Tech Meridian ← ENTITY INDEX
PROMY MERIDIAN RU

COMPANY · ENTITY #3264

AWS Lambda

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

EVENT TIMELINE

19

MODELS · 1 SOURCE · AWS Machine Learning

Fleet-wide edge computer-vision pipeline sample using Amazon SageMaker Python SDK v3 and Neo

AWS published a sample (aws-samples GitHub) updating its 2023 visual-inspection pattern to the Amazon SageMaker Python SDK v3, replacing the deprecated SageMaker Edge Manager with SageMaker Neo. The dual-branch SageMaker Pipeline trains a per-camera anomaly autoencoder and a ResNet18 classifier, evaluates models through quality gates, compiles approved artifacts with SageMaker Neo to produce device-optimized builds (ONNX + Neo-compiled library), and uses Step Functions, an SQS FIFO queue, a DynamoDB semaphore, Lambda, SNS, and EventBridge for fleet orchestration and paced retraining; deployment is via AWS CDK and a full synthetic run costs about $0.07 in compute.

6.0

CODING · 1 SOURCE · AWS Machine Learning

CDK construct orchestrates SageMaker endpoint auto scaling during updates

AWS describes a reusable AWS CDK construct that replaces native Application Auto Scaling resources with three CloudFormation custom resources to deregister and then re-register auto scaling around SageMaker real-time endpoint updates. The approach (Deregister, Endpoint update, Register Scalable Target, Attach Scaling Policy) uses asynchronous custom-resource handlers, a ConfigHash to force re-invocation, and provider-generated Lambda and Step Functions helpers so endpoint configuration and auto scaling parameters can be updated in a single deployment without manual steps or broken CloudFormation stacks.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS Agent Registry enables runtime discovery of MCP servers and agent skills

AWS describes AWS Agent Registry, a managed catalog for registering and semantically searching Model Context Protocol (MCP) servers, agents, and agent skills so orchestrators can discover and instantiate tools at runtime rather than hardcoding endpoints. The post includes a Python reference that wires Amazon Bedrock AgentCore Gateway, AgentCore runtime A2A agents, and an orchestrator that searches the registry, constructs clients from descriptors, and invokes capabilities (authentication must be obtained separately).

6.0

CODING · 1 SOURCE · AWS Machine Learning

Build agentic intelligent document processing on Amazon Bedrock AgentCore with Strands Agents

AWS published a how-to for an agentic intelligent document processing (IDP) solution that uses Amazon Bedrock AgentCore and the open-source Strands Agents SDK to classify, extract, validate, and self-learn from business documents. The architecture ties AgentCore runtime and identity capabilities to AWS services (Textract, S3 Vectors, Aurora DSQL, Lambda, EventBridge, DynamoDB, ECS) in multi-agent workflows so new document formats can be processed and reused with minimal manual template configuration.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Amazon Bedrock managed entitlements: script and CDK to activate AWS Marketplace private pricing

AWS published guidance and sample code to simplify activating AWS Marketplace Private Pricing Offers (MPPOs) for Amazon Bedrock across multiple accounts. It provides two implementation paths: a lightweight one-off script that lists received licenses and can create/activate organization-wide grants, and an event-driven AWS CDK stack that listens for Marketplace agreement events, verifies sellers, creates distributed grants, and requests activation automatically.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

ShipStation Global builds 'Deckhand' AI IT help desk on Amazon Bedrock AgentCore

ShipStation Global built Deckhand, an AI-powered IT help desk agent for Slack, using Amazon Bedrock AgentCore and Anthropic’s Claude. Deckhand draws on a consolidated Bedrock-indexed knowledge base, reads error screenshots, and creates/updates Jira Service Management tickets, reportedly resolving 97.5% of incoming channel queries with a 96% positive employee feedback rating.

6.0

CODING · 1 SOURCE · AWS Machine Learning

Automate remediation after AWS DevOps Agent investigations using Lambda Durable Functions and Amazon Bedrock

AWS describes a workflow that connects AWS DevOps Agent investigation events to an automated remediation pipeline: EventBridge triggers a Lambda Durable Functions orchestrator that sends findings to Amazon Bedrock, which selects from an allowlisted set of purpose-built Lambda tools to propose and (for read-only actions) execute fixes or (for mutating changes) prepare pre-validated changes awaiting one-click human approval. The design uses Durable Functions checkpoints to pause for approvals and iteratively call Bedrock until remediation completes, aiming to lower mean time to resolution (MTTR).

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Six-week playbook helps non-engineers build multi-agent AI prototypes with Amazon Bedrock and Strands

A six-week structured program paired business professionals with mentors and production-grade tools (Amazon Bedrock AgentCore, Strands Agents SDK, DynamoDB, Node.js/Python Flask) to close the gap between AI knowledge and hands-on capability; participants built WealthWise, a multi-agent financial advisory prototype using Amazon Nova models and won first place. The article lays out the program structure, barriers encountered, and five principles that supported rapid learning and prototype delivery.

4.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes tutorial to build a voice travel concierge with Bedrock AgentCore and Nova 2.5 Sonic

AWS published a how-to demonstrating a voice travel concierge built with Amazon Bedrock AgentCore (runtime and Gateway), Amazon Nova 2.5 Sonic for real‑time speech, and Amazon Bedrock Knowledge Bases, using the AWS CDK and the Model Context Protocol (MCP) to connect the agent to backend services. The post details architecture, components (authentication, data stores, Lambdas, API Gateway), and a sample airline implementation that keeps the agent loosely coupled to existing systems.

5.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes 'Adjudicated Query' pattern for provable lease compliance using Amazon Quick

AWS demonstrates an "Adjudicated Query" design pattern and reference architecture that combines a generative-AI chat surface (Amazon Quick) with a deterministic, versioned rules engine to run auditable, provably complete compliance sweeps (example: tens of thousands of leases). The architecture separates conversational translation/narration (model) from pass/fail determinations (rules engine), emits a completeness receipt, and shows an end-to-end deployable sample using MCP, Amazon Bedrock, Cognito, API Gateway, Lambda, and Aurora Serverless v2.

6.0

CODING · 1 SOURCE · AWS Machine Learning

Amazon Bedrock AgentCore guide for ambient agents with event-driven, human-in-the-loop workflows

AWS published a walkthrough and reference implementation showing how to build "ambient" agents on Amazon Bedrock AgentCore that react to event streams (S3, EventBridge, scheduled tasks), pause for human approval via a single ask_human tool, and resume—using AgentCore Runtime plus serverless AWS components (Lambda, DynamoDB, SQS) and Bedrock foundation models. The post includes a sample deployment path requiring AWS CLI, CDK v2, Docker, and an ECR-hosted agent container, and notes session timeouts align with Lambda's 15-minute limit in the reference.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Aderant deploys Intelligent Ticket Analyzer using Amazon Nova Lite via Bedrock

Aderant built an Intelligent Ticket Analyzer that uses Amazon Nova Lite through Amazon Bedrock to automate context gathering, classification, routing, and knowledge enrichment for support tickets across 268 client environments. In the first 2.5 weeks (June 30–July 17, 2026) it reviewed 109 tickets with ~96% routing accuracy, estimates saving 8–14 engineering hours per week, and ran at under $30/month operating cost.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

HEMA builds internal assistant HAL using Model Context Protocol and Amazon Bedrock AgentCore

Dutch retailer HEMA created HAL, an internal AI assistant that consolidates fragmented internal knowledge and delivers answers inside existing tools by exposing sources as MCP tools and running agents on Amazon Bedrock AgentCore. Identity is anchored with Microsoft Entra ID, and AgentCore features (Gateway, Runtime, Memory, Guardrails) were used to avoid custom MCP server infrastructure.

5.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS shows how to feed Amazon Bedrock Data Automation outputs into Salesforce Agentforce via MCP

An AWS blog post outlines an architecture that uses Amazon Bedrock Data Automation and the Model Context Protocol (MCP) to process unstructured evidence (video, audio, images, documents) stored in Amazon S3 and make structured insights available to Salesforce Agentforce inside the Salesforce console. The pattern uses S3 events, AWS Lambda, DynamoDB and EventBridge, and routes agent queries through Amazon Bedrock AgentCore Gateway to retrieve processed outputs via MCP.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

BMW’s CLEA uses Prophet to detect cost anomalies across 14,000 cloud accounts

BMW Group’s in-house FinOps system, Cloud Efficiency Analytics (CLEA), ingests billing exports (including AWS CUR) and builds per-account-service daily forecasts with Prophet to run daily anomaly detection across roughly 14,000 cloud accounts. The serverless pipeline (Step Functions, Lambda, S3, Distributed Map) processes hundreds of thousands of account-service time series in about 20 minutes and alerts owners when actual spend departs from the forecast; the team reports the compute run costs around $50/month.

4.0

COMPANIES · 1 SOURCE · AWS Machine Learning

EXL launches AI-powered Medical IDP solution on AWS

EXL has combined its Xtrakto.AI intelligent document processing application with a domain-tuned EXL Insurance LLM and deployed the pipeline on AWS (using Amazon SageMaker AI and Amazon Bedrock among other services) to extract, summarize, and query medical records at enterprise scale, aiming to cut claims and underwriting review time. The end-to-end solution runs within an AWS Region and uses AWS services for ingestion, orchestration, OCR, model training/inference, and secure access control.

5.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS publishes serverless Git-metrics dashboard solution using Amazon QuickSight

AWS released a serverless, event-driven pipeline that automatically collects Git metrics from GitHub and GitLab, stores results in Amazon S3, and visualizes analytics with Amazon QuickSight. The solution uses EventBridge Scheduler, Step Functions, Lambda, and CloudFormation parameters, and includes change detection, adaptive chunking, and full/incremental loads to provide near-real-time engineering analytics and support observability in the AI-Driven Development Lifecycle framework.

3.0

CODING · 1 SOURCE · AWS Machine Learning

Amazon Quick outlines multi-gate authorization for MCP tool invocations

An Amazon blog post provides a walkthrough to implement defense-in-depth authorization for Model Context Protocol (MCP) tool invocations on Amazon Quick. It describes a Lambda ‘interceptor’ behind an Amazon Bedrock AgentCore Gateway that evaluates OIDC JWT claims through four sequential gates (MFA, geographic restriction, group-to-role mapping, and tool-level permission checks) and shows how to configure Microsoft Entra ID, environment variables, and AWS components to enforce role- and attribute-based controls and parameter-level restrictions.

5.0

CODING · 1 SOURCE · AWS Machine Learning

Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation

AWS outlines a recipe for automating end-to-end PII detection and redaction at scale using Amazon Bedrock Data Automation (BDA) custom blueprints combined with a serverless batch pipeline built on AWS Step Functions and Lambda. The post walks through designing blueprint field scopes (example: Attending Physician Statements), extracting field content with bounding boxes and confidence scores, and applying transformations for precise redaction.

5.0