In this No Dumb Questions, Phoebe asks Stack’s Engineering Manager Doug Whitley and Product Manager Ash Zade everything she wants to know about AI context architecture. What exactly is it? Why is it so important? What makes for good AI context architecture? Why buy one when you can build your own?
No Dumb Questions: What is AI context architecture? Why not just build your own? Read More »
When Bosch Mobility Platform Solutions set out to unify vehicle tracking across India’s fragmented spot logistics market, they faced a daunting reality: dozens of telematics providers, incompatible data formats, and thousands of concurrent tracking requests — all needing real-time resolution. The result was L.OS, a serverless platform on AWS that standardizes this chaos into a
Serverless vehicle tracking at scale: Bosch L.OS on AWS Read More »
Ryan welcomes Meryll Blanchet, Director of Engineering for Adobe Brand Visibility, to chat about Adobe’s recent acquisition of Semrush, how Adobe Brand Visibility was born from Semrush’s AI visibility product and Adobe’s LLM Optimizer, and how Adobe used a three-day internal hackathon instead of a large-scale infrastructure integration to quickly deliver value to customers.
Solving integration woes with a hackathon Read More »
When enterprises transition from using simple chat assistants to autonomous, agentic workloads, they quickly run into a hard truth: Agents are prone to inaccurate insights when working with directly raw tables. BigQuery Graph helps organizations move beyond flat, static tables to represent enterprises exactly how they exist in the physical world: as interconnected business entities
Using BigQuery Graphs with measures for trusted agentic workloads Read More »
Tracking generative AI costs is a common challenge when multiple teams share a single foundation model through Amazon Bedrock. Your HR team answers policy questions, Accounting analyzes financial documents with it, and IT troubleshoots infrastructure issues. All three use the same foundation model. But usage shows up as one line item on the bill. As
Track generative AI costs with Amazon Bedrock inference profiles Read More »
Data residency requirements can affect how government agencies, regulated industries such as financial services, healthcare, and power and utilities, and businesses that make residency commitments plan for the recovery of their critical workloads on AWS. These requirements must be considered and balanced against applicable workload recovery objectives. This post assumes familiarity with AWS Regions, disaster
Recovery strategies to meet data residency requirements Read More »
If your Text2SQL system takes 25-30 seconds to respond, user engagement drops significantly. For teams scaling beyond pilot projects, this latency gap between a working demo and a production-ready tool is the biggest barrier to adoption. Without caching, every question triggers a Large Language Model (LLM) call to generate SQL, and those calls introduce challenges:
Reducing Text2SQL latency with parameterized query templates Read More »
Adobe has used Amazon Web Services (AWS) since 2008. Adobe Firefly powers creative features across applications including Photoshop and Illustrator. Adobe operates a GPU-based training infrastructure built on Amazon Elastic Kubernetes Service (Amazon EKS) to support Firefly. The infrastructure enables teams to run model training jobs across thousands of compute nodes and GPUs, designed to
Adobe Firefly: Simplified observability with Amazon Managed Prometheus Read More »
Most multi-agent systems today follow the same shape: a supervisor agent breaks a task down, hands the pieces to subagents, and stitches the results back together. This is how Kiro CLI delegates to subagents, and what the Strands Agents SDK gives you primitives for with graphs and agents-as-tools. It is a good default. One process
Scaling patterns for self-organizing multi-agent clusters with Kiro Read More »
For organizations deploying AI agents at scale, there’s often a critical divide between structured and unstructured data. While large language models (LLMs) excel at parsing text documents, emails, and PDFs, they can struggle when presented with raw enterprise databases. Meanwhile, standard natural-language-to-SQL (NL2SQL) models often guess how database schemas fit together, which can lead to
Looker’s semantic layer governs Gemini Enterprise data for user trust Read More »
