How Amazon is building AI that takes action in the real world

Key takeaways Amazon Nova Act trains model capabilities, orchestration logic, and tool controls as one integrated system. AWS AI infrastructure makes it possible to build and run reliable agents securely at scale. Amazon uses simulated “gym” environments to train agents before real-world deployment.

AI agents represent a fundamental shift in how artificial intelligence works. Unlike chatbots that respond to isolated prompts or generative AI that creates content on demand, agentic AI systems are software that can reason, plan, and act to accomplish goals with little to no human involvement.

CPUs, GPUs, and accelerators: The chips powering AI From general computing to AI training, here's what each chip does and why Amazon builds its own. What makes AI truly agentic? True agentic AI has two critical requirements: high trust and high reliability. A chatbot can summarize a contract when you ask and maybe even email it to a colleague.

But an agent can review a vendor agreement, flag nonstandard terms, route it to legal for approval, and follow up if no response comes within 48 hours. Agentic systems think holistically, evaluating results and exercising judgment to adjust their approach.

They might autonomously handle code reviews, process insurance claims, or plan complex travel itineraries—all while adapting to obstacles and changing conditions. Why agentic AI matters now The business world is reaching a tipping point with agentic AI.

While generative AI transformed how people search and create, agentic AI turns intelligence into action by adding planning, reasoning, and the ability to execute multi-step workflows. "The field has made extraordinary progress on AI capability. These systems can reason, write code, and hold conversations.

They're incredibly useful when a person is reviewing their work," said Bryan Silverthorn, who leads Amazon's artificial general intelligence (AGI) Lab. "The next challenge, where Amazon is focused, is closing the gap between what businesses trust AI agents to do when someone is watching and what they trust them to do on their own.”

Organizations eager to embrace agentic AI face foundational challenges. Agents can produce different outputs even with identical inputs, yet businesses require predictable outcomes. The technology behind Amazon's AI agents Building reliable agentic AI requires a comprehensive technology stack and rigorous training methodology.

Amazon's approach ensures agents can operate at scale while maintaining the trust enterprises require. A city in the palm of your hand: Exploring the intricate world of an Amazon Web Services chip Tour the micro metropolis where calculations run 24/7 and data commutes at light speed. Foundation models form the core of agentic systems.

Amazon Bedrock provides access to leading models, including Anthropic's Claude, OpenAI's models, and Amazon Nova—all optimized for reasoning and tool use. Amazon Nova Act was designed from the ground up for reliability and action.

An agent-building service, Nova Act trains model capabilities, orchestration logic, and tool controls together as one integrated system. By focusing on reliability, Nova Act specifically addresses the trust gap that has kept most agentic AI as experiments rather than deployed products.

For computer-use agents—which navigate computers as human users do—Amazon uses reinforcement learning at scale, essentially building digital gyms where agents practice scrolling, clicking, and interacting with different user interfaces.

This training approach enables agents to learn through trial and error so that they can achieve greater than 90%+ reliability, the threshold where automation becomes genuinely useful for enterprises.

"Most AI agents are trained on simplified tasks that appear in benchmarks, so they break down in the real world when things get messy," said Gaurav Mishra, a research engineer at Amazon's AGI Lab.

"We use reinforcement learning to have agents practice in thousands of realistic simulated environments, and we're finding that skills learned in one environment transfer to others. That compounding effect is what moves agents from demo-ready to production-ready."

The infrastructure advantage Building AI agents that work reliably requires enormous computing power—to run the agents as well as train them. Training an AI model is one of the most computationally intensive tasks in the history of computing, and the costs can be staggering. Amazon has spent more than a decade solving this problem.

In 2015, the company began designing its own specialized computer chips —first for general cloud computing, then specifically for AI. Today, Amazon has delivered more than 2. 1 million of its latest AI-specific chips into operation, and leading AI companies like Anthropic use Amazon's infrastructure to train their most advanced models.

The advantage of designing your own chips is straightforward: whe