360SOFTY

Insights

Engineering Insights

Practical writing on software architecture, SaaS products, AI automation, legacy modernisation, and the business of building reliable systems.

RSS

Curated links from external sources — not 360Softy original articles.

ExternalAI
AWS Machine Learning Blog

Integrating AWS API MCP Server with Amazon Quick using Amazon Bedrock AgentCore Runtime

This post shows you how to use Amazon Bedrock AgentCore Runtime with Model Context Protocol (MCP) support to connect Amazon Quick with AWS services through the AWS API MCP Server, creating a conversational AI assistant that translates natural language into AWS Command Line Interface (AWS CLI) commands, without the need to switch between tools during critical moments.

Advanced (300)Amazon Bedrock AgentCoreAmazon Quick Suite
AWS Machine Learning BlogRead original
ExternalSoftware Engineering
DZone

Throughput vs Goodput: The Performance Metric You Are Probably Ignoring in LLM Testing

In this blog post, we will see the difference between throughput and goodput, why throughput alone can give you a dangerously false sense of confidence, and how goodput, the metric championed by NVIDIA's AIPerf tool, tells you the truth about your LLM deployment. If you have ever shipped a feature that looked perfectly healthy in your monitoring dashboard but fell apart under real user load, this post is for you.

DZoneRead original
ExternalAI
AWS Machine Learning Blog

Building multi-tenant agents with Amazon Bedrock AgentCore

This post explores design considerations for architecting multi-tenant agentic applications and the framework needed to address SaaS architecture challenges with Amazon Bedrock AgentCore.

Advanced (300)Amazon Bedrock AgentCoreBest Practices
AWS Machine Learning BlogRead original
ExternalAI
AWS Machine Learning Blog

Break the context window barrier with Amazon Bedrock AgentCore

In this post, you will learn how to implement Recursive Language Models (RLM) using Amazon Bedrock AgentCore Code Interpreter and the Strands Agents SDK. By the end, you will know how to process documents of varying lengths, with no upper bound on context size, use Bedrock AgentCore Code Interpreter as persistent working memory for iterative document analysis, and orchestrate sub-large language model (sub-LLM) calls from within a sandboxed Python environment to analyze specific document sections

Advanced (300)Amazon BedrockAmazon Bedrock AgentCore
AWS Machine Learning BlogRead original

Work with 360Softy

Building a SaaS product, AI system, or business platform?

Book a free consultation and we will tell you honestly whether we can help.

Book a consultationView our services →