Category: Amazon Machine Learning

This post shares quantified results of the company Superhuman, which made AI to be the default way of work for their platform team. As a result, developer output rose 60%, 1,300 autonomous pull requests shipped in a single half, and experimentation rates tripled. This post looks at how the Superhuman’s platform team did it, what […]

Independent Software Vendors (ISVs) with established SaaS products face a pivotal modernization decision. While enterprise customers demand intelligent automation through AI agents, they still require complete control over their data. For ISVs already serving regulated industries, the challenge isn’t building a new product, but it’s evolving existing SaaS architecture to incorporate autonomous AI agents while […]

Introduction If you’re managing student transportation for a school district, you’re navigating increasingly diverse student needs alongside rising expectations from families who want real-time visibility into their student’s journey. At the same time, you must deliver consistent, reliable service in a complex operating environment where disruptions can quickly impact safety, service reliability and community trust. […]

A New Chapter in Security For years, cybersecurity defense traditionally followed a familiar, non-continuous rhythm. An issue is discovered, security teams investigate, developers prioritize a fix, and operations deploy a patch. The organization moves on until the next one. That cycle worked when changes happened weekly and threat actors needed months to develop proof-of-concept code. […]

This post shows how to migrate a fleet of AI agents from Amazon Bedrock Agents Classic to Amazon Bedrock AgentCore Runtime using a pattern-first approach and parallel AI subagents. We migrated 20 agents from the open-source HCLS Agents Toolkit in approximately 2.5 hours of wall-clock time, across three phases: establishing a migration template, running parallel […]

Organizations running ASP.NET applications face a common dilemma: adding AI capabilities typically means significant refactoring or building entirely new systems. Your existing REST APIs contain years of valuable business logic, but there’s no straightforward path to make them AI-accessible without a rewrite. What if you could keep your code and add intelligence on top? In […]

An AWS Transform custom agent converts PyTorch and Triton kernels to NKI for AWS Trainium, compiling, verifying, and profiling every candidate on real hardware. The same Claude model that cold-solves 6% of kernels reaches 83% inside the agent’s verify-and-fix loop.

Collecting structured data from dozens of distributed stakeholders (whether for cloud migration planning, compliance assessments, vendor onboarding, or security reviews) is a persistent bottleneck in enterprise workflows. Teams typically rely on emailed spreadsheets, which generate weeks of follow-ups, inconsistent responses, and manual reconciliation. This post shows how to build a conversational data collection agent on […]

Introduction Consider a team with over 400 servers to migrate, dozens of applications with varying dependencies, and multiple teams waiting on cutover windows. Every migration is unique, yet the challenges remain consistent. Teams struggle with manual tracking through spreadsheets, human errors in repetitive tasks like agent installation and DNS updates, decentralized orchestration across disconnected consoles, […]

An AI migration means different things to different teams, and that ambiguity is expensive. Are you migrating with AI, to AI, or from AI? The answer shapes your staffing plan, governance model, and timeline, because each motion carries a different risk profile and demands a different set of skills. Gartner found that by the end […]