Attention-Optimized Context Engineering
Hacking Transformer Attention for Agentic Systems
Context & Problem
Transformer attention is not uniform—models exhibit systematic biases in how they weight information across context windows. Agentic systems operating over long contexts lose critical information to attention dilution, reducing reasoning quality and task success rates.
Solution & Architecture
Developed attention hacking strategies that analyze and exploit attention distribution patterns. By strategically positioning high-value context, using attention anchors, and engineering context structure to align with natural attention flows, we dramatically improve how agentic systems utilize their context windows.
Key Components
- Multi-layer architecture with clear separation of concerns
- Integration with enterprise systems and data sources
- Scalable infrastructure designed for high availability
- Security and governance built into the core design
Impact
Measurable improvements in agentic task completion rates through optimized context utilization. Systems now maintain coherent reasoning over significantly longer interaction chains without the context degradation that typically occurs.
What's Next
- Model-specific attention profiling and optimization
- Dynamic context restructuring based on task phase
- Attention-aware memory architectures for persistent agents