Enterprises are moving rapidly toward the vision of AI as an autonomous workforce. However, scaling from a simple single-agent demo to a collaborative swarm of millions hits a critical infrastructure bottleneck.
Anat Heilper and Ofir Zan examine some of the issues that make it difficult to deploy AI agents at scale, including:
1️⃣ The Coupled Scaling Problem: General-purpose infrastructure doesn't natively understand agent-specific resource patterns. An agent's compute needs (LLM inference) and memory needs (vector stores, conversation history) scale differently. You end up either over-provisioning or constantly reconfiguring infrastructure to match workload patterns.
2️⃣ The State and Amnesia Problem: Agents need durable, accessible state across invocations—conversation history, tool outputs, intermediate reasoning. Standard platforms offer generic databases or object storage, but lack agent-native state management. Developers must cobble together solutions from generic components, leading to fragility.
3️⃣ The Communication Bottleneck: Coordinating thousands of agents requires high-throughput, asynchronous, observable communication patterns. While message queues and event brokers exist, they're not agent-aware—they don't understand task delegation, result aggregation, or semantic routing.
4️⃣ The Security and Identity Silo: In a monolithic setup, each agent is an island. It has to manage its own secrets and API keys. The platform provides no central, secure identity or tool execution service, creating thousands of individual security risks instead of one manageable one.
Read Anat and Ofir’s full analysis to understand why these aren't agent failures but platform gaps. The VAST AI Operating System is purpose-built to solve these platform gaps, providing the shared, decoupled foundation necessary for running AI agents at scale and in production 👇