Multi-Agent AI Latency Is Breaking Real-World Systems | Jon Alexander, Akamai

Jon Alexander, SVP of Product at Akamai's Cloud Technology Group, explains why centralized infrastructure compounds latency across multi-agent AI loops and why 10 seconds can mean total failure for physical AI systems. Full article and transcript: https://tfir.io/multi-agent-ai-fails-... Multi-agent AI systems are not chatbots. They call tools, access file systems, execute code, and coordinate across models running on different clouds and AI labs. Every extra hop adds latency, and in a loop with 100 iterations, small delays stack into seconds. Akamai's Jon Alexander breaks down the infrastructure disconnect facing enterprises today. In this exclusive interview with Swapnil Bhartiya at TFiR, Alexander explains why forcing distributed agent workloads into a single central location is the wrong architecture and what the latency math actually looks like at scale. Key Topics Covered: Why multi-step, multi-agent systems expose the limits of centralized infrastructure How per-iteration latency compounds exponentially across agent loops The difference between human latency tolerance (chatbots) and physical AI requirements (robotics, autonomous vehicles) Why tool access, code execution, and cross-model coordination demand distributed coordination Real-world failure scenarios where 10-second end-to-end latency is unacceptable #AIInfrastructure #MultiAgentAI #EdgeComputing