. Interestingly, newer models often required less explicit instruction in prompts, as their improved capabilities made many previous guardrails unnecessary. This case study provides valuable insights into both the technical and practical aspects of deploying LLM agents in production environments, while also serving as an engaging public demonstration of advancing AI capabilities.