AI Agent Systems Manager @ 99Ravens specializing in context engineering for production agent systems. Comprehensive educational collection covering context fundamentals, optimization strategies, memory architectures, multi-agent patterns, and tool design principles.
Design and evaluate context compression strategies for managing long-running agent sessions and conversation history
Recognize and mitigate performance degradation patterns in language models as context length increases
Understand and effectively manage the complete state of information available to language models during inference
Extend effective context capacity through strategic compression, masking, caching, and partitioning techniques
Build evaluation frameworks for agent systems that measure performance through multi-dimensional rubrics and continuous testing
Design and implement memory architectures that enable agents to persist state across sessions and reason over accumulated knowledge
Design multi-agent systems that distribute work across multiple language models to overcome single-agent context limitations
Design tools that agents can use effectively by following principles that account for how language models perceive and utilize tools