Build And Deploy Scalable Ai Agents With Nvidia Nemo Amazon Bedrock

Bonisiwe Shabane

-Dec 22, 2025, 3:04 PM

build and deploy scalable ai agents with nvidia nemo amazon bedrock

This post is co-written with Ranjit Rajan, Abdullahi Olaoye, and Abhishek Sawarkar from NVIDIA. AI’s next frontier isn’t merely smarter chat-based assistants, it’s autonomous agents that reason, plan, and execute across entire systems. But to accomplish this, enterprise developers need to move from prototypes to production-ready AI agents that scale securely. This challenge grows as enterprise problems become more complex, requiring architectures where multiple specialized agents collaborate to accomplish sophisticated tasks. Building AI agents in development differs fundamentally from deploying them at scale. Developers face a chasm between prototype and production, struggling with performance optimization, resource scaling, security implementation, and operational monitoring.

Typical approaches leave teams juggling multiple disconnected tools and frameworks, making it difficult to maintain consistency from development through deployment with optimal performance. That’s where the powerful combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit shine. You can use these tools together to design sophisticated multi-agent systems, orchestrate them, and scale them securely in production with built-in observability, agent evaluation, profiling, and performance optimization. This post demonstrates how to use this integrated solution to build, evaluate, optimize, and deploy AI agents on Amazon Web Services (AWS) from initial development through production deployment. The open source Strands Agents framework simplifies AI agent development through its model-driven approach. Developers create agents using three components:

The framework includes built-in integrations with AWS services such as Amazon Bedrock and Amazon Simple Storage Service (Amazon S3), local testing support, continuous integration and continuous development (CI/CD) workflows, multiple deployment options, and OpenTelemetry... In this post, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users to upload multi-modal business documents and extract insights using BDA as a parser to retrieve relevant chunks and augment a prompt to a... Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and manually transferring information between systems. Data entry and validation tasks […]

In this post, we explore how agentic QA automation addresses these challenges and walk through a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail... In this post, we explore how Mantle, Amazon’s next-generation inference engine for Amazon Bedrock, implements a zero operator access (ZOA) design that eliminates any technical means for AWS operators to access customer data. In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production. This publish is co-written with Ranjit Rajan, Abdullahi Olaoye, and Abhishek Sawarkar from NVIDIA. AI’s subsequent frontier isn’t merely smarter chat-based assistants, it’s autonomous brokers that purpose, plan, and execute throughout complete programs.

However to perform this, enterprise builders want to maneuver from prototypes to production-ready AI brokers that scale securely. This problem grows as enterprise issues turn out to be extra complicated, requiring architectures the place a number of specialised brokers collaborate to perform refined duties. Constructing AI brokers in improvement differs basically from deploying them at scale. Builders face a chasm between prototype and manufacturing, combating efficiency optimization, useful resource scaling, safety implementation, and operational monitoring. Typical approaches go away groups juggling a number of disconnected instruments and frameworks, making it tough to keep up consistency from improvement by deployment with optimum efficiency. That’s the place the highly effective mixture of Strands Brokers, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit shine.

You should use these instruments collectively to design refined multi-agent programs, orchestrate them, and scale them securely in manufacturing with built-in observability, agent analysis, profiling, and efficiency optimization. This publish demonstrates tips on how to use this built-in resolution to construct, consider, optimize, and deploy AI brokers on Amazon Net Companies (AWS) from preliminary improvement by manufacturing deployment. The open supply Strands Brokers framework simplifies AI agent improvement by its model-driven method. Builders create brokers utilizing three elements: The framework contains built-in integrations with AWS companies equivalent to Amazon Bedrock and Amazon Easy Storage Service (Amazon S3), native testing help, steady integration and steady improvement (CI/CD) workflows, a number of deployment choices,... Visit your regional NVIDIA website for local content, pricing, and where to buy partners specific to your country.

Accelerated infrastructure, enterprise AI software, and advanced NVIDIA AI models Building blocks of AI agents designed to reason, plan, and act Optimized inference platform for fast AI model deployment AI-powered cybersecurity solutions to detect and prevent threats New NVIDIA AI Blueprints for building agentic AI applications are poised to help enterprises everywhere automate work. With the blueprints, developers can now build and deploy custom AI agents.

These AI agents act like “knowledge robots” that can reason, plan and take action to quickly analyze large quantities of data, summarize and distill real-time insights from video, PDF and other images. CrewAI, Daily, LangChain, LlamaIndex and Weights & Biases are among leading providers of agentic AI orchestration and management tools that have worked with NVIDIA to build blueprints that integrate the NVIDIA AI Enterprise software... These five blueprints — comprising a new category of partner blueprints for agentic AI — provide the building blocks for developers to create the next wave of AI applications that will transform every industry. In addition to the partner blueprints, NVIDIA is introducing its own new AI Blueprint for PDF to podcast, as well as another to build AI agents for video search and summarization. These are joined by four additional NVIDIA Omniverse Blueprints that make it easier for developers to build simulation-ready digital twins for physical AI. To help enterprises rapidly take AI agents into production, Accenture is announcing AI Refinery for Industry built with NVIDIA AI Enterprise, including NVIDIA NeMo, NVIDIA NIM microservices and AI Blueprints.

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Build And Deploy Scalable Ai Agents With Nvidia Nemo Amazon Bedrock

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