AI agents will change how we all work and live. Our AWS CEO, Matt Garman, shared a vision of a technological shift as transformative as the advent of the internet. I’m energized by this vision because I’ve witnessed firsthand how these intelligent agent systems are already beginning to solve complex problems, automate workflows, and create new possibilities across industries. With agentic AI, AstraZeneca accelerated healthcare insight discovery, Yahoo Finance transformed financial research for millions of investors, and Syngenta revolutionized agriculture with AI-driven precision farming.
To expand these early successes into widespread adoption, organizations need a practical approach that addresses the inherent complexity of agentic systems. At AWS, we’re committed to being the best place to build the world’s most useful AI agents, empowering organizations to deploy reliable and secure agents at scale.
We’re focused on making our agentic AI vision accessible to every organization by combining rapid innovation with a strong foundation of security, reliability, and operational excellence. Our approach accelerates progress by building on proven principles while embracing new possibilities—creating systems that can adapt as models evolve, new capabilities emerge, and use cases expand across your business.
Today, I’m excited to share how we’re bringing this vision to life with new capabilities that address the fundamental aspects of building and deploying agents at scale. These innovations will help you move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.
A comprehensive foundation for building and deploying production-ready agentic AI systems at scale.
Guiding principles, evolved for agents
At AWS, our approach to agentic AI is shaped by our experience building agent systems internally and helping hundreds of thousands of customers accelerate their AI journeys. Four core principles guide everything we do in this space:
Principle 1: Embrace agility as a competitive edge
Organizations that thrive won’t be those who perfectly predict the future, but those who adapt quickly as it unfolds. Staying nimble requires an agentic architecture that embraces flexibility and openness rather than rigid frameworks or singular models. It means building systems that can incorporate new models as they emerge, connect to your proprietary data sources, and seamlessly integrate with your existing tools.
The dual need for stability and adaptability led us to create Amazon Bedrock AgentCore, a complete set of services for deploying and operating highly capable agents securely at enterprise scale. AgentCore provides a secure, serverless runtime with complete session isolation and the longest running workload available today, tools and capabilities to help agents execute workflows with the right permissions and context, and controls to operate trustworthy agents. Its capabilities can be used together or independently and work with popular open source frameworks such as CrewAI, LangGraph, LlamaIndex, and Strands Agents and with any model including those in (or outside of) Amazon Bedrock, so developers can stay agile as technology shifts. By reducing the undifferentiated heavy lifting, AgentCore helps organizations move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.
Customers like Itaú Unibanco, Innovaccer, Boomi, Box, and Epsilon are already experimenting with AgentCore and are excited about how it speeds their deployment of agents to production. These early adopters recognize that AgentCore helps eliminate the trade-off between open source flexibility and enterprise-grade security and reliability, allowing them to focus on creating business value rather than building security and operational foundations from scratch.
Principle 2: Evolve fundamentals for the agentic era
While the core principles of enterprise technology haven’t changed, how we implement them must evolve for the agentic era. These evolved fundamentals create the foundation that makes production-grade agents possible:
Security and Trust. Agents introduce new security considerations as they cross system boundaries, perform actions on behalf of users or act themselves with pre-authorized user consent. Trust requires transparency, guardrails, and verification. AgentCore Runtime helps address these with dedicated compute environments per session and memory isolation that helps prevent data leaks across agents, building on a decade of AWS Lambda serverless innovation in security and scalability.
Reliability and Scalability. Traditional approaches to scaling software fall short with agentic systems as they follow unpredictable execution paths and have variable resource requirements across interactions. AgentCore Runtime is highly reliable with checkpointing and recovery capabilities to help ensure graceful recovery in case of unexpected interruptions and failures, and it can automatically handle scaling from zero to thousands of concurrent sessions, eliminating capacity planning and infrastructure maintenance.
Identity. As agents act on behalf of users and systems, traditional identity models must evolve. Managing permissions of both the agent and the user as agents navigate complex workflows spanning multiple systems becomes critical to securing your data. AgentCore Identity delivers secure agent access across AWS services and third-party applications and tools with temporary, fine-grained permissions, and standards-based authentication. It works with leading identity providers such as Amazon Cognito, Microsoft Entra ID, and Okta, as well as popular OAuth providers such as GitHub, Google, Salesforce, and Slack.
Observability. Understanding agent decisions requires new approaches to monitoring. Observability becomes essential not just for troubleshooting, but for compliance and continuous improvement, representing a shift from periodic auditing to constant supervision. AgentCore Observability provides real-time visibility through built-in dashboards and standardized telemetry that integrates with your monitoring stack.
Data. Your proprietary data is more valuable than ever, enabling agents to understand your specific context. The ability to securely access, process, and learn from this data becomes a critical differentiator for agent performance and relevance. For example, with AgentCore Gateway, you can transform your data sources including Amazon Bedrock Knowledge Bases into agent-compatible tools so agents can access recent and relevant information.
Seamless Integration. Agents must work with everything in your environment: your systems, other clouds, SaaS applications, and other agents. AgentCore Gateway makes it possible by transforming APIs and services into agent-compatible tools with minimal code, eliminating months of integration work while enabling agents to discover and interact with your systems. Our open source Strands Agents SDK complements this with flexible orchestration patterns, and support for MCP and A2A to enable seamless coordination between multiple agents and tools across different environments. AWS API MCP Server gives agents a callable interface to AWS services, enabling foundation models to discover available operations, reason over input and output requirements, and generate plans that invoke AWS APIs to explore, configure, or manage resources with real-time AWS capabilities beyond model training cutoff.
Tooling and Capabilities. Agents need specialized tools to execute complex tasks and maintain context across interactions. AgentCore Memory makes it easy for developers to build context-aware agents by eliminating complex memory infrastructure management while providing full control over what the AI agent remembers. It provides industry-leading accuracy along with support for both short-term memory for multi-turn conversations and long-term memory that persists across sessions, with the ability to share memory stores across collaborating agents. Built-in tools include AgentCore Browser for web interactions, enabling agents to navigate websites and perform actions on your behalf, and AgentCore Code Interpreter for executing code securely, allowing agents to process data, generate visualizations, and solve complex problems programmatically. These capabilities extend what agents can do while maintaining security and reliability.
Together, these evolved fundamentals help organizations build secure, reliable, and scalable agent architectures that deliver consistent results in production environments. With AgentCore, we’re helping customers focus on creating value rather than reinventing infrastructure.
Principle 3: Deliver superior outcomes with model choice and data
At the heart of every effective agent system lies its foundation model, which powers an agent’s ability to understand, reason, and act. For agents to deliver transformative experiences, carefully selected and potentially tailored models need to interact with rich, context-specific knowledge that determines how effectively the model can make decisions on your behalf. This reality extends to all AI applications, which is why AWS gives customers both the freedom to choose the optimal model for each use case and the tools to enhance those models with their unique data. This approach delivers superior outcomes and the best price-performance for all AI implementations.
Model requirements vary widely—some applications demand sophisticated reasoning, others require fast responses, and many prioritize cost efficiency at scale. No single model excels across all dimensions, which is why we pioneered model choice with Amazon Bedrock in 2023. But the true differentiator is how you combine models with your organization’s proprietary data, transforming generic AI into systems with deep domain expertise.
To help you create models with this high level of expertise, today we’re expanding our model customization capabilities with the launch of Amazon Nova customization in Amazon SageMaker AI. Nova models now offer customers the flexibility to customize the model across the model development life cycle. This includes pre-training and post-training, including both fine-tuning and alignment, with support for parameter efficient fine-tuning (PEFT) and full fine-tuning. With these, Nova now offers the most comprehensive suite of model customization capabilities made available for any proprietary model family. Using techniques including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), reinforcement learning from human feedback using Proximal Policy Optimization (PPO), Continued Pre-Training (CPT), and Knowledge Distillation, customers can create Nova models optimized for their use-case. Once customized, these models can be deployed directly to Amazon Bedrock, allowing you to seamlessly integrate your custom models into your agent systems and other AI applications.
We are also training our own models optimized for specific agent use cases. Nova Act is an AI model trained to perform actions within a web browser. Customers can get started with building their own browser automation agents with the Nova Act SDK, purpose-built to enable reliable browser agents powered by the Nova Act model. The Nova Act SDK, available in research preview today, uses AgentCore Browser for scalable, cloud-based browser execution.
Once you have the right model, you need to ensure it can interact with your organization’s proprietary and current data. Vectors have emerged as the dominant and fastest way AI models can access your data. Until now, the cost of storing vector embeddings—the key to enabling this intelligence—has forced organizations to limit their AI systems to recent data only, constraining their potential. Today’s launch of Amazon S3 Vectors, the first cloud object store with native vector support, marks a fundamental change. By reducing vector storage costs by 90% while maintaining sub-second query performance, S3 Vectors enables agents that remember more, reason deeper, and maintain comprehensive context from every customer interaction, document, and business insight. S3 Vectors integrates directly with Amazon Bedrock Knowledge Bases for cost-effective RAG applications and Amazon OpenSearch Service for tiered vector strategies.
Principle 4: Deploy solutions that transform experiences
While models and infrastructure change how organizations build, agentic solutions transform how businesses operate. The true power of agentic AI lies in its ability to reshape workflows and human productivity across entire industries. These solutions free people from routine tasks and handle complex information flows, enabling teams to focus on creative thinking and strategic decisions. We’re making this transformation accessible to more organizations through pre-built agentic solutions. By combining foundational building blocks with pre-built solutions you can move beyond experiments to comprehensive AI strategies that deliver tangible business impact.
Today, we’re announcing that you can now buy AI Agents and Tools in AWS Marketplace, with streamlined procurement and multiple deployment options. In today’s fragmented AI landscape, AWS Marketplace offers a centralized catalog of curated agents, tools, and solutions from AWS Partners. Fast-track automation with pre-built agents from AWS Partners. Our new API-based deployment method helps you to streamline integrations with other agents and tools that support MCP and A2A. And these agents can run on trusted AWS services or in your AWS environment, where you maintain control over security and access. You can deploy select pre-built agents and tools on AgentCore.
We’re also continuing to give customers ready-to-deploy agent solutions that enable this transformation. Kiro is an AI IDE that helps developers go from concept to production with spec-driven development. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs—then into working code, docs, and tests. So, what you build is exactly what you want and ready to share with your team. Kiro’s agents help you solve challenging problems and automate tasks like generating documentation and unit tests. With Kiro, you can build beyond prototypes while being in the driver’s seat every step of the way. AWS Transform deploys specialized AI agents to automate complex modernization tasks like code analysis, refactoring, and dependency mapping, dramatically reducing project timelines for enterprise workload migrations. Each solution shows our commitment to flexibility and choice, helping you innovate faster and realize business outcomes sooner. And Amazon Connect, a comprehensive customer experience solution, enables organizations to delight their customers with unlimited AI on every customer interaction across all channels.
These four principles guide our product strategy and are embedded in every innovation we’re announcing today: embracing agility, evolving fundamentals, combining model choice with proprietary data, and deploying transformative solutions. Together, they provide a comprehensive framework for successfully implementing agentic AI in your organization.
The path forward
The significant potential for our customers and our own diverse businesses has inspired us to focus on building the most trustworthy agentic AI capabilities on the planet. But the most important advice I can offer is simple: start now.
Don’t get trapped trying to boil the ocean or waiting for all the answers before you begin. Pick a specific business problem that matters and get building. The organizations seeing the greatest success aren’t those with the most ambitious plans, they’re those who have started the learning cycle, gathering real-world feedback that informs each iteration. To help our customers on their AI journey, we’re investing another 100 million dollars, doubling our investment, in the AWS Generative AI Innovation Center which has helped thousands of customers across industries including NFL, Yahoo Finance, BMW, and AstraZeneca achieve millions of dollars in productivity gains and transform customer experiences.
AWS set the standard for security, reliability, and data privacy for cloud computing, and we’re bringing these same principles to agentic AI. No matter your use case or requirements, AWS provides the right foundation to help you succeed. Together, we can reinvent what’s possible for your business through the power of agentic AI.
About the author
Swami Sivasubramanian is Vice President for Agentic AI at Amazon Web Services (AWS). At AWS, Swami has led the development and growth of leading AI services like Amazon DynamoDB, Amazon SageMaker, Amazon Bedrock, and Amazon Q. His team’s mission is to provide the scale, flexibility, and value that customers and partners require to innovate using agentic AI with confidence and build agents that are not only powerful and efficient, but also trustworthy and responsible. Swami also served from May 2022 through May 2025 as a member of the National Artificial Intelligence Advisory Committee, which was tasked with advising the President of the United States and the National AI Initiative Office on topics related to the National AI Initiative.