A Technology Broker and Management Company Specializing in KM Systems
Favorite Java teams have established JVM tooling, continuous integration and continuous delivery (CI/CD) pipelines, and operational patterns, yet taking AI agents from prototype to production is hard: agent frameworks do not solve scalability, governance, security, and operational concerns by default. Amazon Bedrock and Spring AI close those gaps, so Java
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Shared by AWS Machine Learning November 12, 2026
Favorite Over the past year, AI agents have moved from exploration into engineering. Enterprise teams are shipping large language model (LLM) systems that call tools, orchestrate APIs, and answer questions over internal knowledge. A successful demo can show what an agent is capable of. Before release, the team needs to
Read More Shared by AWS Machine Learning November 12, 2026
Favorite More enterprises are building AI agents into their daily work, and a common pattern is an agent that searches internal documents to compose its answers. A single organization might soon run hundreds of agents, each connected to many data sources and tools. This raises a question. When an agent
Read More Shared by AWS Machine Learning November 12, 2026
Favorite Agents in multi-agent systems that span organizational boundaries often need to collaborate in a composable, interoperable way, with agents that other organizations build and run. They need a way to discover each other, communicate, and exchange tasks without exposing internals or requiring the same framework on both sides. The
Read More Shared by AWS Machine Learning November 12, 2026
Favorite With a customer-managed knowledge base on Amazon Bedrock, you provision and scale the vector store, tune the embedding model and index settings, and plan capacity for each new data source. The migration guide in the Agent Toolkit for AWS helps you migrate to Amazon Bedrock Managed Knowledge Base. Managed
Read More Shared by AWS Machine Learning November 12, 2026
Favorite AI agents have moved past one-off prompts. They now run multi-step workloads for hours at a time. The length of task that frontier agents can complete on their own doubles about every four months (METR), and 40 percent of large enterprises are scaling AI agents, up from 27 percent
Read More Shared by AWS Machine Learning November 12, 2026
Favorite Developers want AI coding assistants with minimal setup. With a large language model (LLM) gateway and LiteLLM, they can quickly access foundation models (FMs) available on Amazon Bedrock while you gain visibility and usage control. Platform teams need to monitor latency and errors, finance teams need to attribute inference
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Favorite Part 1 of this series built the operating model that controls Amazon Bedrock spend: attribution with AWS Identity and Access Management (IAM) principal tags and Cost and Usage Report 2.0, observability with model invocation logging, enforcement at three tiers, and a review cadence. That layer answers who spent it,
Read More Shared by AWS Machine Learning November 12, 2026
Favorite Generative AI spend is hard to attribute. Before an organization expands AI access, CFOs and CTOs usually want three answers: what do we spend per person or per team, can we cap it, and how much work does it take to get there. In our previous post, Tokenomics at
Read More Shared by AWS Machine Learning November 12, 2026
Favorite Your Amazon Bedrock inference bill can keep climbing after you tune prompts and application logic. Request-level choices can still change how many tokens the service processes and the rate applied to them. Cache duration, service tier, reasoning effort, and maxTokens can affect cost without changing your model or prompt.
Read More Shared by AWS Machine Learning November 12, 2026