# AWS
> Cloud computing platform providing infrastructure, storage, databases, networking, AI, analytics, security, and application services.

## Articles

### [Gallup scales real-time coaching for thousands with Amazon Bedrock](https://yomu.fyi/post/gallup-scales-real-time-coaching-for-thousands-with-amazon-bedrock.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Tamil Sambasivam
- Published: Aug 26, 2026

Gallup needed to centralize decades of proprietary workplace performance research to provide proactive, real-time coaching for organizational leaders directly within their daily workflows. To solve this challenge, Gallup developed Gallup AI inside its Gallup Access platform using Amazon Bedrock and Anthropic Claude models. The multi-tenant serverless architecture leverages Amazon Bedrock Knowledge Bases and Amazon Kendra for retrieval-augmented generation, while AWS Lambda with FastAPI powers real-time streaming responses. Amazon ElastiCache Serverless caches recent conversation history for sub-millisecond session retrieval, Amazon RDS for MySQL provides durable transactional storage, and Amazon Bedrock Guardrails enforce content safety. Since its June 2024 launch, the system has achieved sub-second time-to-first-byte streaming latency while prompt volume grew approximately sevenfold across thousands of organizations.


### [Closing the AI agent trust gap with graduated autonomy](https://yomu.fyi/post/closing-the-ai-agent-trust-gap-with-graduated-autonomy.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Dev Arora
- Published: Aug 26, 2026

Operating AI agents under binary full-access or read-only permissions creates an operational trust gap due to unpredictable model behavior across updates. The graduated autonomy architectural pattern closes this gap by dynamically granting or revoking tool permissions through sustained reliability scoring. A scoring engine computes a composite score across five weighted dimensions over a rolling 50-action window, mapping agents across four tiers from probation to full autonomy. In-process pre-execution filters check for prompt injection and sensitive targets, while Amazon Bedrock AgentCore Gateway and Cedar policies strictly enforce deny-by-default access at the infrastructure level. Post-execution state capture in Amazon DynamoDB supports reversibility, and AWS CodePipeline delivery gates block releases failing adversarial evaluation benchmarks.


### [How a global payment processor preserved AWS RAM shares and Lake Formation permissions during an AWS Organizations migration](https://yomu.fyi/post/how-a-global-payment-processor-preserved-aws-ram-shares-and-lake-forma.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Sam Mukherjee
- Published: Aug 24, 2026

A global payment processor migrating 382 AWS accounts between AWS Organizations encountered control-plane failures when AWS Resource Access Manager automatically severed organization-bound resource shares. Although data planes continued operating for shared resources such as AWS Transit Gateways, infrastructure automation lost the ability to modify dependencies like AWS Glue Data Catalog databases. The engineering team resolved this by implementing temporary retained bridge shares, establishing external principal associations that survived cross-organization account transfers. Following each account move, automated tooling restored durable original shares, validated Lake Formation permissions, and deleted the bridge copies to prevent configuration drift. This bridge-share workflow enabled the migration of 378 accounts before a Transitional Service Agreement deadline without customer downtime.


### [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore – Part 2](https://yomu.fyi/post/how-agentflo-built-ai-sales-agents-with-amazon-bedrock-agentcore-part-2.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Muhammad Musab Iqbal
- Published: Aug 21, 2026

AgentFlo designed an architecture using Amazon Bedrock AgentCore and AWS serverless infrastructure to address traffic spikes and build trust in autonomous commerce agents. Security operates across three stages: AWS Fargate filters prompt injections and opt-outs, AgentCore Gateway enforces deterministic Cedar authorization policies during tool execution, and post-turn privacy filters screen outputs. Session isolation is maintained across merchants through AgentCore runtime microVMs, while AgentCore Observability and Amazon CloudWatch provide end-to-end tracing. Data reliability and long conversation journeys rely on Amazon DynamoDB context storage, Amazon Bedrock Knowledge Bases, and Amazon S3 Vector embeddings. Early deployment data showed a 12 percent net revenue uplift.


### [How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock](https://yomu.fyi/post/how-clario-technology-detects-phi-pii-in-dicom-images-using-amazon-bed.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Alex Boudreau
- Published: Aug 19, 2026

Clario developed an automated PHI and PII detection solution on AWS to process thousands of DICOM image slices and PDF files in clinical trials. Medical imaging workflows require strict privacy compliance under HIPAA, GDPR, and ICH E6 guidelines across standard tags, custom vendor metadata, and pixel-burned text. To address this, the pipeline retrieves files from Amazon S3 and runs memory-intensive detection workloads on Amazon EKS backed by Amazon RDS for PostgreSQL. Amazon Textract handles optical character recognition, while Anthropic's Claude Sonnet on Amazon Bedrock classifies sensitive data using domain-optimized prompt engineering. The system identifies precise coordinates and sensitive entity types rather than directly altering files, enabling human-in-the-loop review before downstream redaction occurs.


### [AI-powered clinical trial eligibility and safety using Amazon Bedrock AgentCore](https://yomu.fyi/post/ai-powered-clinical-trial-eligibility-and-safety-using-amazon-bedrock.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Sachin Jain
- Published: Aug 19, 2026

Manual chart review across fragmented clinical sources slows clinical trial enrollment, contributing to timeline delays and high screen failure rates. To address this bottleneck, an architecture on AWS automates eligibility and safety assessments while preserving human clinician authority. AWS HealthLake ingests and normalizes records into FHIR R4 resources, while Amazon Bedrock AgentCore orchestrates pre-screening, detailed screening, and site enrollment agents. Amazon Bedrock AgentCore Evaluations scores screening determinations using an LLM-as-a-judge approach for clinical accuracy, operational effectiveness, and safety compliance. This human-in-the-loop workflow produces structured recommendations with source citations, reducing patient matching time from days to minutes while maintaining compliance audit trails.


### [Consistency is the new latency: AI at the data layer](https://yomu.fyi/post/consistency-is-the-new-latency-ai-at-the-data-layer.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Suman Chatterjee
- Published: Aug 18, 2026

Autonomous AI agents depend on the accuracy of the underlying data layer because retrieved data populates the LLM context window for multi-step reasoning. In distributed architectures, standard asynchronous replication lag causes agents to ingest stale data, leading to flawed execution and self-reinforcing hallucination debt when errors are saved back to memory. To resolve this, architectures must align replication strategies with specific task truth requirements across three primary patterns. High-stakes workloads utilize Amazon Aurora with global write forwarding or native Aurora DSQL for multi-region strong consistency, while high-scale scenarios leverage DynamoDB Global Tables with conditional writes to prevent lost updates. Finally, high-velocity ingestion pipelines enforce LOCAL\_QUORUM reads and writes in Amazon Keyspaces to capture critical data spikes reliably.


### [Serverless vehicle tracking at scale: Bosch L.OS on AWS](https://yomu.fyi/post/serverless-vehicle-tracking-at-scale-bosch-l-os-on-aws.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Yogish Kutkunje Pai
- Published: Aug 14, 2026

Bosch Mobility Platform Solutions developed the Logistics Operating System on AWS to unify vehicle tracking across fragmented spot logistics markets. The system standardizes multiple telematics providers, disparate data formats, and diverse communication protocols into a centralized visibility layer. Orchestration is handled by a Tracking Connector running on Amazon ECS with AWS Fargate, while AWS Lambda functions act as provider-specific adapters for protocol translation. Event-driven message ingestion and streaming rely on Amazon MSK, complemented by Amazon ElastiCache for low-latency caching and Amazon DynamoDB for compliance policy enforcement. Currently operational in India with ten integrated ISVs, the platform reportedly reduces integration costs by 15–20% and lowers total tracking costs for small transporters by 25–30%.


### [Track generative AI costs with Amazon Bedrock inference profiles](https://yomu.fyi/post/track-generative-ai-costs-with-amazon-bedrock-inference-profiles.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Erik Mack
- Published: Aug 13, 2026

When multiple departments share a single foundation model in Amazon Bedrock under one IAM role, usage aggregates into one billing line item, complicating department-level chargebacks and budget tracking. This issue can be resolved by deploying Amazon Bedrock application inference profiles wrapped around the shared foundation model and tagging each profile for cost allocation. The application layer determines the user department during authentication and passes the corresponding inference profile ARN as the model identifier. Once the designated cost allocation tag is activated in the AWS Billing and Cost Management console, usage separates into per-department line items in AWS Cost Explorer. This method incurs standard per-token model rates without extra charges for cost attribution.


### [Reducing Text2SQL latency with parameterized query templates](https://yomu.fyi/post/reducing-text2sql-latency-with-parameterized-query-templates.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Yury Brukau
- Published: Aug 13, 2026

In Text2SQL systems, generating SQL on the fly with frontier foundation models can cause response times of 25 to 30 seconds due to 60,000-token prompts and model latency. To solve this without degrading accuracy, an intelligent caching layer was built using parameterized SQL query templates paired with question embeddings. When an incoming question matches a cached template via semantic search above a confidence threshold, named entity recognition extracts values to populate template placeholders, bypassing the initial SQL generation step. In production, this approach reached a 60% cache hit rate after two weeks, cutting per-hit token consumption by roughly 90% and reducing overall latency by 80% to under 5 seconds.


### [Adobe Firefly: Simplified observability with Amazon Managed Prometheus](https://yomu.fyi/post/adobe-firefly-simplified-observability-with-amazon-managed-prometheus.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Dev Arora
- Published: Aug 13, 2026

Adobe Firefly powers generative creative features using a large-scale GPU training infrastructure hosted on Amazon Elastic Kubernetes Service. As workloads scaled to thousands of nodes and GPUs, Adobe's self-managed Prometheus setup struggled with query latency across high-cardinality telemetry data. To resolve these performance bottlenecks, the team moved two million critical time series metrics to Amazon Managed Service for Prometheus while using managed scrapers alongside their existing infrastructure. This setup accelerated query response times by more than 28x over 24-hour windows, reducing query execution times to approximately ten seconds. Additionally, the migration expanded practical monitoring windows from six hours to 24 hours, giving teams reliable visibility across long-running distributed training jobs.


### [Scaling patterns for self-organizing multi-agent clusters with Kiro](https://yomu.fyi/post/scaling-patterns-for-self-organizing-multi-agent-clusters-with-kiro.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Ivo Kammerath
- Published: Aug 11, 2026

Traditional multi-agent architectures rely on a central supervisor agent to decompose tasks and aggregate results, creating single points of failure and context-window throughput bottlenecks. To address these scaling limits on decomposable workloads, kiro-flock implements self-organizing clusters of headless Kiro CLI agents on Amazon EC2 coordinated entirely through shared Amazon S3 state. Agents operate in bounded peer topologies such as logical rings, reading neighbor append-only logs and generating artifacts without direct inter-agent messaging or central brokers. Each agent starts fresh per iteration without conversational memory, reading current direction and peer logs to prevent drift and preserve steering control. This shared-state pattern enables resilient parallelism for tasks requiring diverse independent contributions, such as code reviews or codebase migrations.


### [Building a serverless AI assistant at Pelago: concept to care in two weeks](https://yomu.fyi/post/building-a-serverless-ai-assistant-at-pelago-concept-to-care-in-two-we.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Anton Aleksandrov
- Published: Jul 22, 2026

Pelago engineered a serverless, event-driven AI assistant on AWS within two weeks to provide substance use recovery coaches with contextual response suggestions. Operating under strict Protected Health Information constraints, the system maintains clinical safety through human-in-the-loop review and keeps data confined to an Amazon VPC. Incoming messages flow via AWS AppSync and Amazon DynamoDB to an Amazon SNS topic, which fans out to independent AWS Lambda functions. The asynchronous Chat Assistant Lambda queries history, invokes Amazon Bedrock, and persists suggestions into Amazon RDS MySQL in under 10 seconds. When coaches access a conversation via Amazon API Gateway, pre-generated suggestions load in under 100 milliseconds.


### [Building multi-Region resiliency for AWS CloudFormation custom resource deployment](https://yomu.fyi/post/building-multi-region-resiliency-for-aws-cloudformation-custom-resourc.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Raman Pujani
- Published: Jul 22, 2026

AWS CloudFormation custom resources extend template provisioning capabilities but lack built-in multi-Region coordination, risking duplicate executions and unhandled failures during regional outages. To solve this limitation, an active-active architecture leverages Amazon Simple Notification Service topics to fan out lifecycle events across primary and secondary infrastructure Regions. The primary Region processes events immediately, whereas the secondary Region processes incoming messages after a deliberate delay implemented via Amazon Simple Queue Service. Coordination relies on Amazon DynamoDB Global Tables with conditional writes to manage distributed locking and enforce idempotency. When failures occur in the primary Region, Amazon CloudWatch alarms signal Amazon Application Recovery Controller to automate failover without manual intervention.


### [Architecting offline-first generative AI applications for edge deployments using AWS services](https://yomu.fyi/post/architecting-offline-first-generative-ai-applications-for-edge-deploym.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Ahmed Ewis
- Published: Jul 22, 2026

Industrial edge environments often face significant unplanned downtime yet lack reliable cloud connectivity to run large-scale artificial intelligence models. To address this limitation, an offline-first reference architecture shifts generative inference to edge devices while leveraging cloud services for preparation and lifecycle management. The workflow uses Amazon Bedrock with Amazon Nova Pro to create labeled dataset pairs, Amazon SageMaker AI Pipelines to fine-tune a compact model, and AWS IoT Greengrass to orchestrate edge deployments. Local execution relies on Ollama, Strands Agents, and a CPU-based ChromaDB retrieval pipeline that consumes zero GPU VRAM. In evaluations across three foundation model judges, the fine-tuned gpt-oss-20b model combined with retrieval-augmented generation consistently outperformed the base model configuration across accuracy, completeness, and relevance metrics.


### [Automate custom PII detection at scale with Amazon Macie and Step Functions](https://yomu.fyi/post/automate-custom-pii-detection-at-scale-with-amazon-macie-and-step-func.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Aishwariya Khiani
- Published: Jul 22, 2026

Regulated organizations ingesting large data volumes must detect and classify standard and domain-specific personally identifiable information to satisfy compliance mandates like GDPR, HIPAA, CCPA, and PCI DSS. To eliminate manual inspection, an event-driven architecture orchestrates Amazon Macie scans for files landing in Amazon S3. Amazon EventBridge captures upload events and invokes AWS Step Functions, which manages Lambda functions to stage objects, trigger Macie classification jobs with custom regex identifiers, and poll for results. The pipeline separates data across a three-bucket pattern representing raw, staged, and scanned states to prevent mixing unprocessed data with validated assets. Finally, the workflow exports timestamped compliance reports in CSV and JSON formats while alerting security teams via Amazon SNS on high-severity findings.


### [Eclipse Dataspace Components on AWS: Cost optimization strategies](https://yomu.fyi/post/eclipse-dataspace-components-on-aws-cost-optimization-strategies.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Jorge Hernández Suárez
- Published: Jul 17, 2026

Deploying Eclipse Dataspace Components (EDC) connectors on AWS presents challenges in forecasting infrastructure expenses without clear benchmarks. Estimated costs differ substantially between business-critical setups and non-critical environments based on compute and database sizing. A business-critical configuration relying on Amazon Aurora PostgreSQL db.r6g.large and standard Amazon ECS with AWS Fargate totals an estimated 387.00 USD monthly in the eu-south-2 region. Sizing down to db.t4g.medium and switching to AWS Fargate Spot lowers the non-critical monthly expense to 164.00 USD, representing a 58% reduction. Core cost drivers remain baseline infrastructure components, while storage, API calls, and data transfers contribute marginally under baseline assumptions.