As AI systems become more advanced, they are now designed to perform complex tasks with multiple layers of decision-making. These systems, often called agentic AI, involve hierarchies of smaller units called agents and sub-agents that work together to accomplish goals. This shift from simple chatbots to more autonomous, tool-using agents introduces new demands on computing power and system design.

Understanding How Agents Consume Resources

Unlike traditional chatbots that respond to user inputs in a predictable way, agentic AI systems can call various tools, spawn sub-agents, and manage their own memory. This flexibility means they process much larger amounts of data, often involving thousands or even hundreds of thousands of tokens in a single session. For example, some real-world applications like Claude Code handle over 150,000 tokens per context window, which is far beyond the scope of standard models.

This high token consumption is driven by the need to keep track of complex interactions, tool outputs, and internal states. As agents call tools such as calculators or data retrieval systems, they add new information directly into their context, making the input sequences unpredictable. Managing this effectively requires advanced techniques like prompt caching and context compaction, which help reduce costs and latency.

The Economics of Running Large-Scale Agentic AI

Traditional AI serving systems are built around predictable, linear interactions. However, as systems become more agentic, their resource needs grow exponentially. The unpredictability of tool calls and multi-step reasoning causes token consumption to spike, which can drive up operating costs and slow down response times.

To address these challenges, NVIDIA has developed specialized hardware and software solutions. The Vera Rubin platform, for instance, combines multiple chips—like NVL72, Vera CPU, Groq LPX, and others—working together to handle the enormous throughput required by trillion-parameter models. Technologies like Dynamo, NVFP4, and TensorRT WideEP optimize for both speed and efficiency, enabling low-latency inference even with massive context windows of up to 400,000 tokens.

This extreme co-design approach ensures that large, complex models can run economically at scale, making autonomous AI systems more practical for real-world use cases. The goal is to balance throughput and latency, ensuring systems can respond quickly without incurring prohibitive costs.

Designing Infrastructure for Future AI Agents

As AI systems transition from simple chatbots to fully autonomous agents, their architecture must evolve. Traditional models are linear, predictable, and easy to scale. But agentic systems require a more sophisticated setup that can handle dynamic task chaining, tool integration, and memory management.

Modern architectures feature primary agents that oversee entire tasks, delegating subtasks to sub-agents that operate within smaller context windows. These sub-agents can self-manage their memory and even write information to files for later retrieval. Summarization and context compression techniques help keep input sizes manageable, preventing context rot—where the quality of output degrades as the conversation or task grows longer.

This layered approach allows systems to handle more complex workflows efficiently. By leveraging smaller context windows and parallel processing, these architectures reduce processing costs and improve response quality. They also create a more resilient foundation for future AI applications that require higher levels of autonomy and reasoning.

Overall, the evolution of agentic AI demands a rethink of hardware and software design. The integration of specialized chips, optimized algorithms, and memory management techniques is key to building scalable, efficient systems that can support the rising complexity of autonomous AI agents.

Inspired by

• https://developer.nvidia.com/blog/building-for-the-rising-complexity-of-agentic-systems-with-extreme-co-design/

Sources

• trychroma.com
• anthropic.com