AWS Lambda

Headlines · 7

1. 3d
   AWS details a nine-stage AI agent evaluation framework on Bedrock AgentCore

   An AWS Machine Learning Blog post describes how AWS's Sales, Marketing, and Global Services (SMGS) Operations team built a continuous quality-evaluation framework for a production multi-agent system on Amazon Bedrock AgentCore Evaluations. The framework spans nine lifecycle stages—defining quality, experimentation, CI merge checks, CD production gates, canary scoring, adversarial testing, live drift detection, a real-time pulse, and an automated feedback loop—and runs at three levels at once: tools, individual sub-agents, and multi-agent orchestration, each with its own golden set and promotion criteria. AWS reports that its internal sales agents produced fluent but wrong answers such as labeling $410.08 of three-month revenue as "$410.08 Million" and presenting estimates as sourced facts while conventional monitoring stayed green; in one pre-production test, mismatched data, tool, and prompt changes dropped the pass rate from 78 percent to 38 percent.

   AWS Machine Learning Blog · 🔥 0
2. 5d
   Build self-improving document processing with Amazon Bedrock AgentCore

   AWS published a technical guide for building an agentic intelligent document processing (IDP) system on Amazon Bedrock AgentCore, in which eight specialized agents collaborate across two graph workflows built with the open-source Strands Agents SDK. A new document type goes through a training workflow — an Analyzer extracts text with Amazon Textract, a Matcher searches Amazon S3 Vectors, an Instructions agent generates extraction prompts, an Extractor produces structured JSON and a Validator checks it against schemas and business rules in Amazon Aurora DSQL — while documents matching a known layout skip prompt generation in an automation workflow. In the sample purchase-order scenario, the first run took 3 minutes 49 seconds and a repeat document took 1 minute 30 seconds, and AWS estimates about $58 per month for 10 training plus 1,000 processed documents in US East (N. Virginia), with model inference roughly 85 percent of that cost. AWS cites IDC figures that document-related challenges cost organizations $19,732 per information worker annually and cut productivity by 21.3 percent.

   AWS Machine Learning Blog · 🔥 0
3. 5d
   AWS tutorial: build a slide-generating agent on Amazon Bedrock AgentCore

   An AWS Machine Learning Blog post walks through building a system that turns quarterly reports, compliance documents and market analyses into PowerPoint decks using Amazon Bedrock AgentCore. A single supervisor agent runs on AgentCore runtime and uses the Strands Agents framework to route requests between a document-generation tool and a summarization tool, with a RAG pipeline built on Amazon Bedrock Knowledge Bases, Amazon OpenSearch Serverless and Amazon Titan Text Embeddings v2. Anthropic Claude Sonnet 4.6 writes the content, Stability AI Stable Image Inpaint creates slide images, and a .NET 8 minimal API exposes the workflow to existing enterprise applications; AWS says the flow finished in under 90 seconds in its internal testing, and a public code repository is included.

   AWS Machine Learning Blog · 🔥 0
4. 5d
   Tamnoon builds Tami, an agentic cloud remediation orchestrator on Amazon Bedrock

   An AWS Machine Learning Blog post describes how Tamnoon built Tami, an agentic cloud remediation orchestrator, on Amazon Bedrock. Tami ingests findings from CNAPP and cloud-native tools such as Wiz, CrowdStrike and AWS Security Hub, then uses four specialized agents — investigation, safety scoring, remediation and code generation — to produce fixes rated SAFE, RISKY or AWAITING DATA, with Bedrock Guardrails, a generate-and-review loop and human approval gating execution. Tamnoon says the approach cut critical cloud exposure by more than 90% in 90 days with zero production issues; those figures are the vendor's own and have not been independently verified.

   AWS Machine Learning Blog · 🔥 0
5. 6d
   Automating Amazon Bedrock private pricing entitlements

   AWS published a walkthrough on using managed entitlements to handle Amazon Bedrock third-party model access across many AWS accounts, so organizations subscribe once from a central account and distribute access via AWS License Manager. It offers two paths: a lightweight one-time script that lists received licenses in a dry run and activates organization-wide grants with --apply, and an event-driven AWS CDK stack that listens for AWS Marketplace agreement events via Amazon EventBridge, verifies a seller allow-list in Lambda, then creates and activates distributed grants with logging to CloudWatch. AWS notes that grant activation and billing visibility can take hours or days to appear.

   AWS Machine Learning Blog · 🔥 0
6. 6d
   ShipStation's Deckhand AI IT help desk on Bedrock AgentCore resolved 97.5% of queries

   ShipStation Global's engineering team built Deckhand, an AI IT help desk agent that lives in Slack and runs on Amazon Bedrock AgentCore, answering routine IT and company questions, reading error-message screenshots, and creating or updating Jira Service Management tickets. The agent uses the Strands Agents SDK with Anthropic's Claude on Amazon Bedrock, exposes classification, ticketing, user resolution and image analysis as MCP tools through AgentCore Gateway, and draws on a Bedrock knowledge base indexing Confluence articles, intranet pages and Slack history. According to the co-written AWS/ShipStation post, it resolved 97.5% of incoming channel queries with a 96% positive feedback rating over three months; a DSPy recursive language model (RLM) pipeline mines resolved Slack threads into verified self-service steps, and the bot is designed to stay silent during human-to-human or social chatter.

   AWS Machine Learning Blog · 🔥 0
7. Oct 7
   AWS playbook: six-week program turns non-engineers into AI agent builders

   In an AWS Machine Learning Blog post, Amazon Web Services describes a six-week program that pairs non-engineering business staff with mentors and production-grade tools—Kiro, Amazon Bedrock, Strands Agents SDK and AWS Lambda agents—for about four hours a week, ending in a working prototype. A team of four customer-facing employees with no engineering background built WealthWise, a multi-agent financial advisory prototype with five agents (portfolio analysis, risk assessment, financial planning and more), a dual-server Node.js and Python Flask architecture, four DynamoDB tables and, the team says, sub-5-second responses for complex reasoning; it took first place in the demo judging. AWS reports that self-rated “strong or expert” understanding of agentic AI rose from 27% to 82% among participants, and readiness to identify AI opportunities from 41% to 85%.

   AWS Machine Learning Blog · 🔥 0

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