Large language models (LLMs) deliver strong results on general tasks, but they often struggle with specialized work that requires understanding proprietary data, internal processes, or domain-specific terminology. Amazon Nova Forge addresses this by enabling you to build your own frontier models using Amazon Nova. You can start development from early model checkpoints, blend proprietary data … Read more
Traditional computer vision solutions can require significant upfront investment. Setting up data pipelines, model training infrastructure, compute resources, and a dedicated data science team is often prohibitive for small companies or teams. Amazon Nova 2 Lite, available through Amazon Bedrock, provides an appealing alternative solution. This multimodal foundation model detects objects through natural language prompts … Read more
Code review was always manual and ineffective because of the inherent disconnect between code and product. Developers could review whether code compiled and worked, but not whether it fulfilled all functional and design requirements. In the past, QA teams spent hours manually clicking through preview environments to ensure features behaved as expected, and even more … Read more
In modern development workflows, developers increasingly rely on agentic coding assistants such as Kiro Integrated Development Environment (IDE) to interact with remote tools and services. However, organizations require robust authentication mechanisms to provide secure, identity-verified access between these agentic coding assistants and enterprise Model Context Protocol (MCP) servers. Amazon Bedrock AgentCore is a fully managed … Read more
AI agents are only as powerful as the tools they can access. Whether retrieving customer data from a CRM, posting updates to Slack, or querying a GitHub repository, agents need to call external APIs, and that means securely passing credentials at runtime. Getting that right, without hardcoding secrets in code or exposing them in agent … Read more
Rare cancer research generates heterogeneous data across genomic sequencing pipelines, clinical trial registries, biomarker repositories, and peer-reviewed literature. Integrating these sources for a single investigation typically requires custom ETL pipelines, manual schema reconciliation, and iterative querying across disconnected systems—a process that can take weeks before any analysis begins. Amazon Quick Research addresses this integration challenge … Read more
GPT-5.5, GPT-5.4, and Codex are now generally available on Amazon Bedrock. Deploy them in production applications and agents today, on Bedrock’s high performance inference engine. Key takeaways GPT-5.5, the most advanced frontier model from OpenAI, is generally available on Amazon Bedrock. Pricing matches OpenAI first-party rates. Codex on Amazon Bedrock is generally available with pay-per-token pricing. Inference runs through Bedrock, and … Read more
While deploying Model Context Protocol (MCP) servers in production, enterprises need fine-grained access control across servers, observability into which teams use which tools, security guarantees against data exfiltration, and centralized credential management, all at scale. Amazon Bedrock AgentCore Gateway sits between MCP servers and the clients that consume them, centralizing credential management, observability, and secure … Read more
Securing AI agent behavior is a key customer challenge in building agentic solutions. As enterprises rapidly adopt AI agents to automate workflows, they face a scaling challenge in managing secure access to tools across the organization. Modern unified enterprise AI platforms have hundreds of agents serving users across the organization. These agents need to access … Read more
Agents increasingly take actions on behalf of their end users, whether that’s selecting tools, browsing the web, and calling MCP servers autonomously to achieve a goal. When the tools, MCP endpoints, or web resources an agent reaches are paid, the agent gets stuck without the ability to transact. Amazon Bedrock AgentCore payments, announced in preview … Read more
When you build agentic AI solutions, you face unique operational challenges. Agents make unpredictable decisions, costs spiral unexpectedly, and debugging non-deterministic failures seems impossible. Agentic AI applications don’t just execute predetermined workflows. They reason, adapt, and make autonomous decisions, and DevOps practices need to be adapted. That’s where AgentOps comes in, the operational discipline for … Read more
If you’re iterating on deploying large language models (LLMs) on AWS GPU instances, you’ve probably noticed the larger the model to be loaded into GPU High Bandwidth Memory (HBM), the longer the painful wait until the GPUs are ready for inference. As models grow to hundreds of billions of parameters and GPU environments grow ever … Read more
Model Context Protocol (MCP) integration in Amazon Quick transforms how financial analysts access time-series market intelligence, removing the need for complex database queries. As a financial analyst, you navigate millions of stock trades flowing through markets every second, searching for patterns that drive trading decisions. Financial institutions often use time series databases to analyze high-frequency … Read more
Deploying large language models (LLMs) at scale on Amazon SageMaker AI Inference makes observability a critical pillar of any production machine learning (ML) strategy. Unlike conventional software that returns deterministic outputs, LLMs generate variable, free-form responses that are difficult to validate with standard metrics. LLM output quality can change over time as input distributions shift, … Read more
This solution builds on open source tools including PyTorch, Hugging Face Transformers, and Liger Kernels. The authors would also like to thank Aiham Taleb, Arefeh Ghahvechi, Manav Choudhary, Rohit Thekkanal, Daz Akbarov, Jamila Jamilova, Ross Povelikin, Almas Moldakanov, Christelle Xu, and Ivan Khvostishkov for their contributions in making this project possible. Azercell Telecom LLC, Azerbaijan’s … Read more
As ML teams grow, embedding Amazon SageMaker AI MLflow Apps into a custom portal requires a scalable approach to access management. Distributing presigned URLs doesn’t scale for teams with dozens of data scientists, and granting individual AWS Management Console access adds operational overhead for administrators managing access controls. Teams who rely on SSO-integrated internal portals … Read more
Machine learning (ML) teams use MLflow to manage their ML lifecycle effectively. Amazon SageMaker MLflow provides comprehensive ML experiment tracking and model management capabilities. However, many enterprises have existing infrastructure requirements that need HTTPS-based integrations rather than direct SDK usage. Many organizations need to integrate Amazon SageMaker MLflow with their established systems while maintaining their … Read more
This post was co-authored with Karan Singh, Head of Partnerships at LangChain Validating AI agent behavior before production is one of the hardest problems in applied AI. Agents are non-deterministic, multi-step where errors in early steps can affect downstream results. A single bad tool call can cascade through an entire workflow. LangSmith on AWS gives … Read more
Agent evaluation is most powerful when you combine fast-moving online signals with stable offline baselines. To understand whether your agent is truly improving over time, you need a fixed benchmark alongside your changing real-world traffic. Managing test cases for evaluation baselines as a dataset in Amazon Bedrock AgentCore brings the discipline of versioned test fixtures … Read more
Today, we’re excited to announce the availability of Anthropic’s most advanced Opus model, Claude Opus 4.8, on Amazon Bedrock and the Claude Platform on AWS. Claude Opus 4.8 represents a meaningful step forward, delivering improvements across the workflows teams run in production, from agentic coding and deep knowledge work to multi-stage autonomous tasks that span … Read more
Financial institutions running on AWS and Snowflake benefit from a deeply integrated framework that combines Snowflake’s AI Data Cloud with AWS cloud infrastructure, including integrations with AWS services such as Amazon Simple Storage Service (Amazon S3), AWS Glue, Amazon SageMaker, and Amazon Bedrock. With over 50 native integrations between AWS services and Snowflake, organizations can … Read more
Financial institutions process thousands of documents daily, including tax forms, loan statements, and purchase orders. Each has a unique format, structure, and field names, making it challenging to create automation workflows using optical character recognition (OCR) software. Amazon Bedrock Data Automation (BDA) helps solve these challenges by automating the extraction, validation, and analysis of data … Read more
Developing AI agents for business support presents unique challenges that many organizations face when trying to automate routine HR tasks. Works Human Intelligence (WHI) develops, sells, and supports the integrated HR system “COMPANY” for major Japanese corporations and public interest corporations. In this post, we share how the AWS Generative AI Innovation Center (GenAIIC) collaborated … Read more
A special thanks goes to the Verizon Connect team who’s been working very hard on the project: Matteo Simoncini, Luca Bravi, Alberto Rossettini, Martin Villarruel, Ceyhun Unlu, Adriel Zuquini, Andrea Benericetti. Fleet managers today face an overwhelming challenge: transforming data overload into actionable insights. When you’re managing thousands of vehicles, each generating hundreds of daily … Read more
AWS leaders manage complex data across multiple hierarchies while making time-sensitive decisions that impact global operations. Traditional business intelligence relies on static dashboards and manual reports, which creates delays and limits organizational agility. NarrateAI, our intelligent conversational solution, addresses this through conversational agentic AI powered by our data lake and Amazon Bedrock AgentCore. Accessible through … Read more
As agent adoption scaled, we saw a common pattern emerge across enterprises, including our own sales organization: specialized agents deliver value, but without orchestration, users carry the cognitive load of choosing between them. At AWS Sales, this meant more than 20 domain-specific agents deployed across the global organization, with representatives context-switching between systems instead of … Read more
The industry is entering a world where billions of generative AI agents operate autonomously, acting on behalf of humans, making decisions, and completing tasks without human intervention. To support this shift, Amazon Bedrock AgentCore provides a modular, fully managed platform that helps developers build, deploy, and operate generative AI agents at scale. By abstracting the … Read more
Generative AI has rapidly evolved from experimental prototypes into systems that are expected to operate reliably in production, at scale, and under real-world performance constraints. As organizations move beyond demos and proofs of concept, they increasingly encounter challenges related to inference latency, scalability, state management, and operational visibility. Building high-performance AI agents today requires more … Read more
Building high-performance generative AI agents requires architecture that can deliver fast inference, coordinate multiple agents, and operate reliably under production workloads. If you are building generative AI agents to automate reviews, power digital assistants, and support complex decision-making workflows, you need these agents to perform well. They must reduce manual effort, respond in near real … Read more
AgentWatch delivers ambient AWS resource monitoring for your DevOps team, moving beyond the reactive cycle of managing Amazon CloudWatch alarms across multiple accounts. CloudWatch alarms trigger too late, AWS Lambda errors accumulate unnoticed, and Amazon Elastic Compute Cloud (Amazon EC2) performance degradation goes undetected until customers report problems. This leaves your team constantly firefighting rather … Read more