CMU AI Agents 2026: 3. Long Context Modeling for Agents

Context management for long-context agents requires balancing computational efficiency with model accuracy as interaction histories grow quadratically. State-of-the-art architectures utilize hybrid approaches, combining local windowed attention with global sparse or dense mechanisms to handle million-token sequences. Linear attention variants, such as Gated DeltaNet, further optimize performance by updating state matrices in constant time. Key-value (KV) caching remains essential for reducing inference costs, though maintaining high cache hit rates requires avoiding dynamic inputs that invalidate prefixes. While context compaction—summarizing past interactions—mitigates memory constraints, it risks critical information loss, necessitating robust, tested strategies to prevent agent failure. Effective implementation relies on optimizing these architectural and caching techniques while ensuring that compaction processes preserve essential task instructions and state consistency.