We show how design choices alone can make AI agents 100× slower, less accurate, or fail at coordination - even with the same LLM.
Measured across multiple frameworks • Same model, same task • Controlled experiments
Note: All numerical values on this page should be verified against the paper PDF for the most accurate and up-to-date results.
How agents are scheduled can create 100× latency differences
Memory structure matters more than context size alone
Rigid planning interfaces can break reasoning accuracy
Procedural specialization design can improve F1 scores by 58 points
Communication topology decides coordination success
Same LLM model • Same task
Different framework architectures
Direct LLM Call
Baseline: 1× latency
Multi-Agent Framework
Best: 1.3× latency
(efficient architecture)
Multi-Agent Framework
Worst: 117× latency
(poor architecture)
Just due to design. Same model, same task, wildly different performance. Measured across graph-based, role-based, and GABM-style frameworks.
How information flows between agents determines what they remember.
Planning accuracy can drop by 30% with poorly designed architectural choices.
Success rates can drop from 90% to below 30% based on how agents communicate.
100× latency means 100× API costs. Architecture choices directly affect your infrastructure spend.
Choose the wrong framework architecture and you'll spend months optimizing instead of building features.
Architectural bottlenecks become impossible to fix at scale. Get it right from the start.
Learn the architectural principles that make or break AI agent performance.