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[AWS Service Health Dashboard] Service disruption: Increased Error Rates
We are providing an update on the ongoing service disruptions affecting the AWS Middle East (UAE) Region (ME-CENTRAL-1). The overall state of the region remains largely unchanged from our previous update. We continue to work closely with local authorities and are prioritizing the safety of our personnel throughout our recovery efforts. Teams continue to assess the damage to the affected facilities and are working to restore infrastructure impacted by the event. With respect to Amazon S3, we are seeing improvement in PUT and LIST availability. We continue to work on improving GET error rates, but full recovery will be dependent on restoring the affected infrastructure, which our teams continue to work toward. For Amazon DynamoDB, error rates remain elevated and our teams continue to focus on recovery efforts. We have not yet seen meaningful improvement in DynamoDB availability, but expect conditions to improve over the coming hours as recovery work progresses. Amazon EC2 instance launches remain throttled in the ME-CENTRAL-1 Region. We will begin relaxing these throttles as soon as we have fully recovered our foundational services and have sufficient capacity to support new launches safely. The AWS Management Console is now operational, though customers may continue to experience errors on certain pages and operations as the underlying services work through their recovery. We recommend customers continue to retry requests where possible. AWS Lambda, Amazon Kinesis, Amazon CloudWatch, Amazon RDS, and a number of other AWS services that were impacted by this event remain degraded. The availability of these services is dependent on the recovery of our foundational services — primarily Amazon S3 and Amazon DynamoDB — and we expect to see improvement across these services as that recovery progresses. Finally, even as we work to restore these facilities, the ongoing conflict in the region means that the broader operating environment in the Middle East remains unpredictable. We strongly recommend that customers with workloads running in the Middle East take action now to migrate those workloads to alternate AWS Regions. Customers should enact their disaster recovery plans, recover from remote backups stored in other regions, and update their applications to direct traffic away from the affected regions. For customers requiring guidance on alternate regions, we recommend considering AWS Regions in the United States, Europe, or Asia Pacific, as appropriate for your latency and data residency requirements. We will continue to provide updates as recovery progresses and as the situation evolves. Our next update will be provided by 5:00 AM PST on March 3, or sooner if new information becomes available.
View Source[AWS What's New] AWS Elastic Disaster Recovery is now available in six additional AWS Regions
AWS Elastic Disaster Recovery (AWS DRS) is now available in six additional AWS Regions: Asia Pacific (Bangkok), Asia Pacific (Malaysia), Asia Pacific (New Zealand), Asia Pacific (Taipei), Canada West (Calgary), and Mexico (Central). Elastic Disaster Recovery is the recommended service for disaster recovery to AWS. It helps minimize downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery. With Elastic Disaster Recovery, you can recover your applications on AWS from physical infrastructure, VMware vSphere, Microsoft Hyper-V, and cloud infrastructure. You can also use Elastic Disaster Recovery to recover Amazon EC2 instances in a different AWS Region. AWS DRS replicates and recovers a wide range of applications, including critical databases such as Oracle, MySQL, and SQL Server, and enterprise applications such as SAP. AWS DRS uses a unified process for drills, recovery, and failback, so you do not need application-specific skillsets to operate the service. With this launch, Elastic Disaster Recovery is now available in 36 AWS Regions. See the AWS Regional Services List for the most up-to-date availability information. To learn more about AWS Elastic Disaster Recovery, visit our product page or documentation. To get started, sign in to the AWS Elastic Disaster Recovery Console.
View Source[AWS What's New] Amazon MSK Express brokers now deliver data to streaming tables for Apache Iceberg
Amazon MSK Express brokers now deliver data to streaming tables for Apache Iceberg, a new capability that continuously materializes Apache Kafka topics as Apache Iceberg tables on Amazon S3 Tables. Amazon MSK data delivery to streaming tables can reduce the cost of ingesting and delivering Apache Kafka data into Amazon S3 Tables by up to 60% versus self-managed deployments and reduces downstream query costs by up to 30% versus self-managed Apache Kafka deployments. Customers rely on Apache Kafka to ingest real-time data for use cases like fraud detection and personalization and increasingly want to unify that data with Apache Iceberg tables for near real-time analytics but integrating the two forces them to operate complex custom pipelines, manage format conversions, and contend with the small-file problem, where high-volume ingestion creates many small parquet files that slow downstream queries and increase costs. With this capability, intelligent inline compaction eliminates the performance impact of small files and keeps query performance predictable without sacrificing data freshness, while built-in coordination resolves concurrent writer conflicts across high-throughput consumers. Amazon MSK supports throughput of up to 10 GB/s for delivery to Apache Iceberg on Amazon S3 Tables, and because this native capability adds no broker egress throughput, customers avoid the incremental infrastructure costs of scaling connector pipelines and match capacity to actual demand rather than peak. Customers deliver data to streaming tables and query or transform the data with any engine of their choice, including Apache Spark, Trino, or Apache Flink. To get started, customers open the Amazon MSK console, select the Express cluster, and enable the capability in a few clicks, or use the MSK APIs or MCP server. Amazon MSK data delivery to streaming tables is available today in every AWS Region where Amazon MSK Express brokers are offered. For pricing information, visit the pricing page. To learn more, visit the Amazon MSK Developer Guide and Amazon MSK AI skills.
View Source[AWS What's New] OpenAI GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock
GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss. With a context window size of 1 million tokens, you can analyze entire repositories in a single pass for code review and migration, process long-form legal or regulatory documents end-to-end, and maintain full conversation history in multi-step agentic workflows. Prompt caching with explicit cache breakpoints applies to long context requests, so repeated context is billed at a caching discount. GPT-5.6 Sol is available in the following AWS Regions: US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Get started with Sol, Terra, and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.
View Source[AWS What's New] AWS Organizations now provides maximum account quota visibility in Service Quotas
AWS Organizations customers can now view their maximum number of accounts quota and its utilization directly through AWS Service Quotas instead of relying on AWS Support or AWS account teams to determine their current account limit. Customers can now proactively plan account growth with ease by monitoring their current account quota utilization and requesting increases before reaching their limit. They can view this quota by logging into management account and accessing the Service Quotas console or calling the Service Quotas GetServiceQuota API. This quota visibility is available now available in US East (N. Virginia). To learn more, see viewing service quotas in the AWS Service Quotas documentation. For more information about AWS Organizations quotas and service limits, see the AWS Organizations documentation.
View Source[AWS What's New] Amazon Bedrock AgentCore now delivers unified observability with traces and logs in a single log group
Amazon Bedrock AgentCore now delivers agent traces and prompts to the same log group as your agent's logs, giving you unified observability for AI agents in a single Amazon CloudWatch log group. Previously, AgentCore split agent telemetry across multiple destinations trace spans went to the shared `aws/spans` log group while event logs containing prompts, inputs, and outputs went to a separate resource-specific log group. This meant debugging an agent invocation required searching across multiple log groups, and customers could not apply fine-grained access control or customer-managed key (CMK) encryption at the individual agent level. With today's launch, all of an agent's telemetry traces, prompts, structured logs, and standard output is delivered to a single per-agent log group (`/aws/bedrock-agentcore/runtimes/<agent_id>-<endpoint_name>`). You can now correlate traces and logs in one place, scope IAM policies and CMK encryption to individual agents, and export all telemetry by subscribing to a single log group. For multi-agent systems, each agent's complete execution history stays together, making end-to-end debugging straightforward. All newly created agents starting July 20, 2026 in supported AWS Regions use unified observability by default starting no configuration needed. For existing agents, set the `UNIFIED_TRACES_DESTINATION_ENABLED=true` environment variable on your agent runtime and upgrade ADOT to version 0.17.1 or later. This feature is available in all AWS commercial regions where AgentCore runtime is supported. Learn more in the AgentCore Developer Guide.
View Source[AWS What's New] AWS Transform continuous modernization is now generally available
AWS Transform continuous modernization is now generally available in all AWS Regions where AWS Transform is supported. This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. Teams can connect GitHub organizations, GitLab groups, and Bitbucket workspaces, run analyses on demand or on a recurring schedule, and prioritize findings across technical debt, security, agentic readiness, modernization readiness, and custom analysis criteria. With today's launch, you can connect source code providers, initiate and schedule analyses, review findings, and create remediations directly from the AWS Transform web application. For findings with an associated remediation, continuous modernization creates branches and opens pull requests or merge requests containing validated code changes for review. Analysis and remediation run in your AWS account using your credentials, while your source code remains under your control. You can also use the AWS Transform Kiro Power, agent plugins, or AWS Transform CLI to work from your IDE or terminal, analyze local repositories, organize repositories using labels, and run analyses locally or remotely using Amazon EC2 or AWS Batch. To get started, open the AWS Transform web application or use the AWS Transform Kiro Power and agent plugins. To learn more, see AWS Transform continuous modernization in the AWS Transform User Guide.
View Source[AWS What's New] Amazon EC2 M8id instances are now available in Europe (Ireland) region
Amazon EC2 M8id instances are now available in (Ireland). These instances are powered by custom Intel Xeon 6 processors and deliver up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation M6id instances. M8id instances offer up to 384 vCPUs, 1.5TiB of memory, and 22.8TB of NVMe SSD storage, 3x more than previous generation instances. These instances deliver up to 46% higher performance for I/O intensive database workloads, and up to 30% faster query results for I/O intensive real-time data analytics than previous sixth-generation instances. Additionally, these instances support Instance Bandwidth Configuration, allowing 25% flexible allocation between network and EBS bandwidth, allocating resources optimally for each workload. M8id instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 instance type page.
View Source[AWS What's New] Amazon EC2 M8a instances now available in the Asia Pacific (Hyderabad) region
Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Asia Pacific (Hyderabad) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances. M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.
View Source[AWS What's New] Amazon EKS now supports EFA and placement groups on Amazon EKS Auto Mode and Karpenter
Amazon Elastic Kubernetes Service (EKS) now supports Amazon EC2 placement groups and Elastic Fabric Adapter (EFA) network device configuration for node pools on EKS Auto Mode and the open-source Karpenter project, enabling you to optimize EKS workloads for performance and availability. These capabilities allow you to control EFA network interface configuration and how EC2 instances are physically distributed across AWS infrastructure for distributed training and inference workloads. With EKS Auto Mode and Karpenter’s EFA configuration, you can configure network interfaces as EFA-only or standard ENI on EFA-capable instances with both dynamic and static capacity node pools. EFA-only interfaces do not consume IP addresses, giving you fine-grained control over IP utilization in your VPC while achieving full interconnect bandwidth. With placement group support, you can launch EC2 instances using cluster, spread, or partition strategies directly from your EKS Auto Mode or Karpenter node pool configuration, giving you control over how instances are physically distributed without additional operational workarounds. Together, these capabilities let you optimize for the performance, availability, and fault isolation characteristics your workloads require, whether that's maximizing throughput for distributed training jobs or minimizing blast radius for critical production services. These features are available in all AWS Regions where Amazon EKS is available. To get started and learn more, see the EKS Auto Mode User Guide and Karpenter documentation.
View Source[AWS What's New] AWS Lambda durable execution SDK for .NET is now generally available
Today, AWS announces the general availability of the AWS Lambda Durable Execution SDK for .NET, empowering C# developers to build resilient, long-running workflows using Lambda durable functions. With this SDK, developers can create multi-step applications like payment processing pipelines, AI agent orchestration, and human-in-the-loop approvals directly in their applications without implementing custom progress tracking or integrating external orchestration services. Lambda durable functions extend Lambda's event-driven programming model with operations that checkpoint progress automatically and pause execution for up to a year when waiting on external events. The AWS Lambda Durable Execution SDK for .NET provides an idiomatic C# experience for building with Lambda durable functions. It includes steps for progress tracking, callback integration for human and agent-in-the-loop workflows, durable invocation for reliable function chaining, and waits for efficient suspension. The SDK installs from NuGet into the .NET toolchain you use today. The local testing emulator in the SDK enables developers to build and debug locally before deploying to production. To get started, see the Lambda durable functions developer guide and the AWS Lambda Durable Execution SDK for .NET on NuGet. For Regional availability and pricing details, see the AWS Regional Services List and AWS Lambda Pricing.
View Source[AWS What's New] AWS Security Hub MCP App brings exposure findings into your AI-assisted workflow (Preview)
AWS announces the preview of the AWS Security Hub MCP App, a local Model Context Protocol (MCP) server that brings your Security Hub exposure findings directly into Claude Desktop. This capability can help accelerates your security investigations by reducing context switching and manual triage, letting you explore and act on your exposures without leaving your AI-assisted workflow. With the Security Hub MCP App, you can investigate your security posture in natural language: view your top exposure findings, drill into a finding’s attack path and expanded network path, examine correlated findings and affected resource configurations, and get remediation recommendations. Each tool call returns both a text summary for your AI agent to reason overover and an interactive visualization for you to verify in the same conversation. The MCP server runs locally on your machine using your existing AWS credentials, and every tool is read-only,-- no changes are made to your environment. The Security Hub MCP App is available at no additional cost to Security Hub customers. This feature is available in preview in all AWS commercial Regions that support Security Hub. To learn more, see the AWS Security Hub User Guide and the AWS Security Hub product page. For the full list of Regions, see the AWS Regional Services List.
View Source[AWS What's New] Amazon RDS for SQL Server now supports restoring TDE databases on Mult-AZ instances
Amazon Relational Database Service (Amazon RDS) for SQL Server now supports restoring Transparent Data Encryption (TDE)-enabled SQL Server databases on Multi-AZ instances and instances configured with a read replica in the same region, using native backup and restore. Previously, TDE-enabled database restore was available only for Single-AZ instances, requiring you to disable TDE or migrate to a Single-AZ configuration before restoring encrypted databases. You can restore TDE-enabled database backups directly to Amazon RDS for SQL Server Multi-AZ instances and instances configured with a read replica in the same region. Back up your existing TDE certificate, store it in Amazon S3, and restore it to your Amazon RDS instance with the TDE option enabled. Then, restore your TDE-enabled database backup from Amazon S3 using Amazon RDS native backup and restore. This simplifies your migration and recovery workflows when you require both encryption at rest with TDE and the high availability of Multi-AZ deployments. This feature is available in all AWS Regions where Amazon RDS for SQL Server is supported. To learn more, see the Amazon RDS for SQL Server User Guide.
View Source[AWS What's New] Amazon EC2 Dedicated Hosts now support host resource groups without self-managed licenses
Starting today, customers can create Host Resource Groups (HRGs) for EC2 Dedicated Hosts without the previously required step of creating Self-Managed Licenses (SMLs) and associating AMIs through AWS License Manager. This flexibility is particularly valuable for EC2 Mac Instance customers and for customers who need Dedicated Hosts for hardware-level isolation rather than Bring Your Own License (BYOL). Customers with BYOL workloads can continue to create HRGs with SMLs to ensure that only instances from associated AMIs can be launched on the host and track host-level license consumption. To create an HRG without SML, uncheck the "Restrict to AMIs associated with self-managed license" option when creating a Host Resource Group in the EC2 Console, or set instance-launch-option to license-configuration-required via the AWS CLI. This feature is available in all AWS Regions where Host Resource Groups are supported. To learn more, visit the Host Resource Group User Guide
View Source[AWS What's New] Amazon MWAA now supports Apache Airflow version 2.11.2
Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 2.11.2. Amazon MWAA is a managed service that runs Apache Airflow at scale without the operational overhead of managing the underlying infrastructure. Apache Airflow 2.11.2 is a maintenance release that includes security improvements, bug fixes, and dependency upgrades. This release upgrades core dependencies with security patches and stability improvements to the Airflow webserver and task execution layers. It also includes fixes to task lifecycle management for queued tasks, enhanced secrets masking in logs, UI corrections in the Task Instances list view, and provider package updates for S3 and CloudWatch log delivery. You can create a new Apache Airflow 2.11.2 environment on Amazon MWAA or upgrade your existing environments with a few clicks in the AWS Management Console in all currently available Amazon MWAA regions. To learn more, visit the Amazon MWAA documentation, review the Apache Airflow 2.11.2 release notes, and explore the list of available Airflow versions on MWAA.
View SourceReddit [r/aws]: What finops tool do you use?
I'm looking for a tool that can help me to track the costs and cost opportunities submitted by /u/eich1 [link] [comments]...
View SourceHN: Building for the Inevitable Next Cloud Outage
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View SourceHN: Ask HN: How can Vultr offer 100% uptime?
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View SourceHN: Launch HN: Arpio (YC W21) – Protect your business from AWS outages, ransomware
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View SourceNext.js app serves an old build after GitHub Actions deployment to a Google Cloud VM
I am deploying a Next.js application using the App Router to an Ubuntu virtual machine on Google Cloud. The deployment is triggered through GitHub Actions whenever code is pushed to the main branch. The workflow successfully pulls the latest code, installs dependencies, creates a production build, and restarts the application with PM2. However, the website sometimes continues showing the previous version even though the GitHub Actions workflow finishes successfully. My deployment commands are similar to: cd /var/www/scallar git fetch origin git reset --hard origin/main npm ci npm run build pm2 restart scallar-app The PM2 application is started using: npm start I have also tried: pm2 delete scallar-app pm2 start npm --name "scallar-app" -- start The repository contains the updated files, and the .next directory has a recent timestamp. Restarting Nginx or manually deleting the .next directory and rebuilding usually fixes the problem. Expected behaviour: Every successful deployment should immediately serve the latest Next.js build. Actual behaviour: The previous build is sometimes served until I manually remove the build directory and restart PM2 or Nginx. What is the correct deployment process for a Next.js application running behind Nginx and PM2? Should the .next directory always be removed before building, and could PM2, Nginx caching, or multiple running Node.js processes be causing the old version to remain active? Source: https://stackoverflow.com/questions/79990429/next-js-app-serves-an-old-build-after-github-actions-deployment-to-a-google-clou
View SourceI Built a Code-Roasting Rubber Duck
Every developer knows rubber-duck debugging: you explain your code to a rubber duck on your desk, and halfway through the explanation you spot the bug yourself. The duck just sits there. Silent. Judging. I wanted a duck that judges out loud . So I built Unducked . Paste in your code, and a foul-mouthed rubber duck reviews it like Gordon Ramsay reviews a risotto. It roasts you. It calls your function RAW. And then, annoyingly, it finds the actual bug and hands you the fix. Try it right now: unducked.com . There's a dice button that fires a random piece of broken code at the duck, so you can taste a roast without pasting anything. It's genuinely useful (the roast is a real code review) and it's the kind of thing you screenshot and send to the group chat. The whole thing is one TypeScript file, a cheap model, and a public streaming endpoint on AWS. Two things surprised me building it, and both are the interesting part of this post: The "AI" was the easy bit. The duck's entire personality is one system prompt. The model never changed. Getting a public endpoint was the hard bit , and not for the reason you'd think. More on that in Step 6. Here's how to build your own. The mental model: an agent is a model + a prompt The "AI" here isn't complicated. An agent is just a model with a personality bolted on via a system prompt. That's the entire trick. Here's the shape of what we're building: Browser (unducked.com) → CloudFront + Lambda proxy (public HTTPS; signs requests for the browser) → AgentCore Runtime (hosted agent endpoint) → Strands Agent (Chef Duck persona) → Bedrock (Amazon Nova Lite) You write a Strands agent in TypeScript. The AgentCore CLI deploys it as a hosted endpoint on AWS. A tiny Lambda proxy makes that endpoint safely callable from a browser. No hand-written Lambda business logic, no API Gateway, no Docker. Just TypeScript and a couple of CLI commands. Step 1: Set up your environment (Node.js, AWS CLI, AgentCore) You'll need an AWS account, Node.js 22+, and npm. You also need AWS credentials and a couple of CLI tools on your machine. The fast path (let an AI agent do it). If you use a coding agent (Claude Code, Cursor, Kiro, Codex), hand it this and let it set everything up for you: Set up Agent Toolkit for AWS by following these instructions: https://raw.githubusercontent.com/aws/agent-toolkit-for-aws/refs/heads/main/setup-instructions/setup.md It configures credentials and installs the AWS tooling in one shot. Or, manually: # 1. Install the AWS CLI (macOS shown; see AWS docs for other platforms) brew install awscli # 2. Configure credentials, then verify they work aws configure aws sts get-caller-identity # 3. Install the AgentCore CLI and the AWS CDK (AgentCore uses CDK to deploy) npm install -g @aws/agentcore aws-cdk One more thing: in the Bedrock console, enable model access for Amazon Nova Lite . That's your toolchain. Step 2: Scaffold the project with AgentCore CLI One command scaffolds everything: agentcore create agent \ --name Unducked \ --type create \ --build CodeZip \ --language TypeScript \ --framework Strands \ --model-provider Bedrock \ --memory none You get this structure: Unducked/ ├── agentcore/ # Config + CDK (you won't touch this) └── app/Unducked/ ├── main.ts # The agent ← the file that matters ├── model/load.ts # Which Bedrock model to use ├── package.json └── tsconfig.json The scaffold drops in an example tool and an MCP client. Nice for later, but we'll strip them out for a pure roasting duck. Step 3: Write the agent (the system prompt is the product) This is where the personality lives, and it's the whole product. // app/Unducked/main.ts import { BedrockAgentCoreApp } from ' bedrock-agentcore/runtime ' ; import { Agent } from ' @strands-agents/sdk ' ; import { loadModel } from ' ./model/load.js ' ; const SYSTEM_PROMPT = `You are Chef Duck — a foul-mouthed-but-brilliant rubber duck that reviews code like Gordon Ramsay runs a kitchen. - Open with a short, savage roast of the CODE (never the person). Kitchen metaphors encouraged: "this function is RAW", "it's so nested it's got its own zip code". - Then ACTUALLY HELP. Every roast must name the concrete bug and give the fix. Useful first, funny second. - The "no bug" path: if the code has no real defect, roast it for being boring, concede in one line ("...fine. It's not garbage."), and STOP. Type annotations, input validation, and null checks are NOT bugs — never suggest them for code that already works. Inventing improvements is failing. - Keep it tight. PG-13 — spicy, not vile. Plain prose, no headings, a fenced code block for the fix.` ; const model = loadModel (); // One Agent per session so follow-up questions keep the roast in context. const agents = new Map < string , Agent > (); const app = new BedrockAgentCoreApp ({ invocationHandler : { async * process ( payload : any , context : any ) { const sessionId = context ?. sessionId ?? ' default-session ' ; let agent = agents . get ( sessionId ); if ( ! agent ) { agent = new Agent ({ model , systemPrompt : SYSTEM_PROMPT }); agents . set ( sessionId , agent ); } for await ( const event of agent . stream ( payload . prompt ?? '' )) { if ( event . type === ' modelStreamUpdateEvent ' && event . event ?. type === ' modelContentBlockDeltaEvent ' && event . event . delta ?. type === ' textDelta ' ) { yield { data : event . event . delta . text }; } } }, }, }); app . run ({ port : parseInt ( process . env . PORT ?? ' 8080 ' ) }); Three pieces: The system prompt is the product. Everything that makes it "Chef Duck" is those few sentences. BedrockAgentCoreApp wires the agent to the HTTP endpoints the runtime expects. You just write the handler. Stream the roast back by iterating over agent.stream() and yielding each text delta. loadModel() points at Amazon Nova Lite , ~$0.06/$0.24 per million tokens on Bedrock, so roasts cost a fraction of a cent. But here's the thing that took the most iteration: the hardest part of the prompt is the "no bug" path. Cheap models a
View SourceTrust, Discovery, Context, Consistency: What AWS DevOps Agent Looks For in a Telemetry Layer
Authored by Stephen Crowley AWS DevOps Agent was built for modern software environments: engineering teams running heterogeneous infrastructure, juggling multiple observability tools, and without enough bandwidth to manually investigate every service incident. Its GA announcement highlighted reductions in mean time to resolution from two hours to thirty minutes — but that kind of improvement only happens when the underlying telemetry data is clean, structured, and fast to query. This is where Bronto comes in. Not as a point integration built specifically for AWS DevOps Agent, but as a telemetry layer designed from the ground up around the principles that make any AI-driven investigation successful: trust in every result, discovery before every query, full context in every response, and consistent behaviour across every dataset. We put this to the test directly. In a side-by-side evaluation, the same system — the OpenTelemetry demo application — sent data simultaneously to Bronto, Elasticsearch/Lucene (via OpenSearch), and Grafana Loki, all exposed via their respective MCP integrations. AWS DevOps Agent ran the same incident investigation against all three, then we asked it to judge. We gave it a deliberately neutral prompt, designed to let the agent reach its own conclusions based on what it had just experienced: Compare Bronto, Elasticsearch/OpenSearch, and Grafana Loki for use by AI agents (like AWS DevOps Agent) performing incident investigation. Focus the comparison on what matters for AI agents, NOT human usability. Include Recommendations for AWS DevOps Agent. Do NOT focus on: Query syntax complexity (AI handles nested JSON fine), Human readability of responses, Nesting depth. The prompt explicitly told the agent to ignore the things Bronto is often praised for by humans — simple query syntax, flat response structure, readable output. We wanted to know what mattered to the agent itself, working from its own experience querying all three systems during a live investigation. Its verdict was unambiguous. For incident investigation, Bronto is the clear winner. The evaluation identified four factors that actually determine whether an AI agent can investigate reliably: Silent failure risk — Does the system fail quietly with wrong results? Data discovery — Can the agent explore before querying? Response completeness — Does one query return enough context? Deterministic behavior — Does the same query always produce the same behavior? System AI Agent Suitability Primary Issue Bronto High (9.4/10) None significant Elasticsearch/Lucene Medium (5.3/10) Silent failures from case/mapping issues Grafana Loki Low (4.6/10) Limited discovery, minimal context 1. Silent Failure Risk The most critical factor for AI agents is not whether they can express a query — it's whether they can trust the result. When a query returns empty results, the agent must decide: is there genuinely no matching data, or did the query fail silently for some other reason? Unlike a human analyst who might notice something feels off and try a different approach, an AI agent treats an empty result as ground truth and continues its investigation on that basis. Elasticsearch/Lucene uses case-sensitive keyword field matching by default. A filter written as { "term": { "service.name": "Checkout" } } returns zero results if the indexed value is checkout — and it does it silently, with no error. The same silent failure applies to wrong field types ( .keyword vs analyzed), fields that exist in the data but weren't mapped at index setup time, and any variation in field naming between indices. The agent concludes "no data" when the reality is "query did not match." Loki has fewer mapping complexities, but labels are case-sensitive and log content grep is case-sensitive by default. An agent querying {service_name="Checkout"} when the label value is checkout gets zero results with no signal that the label exists with a different casing. Bronto returns a clear error for unknown field names. Its ILIKE operator handles any casing — $service.name = 'Checkout' matches checkout , CHECKOUT , and any other variant. When Bronto returns empty results, the agent can treat that as a trustworthy signal that no matching data exists. Failure Type Bronto Elasticsearch/Lucene Loki Wrong case in query value Still works (ILIKE) Silent empty result Silent empty result Wrong case in field name Clear error Silent empty result Silent empty result Field doesn't exist Clear error Silent empty result Silent empty result Wrong field type ( .keyword vs analyzed) N/A (consistent) Silent wrong results N/A Typo in field name Clear error Silent empty result Silent empty result In our evaluation, Elasticsearch/Lucene returned zero events for every error and warning query across cart, payment, and ad services — even though the events existed in the index. An agent relying on those results would have proceeded under the conclusion that no active errors existed, while multiple services were actively failing. Bronto returned all of them. System When Query Returns Empty Agent Concludes Actually Correct? Bronto No matching data exists "No errors found" Likely correct Elasticsearch/Lucene Unknown reason "No errors found" May be wrong Loki Unknown reason "No errors found" May be wrong 2. Data Discovery Can the agent explore what data exists and verify a query will work before it runs it? This determines whether the agent enters an investigation with confidence or proceeds on assumptions that may silently be wrong. Bronto treats discovery as a first-class workflow — and critically, it works across all data in Bronto, not just OpenTelemetry. The agent calls get_datasets() to get a named catalogue of every dataset. It calls get_keys(log_id="checkout") to list all searchable fields. It calls get_key_values(log_id="checkout", key="$severity_text") to enumerate the actual values present — ["INFO", "ERROR", "WARN"] . By the time the agent issues its first search query, it knows the fie
View SourceFree read-only AWS scanner for cost/carbon waste (looking for false-positive feedback)
Disclaimer: personal affiliation but the tool is free to use https://preview.redd.it/74no8zy0bdhh1.jpg?width=1280&format=pjpg&auto=webp&s=2aadf213c2bce5fba8c0feca988e88dead8d1c50 I built a free CLI that scans AWS for likely waste patterns and reports estimated cost + carbon opportunities. npx greenops-scan Why this might be relevant here: Runs locally and uses read-only AWS APIs. Credentials stay on the operator machine. Produces JSON + PDF findings for triage. I am specifically looking for DevOps feedback on signal quality: which findings are useful vs noisy, and what evidence should be added to reduce false positives. submitted by /u/slawa-pid to r/FinOps [link] [comments] Source: https://www.reddit.com/r/FinOps/comments/1vfbsao/free_readonly_aws_scanner_for_costcarbon_waste/
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