Compute Economics in Agentic Inference
Optimizing token context compression and speculative routing for low-latency agent architectures.
Agent loops consume orders of magnitude more tokens than conversational queries. Scaling autonomous systems to production requires aggressive inference optimization and compute budgeting.
Context Pruning Strategies
Rather than passing the entire history to every downstream LLM call, selective context summarization and observation pruning yield substantial latency improvements without compromising task success rates.
We are currently benchmarking tiered model routing, where lightweight models handle structured formatting and validation steps, reserving frontier models exclusively for high-ambiguity planning and verification phases.