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COMPANY · ENTITY #4009

Retrieval Augmented Generation (RAG)

Related event timeline, sources and context from the news index.

EVENT TIMELINE

4

CODING · 1 SOURCE · InfoQ AI, ML & Data Engineering

InfoQ outlines evolution of agentic harnesses from chains to graphs to code

An InfoQ article summarizes three paradigms for building agentic harnesses around LLMs — chains (linear workflows), graphs (explicit control-flow with nodes/edges), and code (sandboxed, execution-first systems) — and argues the field has moved from ad‑hoc glue to production-grade, observable architectures. The piece highlights tradeoffs (latency, fragility, resilience) and recommends treating LLM outputs as untrusted, decoupling planning from execution, and investing in observability and minimal necessary abstractions.

6.0

CODING · 1 SOURCE · AWS Machine Learning

Amazon Bedrock AgentCore demo automates PowerPoint creation using Claude Sonnet 4.6

AWS published a how-to showing an AI agent system built on Amazon Bedrock AgentCore that automates generating client PowerPoint presentations from enterprise documents. The solution uses a supervisor agent (AgentCore runtime + Strands Agents) with Anthropic Claude Sonnet 4.6 for content, Amazon Titan Text Embeddings v2 and Amazon OpenSearch Serverless for RAG vector search, Stability AI Stable Image Inpaint for images, a .NET 8 REST API and AWS Lambda for generation, and a public code repository; internal testing completed in under 90 seconds.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Amazon Bedrock: multi-account AI agent architecture using AgentCore Gateway and MCP

Amazon’s post describes a reference architecture for building multi-account AI agents that query data in-place across multiple AWS line-of-business (LOB) accounts without centralizing datasets. It shows how a central platform account can host agents and Bedrock inference while AgentCore Gateway (with MCP, AgentCore Identity, Policy in AgentCore/Cedar, and optional Okta auth) aggregates LOB MCP servers, provides unified tool discovery, authorization, observability, and applies Bedrock guardrails and RAG-backed knowledge retrieval.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS outlines vector-store choices for Amazon Bedrock Knowledge Bases (OpenSearch, Aurora pgvector, S3 Vectors)

AWS published guidance for the customer-managed configuration of Amazon Bedrock Knowledge Bases, comparing three supported vector-store backends—Amazon OpenSearch Service (managed and serverless), Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors—and evaluating their tradeoffs across RAG use cases (latency, cost, scalability). The post maps each backend to example workloads such as e-commerce product search and discusses performance and cost implications.

6.0