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Only First party#Deployment
Nov 13Fri
  1. AWS Machine Learning Blog36

    Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets

    This AWS post describes how to get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features: IAM principal and session tags for attribution, Cost Explorer or CUR 2.0 exports (with the caller identity option) for reporting, and AWS Budgets with budget actions that attach a Deny IAM policy when a threshold is crossed. It notes that budget data refreshes up to three times a day, cost allocation tags are not retroactive, and a Deny action on a shared role blocks everyone using it.

  2. Databricks Blog28

    Eclipse Aerospace simplifies flight-data ingestion with Databricks

    Eclipse Aerospace worked with Databricks to replace a multi-stage AWS Lambda and Avro pipeline with an Arrow-native PySpark custom data source that wraps its existing Rust flight-data decoder, reading proprietary telemetry files directly in Spark on serverless compute. The approach, enabled by the Python Data Source API available from Databricks Runtime 15.4 LTS, cut latency, removed duplicate Avro storage and a Lambda tier, and brought decoding into the Spark UI and job metrics. Eclipse reports the connector can process records within a minute of upload and commonly handle multi-gigabyte batches in parallel in under two minutes.

  3. Databricks Blog22

    How Constellation is building a governed data foundation on Databricks

    Constellation, the largest US nuclear fleet operator, is building a governed Enterprise Data Platform on Databricks to give data consistent shared meaning across domains, choosing Databricks partly because it interoperates with existing architecture rather than requiring a rip-and-replace. The platform rests on conformed semantic definitions, a semantic knowledge layer with selective Data Vault modeling, and Unity Catalog for lineage, fine-grained access and ownership of meaning, with early use cases including a nuclear engineering assistant built with Databricks Genie, outage planning, and sales intelligence.

  4. AWS Machine Learning Blog34

    Building a self-service LLM gateway with Amazon Bedrock and LiteLLM

    This post describes a pattern for a self-service LLM gateway that runs LiteLLM on AWS Fargate to expose Amazon Bedrock models through an OpenAI-compatible API, using Bedrock Application Inference Profiles for per-model quota isolation and scoped IAM access. It covers configuration of developer tools, LiteLLM model mapping with per-token costs for budget enforcement, CDK constructs for inference profiles, Langfuse observability, CloudWatch TPM alarms, and lessons learned from scaling to several hundred developers.

  5. AWS Machine Learning Blog34

    Optimizing cost per task and proving agent ROI on Amazon Bedrock

    This AWS post argues that model cost should be measured per completed task rather than per token, citing four hidden cost drivers—tokenizer efficiency, output verbosity, agentic loop efficiency, and reasoning overhead—and an illustrative example where a model with double the list price costs 65% less per task. It describes levers for reducing cost per task (prompt caching, intelligent prompt routing, output constraints, batch inference) and Bedrock features such as CountTokens, the usage block, and invocation logging for measuring token and turn counts. For ROI, it recommends documenting and measuring a human baseline before launch, instrumenting workflows as experiments with run records, joining cost to outcomes via Athena, and using a five-dimension scorecard with green/yellow/red thresholds.

  6. Wiz Research22

    ASPM in the AI Era Part 3: Fixing Risk at the Speed of Code

    The final installment of Wiz's series argues that AppSec's bottleneck is remediation, not detection, citing Verizon DBIR data that median patch time has risen to 47 days from 32. It describes how Wiz Code maps cloud findings to code owners via VCS connections and correlated data, and how the Wiz Green Agent generates tailored remediation plans that can open pull requests or hand off to coding agents like Claude Code. Wiz Workflows then routes tickets and alerts to Jira, ServiceNow, Slack, or Teams with full scanner and runtime context.

  7. Databricks Blog58

    Lakehouse//RT: Scale real-time analytics, not your bill

    Databricks introduces Lakehouse//RT, a real-time analytics engine built to serve high-concurrency, low-latency workloads directly on governed lakehouse data without costs scaling proportionally with query volume. It claims sub-100 millisecond latency at 12,000 queries per second, node-level autoscaling, and elimination of separate serving layers like Druid or Pinot, with cited customers reporting 6x to 10x lower infrastructure costs. Lakehouse//RT is available in beta, with a 30% discount through January 2027.

  8. AWS Machine Learning Blog71

    Run agents securely and efficiently with the next generation AgentCore runtime

    AWS announced updates to the Amazon Bedrock AgentCore runtime that keep each session in its own hardware-isolated Firecracker MicroVM while starting sessions up to 15x faster, scaling to 4x more compute (up to 8 vCPUs and 32 GB, with container images up to 10 GB), and running up to 68% more efficiently. The runtime now reclaims memory within minutes of release and scales idle agents to zero, and agents can dynamically mount and save work to a filesystem such as an Amazon S3 bucket with FUSE support so data stays inside the trust boundary. The next generation runtime is available today in AWS Regions across North America, South America, Europe, and Asia Pacific.

    Why it matters: Announces concrete performance, scaling, billing, and data-isolation changes to a managed agent runtime, with customer metrics and availability details useful to teams planning production agent deployments.

  9. Mastra Blog47

    Introducing Private Model Gateways for Mastra Agents

    Mastra now lets agents route requests to self-hosted open-weight models through a custom model gateway, configured with a custom ID and referenced as a model prefix such as private/ollama/gpt-oss:20b. The post walks through creating a gateway for a local Ollama server, registering it on the Mastra instance, and setting AUTO_BLOCK_EXTERNAL_PROVIDERS=true to hide external providers in Studio's model picker. It requires @mastra/[email protected] or later and cites privacy/compliance and fixed infrastructure costs as motivations.

  10. Amp Chronicle50

    Bye Bye, Dial

    Amp is retiring its Dial and its four modes (low, medium, high, ultra) in favor of a model selector that lets users pick the model and its effort per thread, including mid-thread, and shows which subscription or API key will be billed or whether Amp credits are used. The change follows the addition of bring-your-own subscriptions and keys, which made the selected model determine billing as well as token production. New threads start on Staff Pick (Claude Opus 5.5 today), and existing threads keep running on their prior model.

  11. Databricks Blog25

    How H6.ai builds AI-powered financial automation with Lakebase Postgres

    H6.ai automates accounts payable on top of customers' existing ERPs by combining AI-assisted invoice understanding with deterministic, auditable policy execution. To make regression testing reliable despite constantly changing financial data, it gives each customer a dedicated Lakebase Postgres database and uses copy-on-write branches to freeze snapshots, run each test on its own disposable branch, and judge results with deterministic assertions plus a panel of AI judges. The company says branching provides isolation, frozen state, cheap disposable copies and schema-safe replays, and it plans to use Lakehouse//RT queries on Lakebase tables for read-only analytics without copying data.

  12. Claude Blog58

    Operate Claude Managed Agents inside your security perimeter

    Anthropic announced a private beta of self-hosted Claude Managed Agents, which runs the full service—harness, sandboxes, credentials, and session history—in a customer's own cloud or on-premise, with model requests routed to Anthropic, Amazon Bedrock, Google's Agent Platform, Microsoft Foundry, or a customer gateway. The design separates the harness from the sandbox, keeps credentials out of the sandbox via vaults or a customer secrets manager, allows per-environment host allowlists, and exports session records over OpenTelemetry. Access is available by request through an Anthropic account team.

  13. Factory Blog55

    Launching Factory’s Cyber Defense Network

    Factory introduced the Cyber Defense Network, an ecosystem of cybersecurity companies whose findings connect into Security Factory, with founding partners including Snyk, Palo Alto Networks, Semgrep, Cogent, Casco, DepthFirst, and Detail. Partner findings flow into Security Factory, where Factory Droids investigate vulnerabilities, implement code changes, and validate fixes through testing across signals, triage, and continuous remediation stages. Factory plans to expand the network to additional cybersecurity innovators and model providers.

  14. Databricks Blog58

    How Block powers collaborative AI agents with Buzz and Unity Gateway

    Block developed Buzz, an open source, model-agnostic collaboration space where employees and AI agents work together in group discussions, focused conversations, and one-on-one exchanges, with users choosing the model or coding harness behind each agent. Databricks Unity Gateway provides the model access layer, letting Buzz users select Databricks as their inference provider and choose from frontier models from OpenAI, Anthropic, and Google plus select open-weight models through a single integration. Block has also been experimenting with multi-agent workflows, such as a more capable model coordinating agents powered by smaller, more efficient models, and developed an internal benchmarking approach for these agent teams.

  15. PyTorch Blog38

    Fast by Default: Compile-Time Heuristics for Helion Kernels

    Helion now uses compile-time heuristics that analyze a kernel's IR and dataflow to classify it as matmul, reduction, or pointwise and emit block sizes, warps, and stages without autotuning. On H100, the computed configs reach 1.18x hand-written FLA Triton for linear attention, 1.27x vLLM's handwritten CUDA reduction kernels, and 1.09x torch.compile max-autotune on a general reduction suite. The heuristics are merged into Helion and enabled by default on H100 and B200 with the Triton backend.

  16. AWS Machine Learning Blog36

    From prototype to production: Engineering agents with Agent DLC

    AWS authors propose the Agent Development Lifecycle (Agent DLC), which ties six activities—Define, Build, Evaluate, Release, Observe, and Feed Back—to an explicit "gold standard" of criteria, thresholds, and approved examples, with an inner loop (score, inspect the path, change one thing, re-evaluate) and an outer loop that updates the standard from production feedback. A reference HR policy assistant shows chunk-level retrieval precision of 1.00 on all three test questions while fact-level precision was 0.59, 0.53, and 0.09, and a prompt-only change improved one question but worsened another, which the authors attribute to missing source coverage. The post also describes a benefits-enrollment answer that scored 0.90 groundedness yet made a false claim about the user's contact preference, and outlines a one-week AWS getting-started plan using Amazon Bedrock AgentCore Evaluations and Observability.

  17. AWS Machine Learning Blog48

    Fine-grained access control for Amazon Bedrock Managed Knowledge Base tools

    This post describes an architecture in which all employees share one agent and knowledge base, but each user can search only the documents they are allowed to view, using Amazon Bedrock Managed Knowledge Base ACL awareness exposed as MCP tools through Amazon Bedrock AgentCore Gateway. The design keeps the LLM from deciding who the user is: a request interceptor injects the verified email from the JWT into userContext, Policy in AgentCore checks it against the token's email claim, and a resource-based policy denies searches from principals other than the gateway execution role. It also covers prerequisites, ACL metadata files, tool schema configuration with parameterOverrides, and a verification script that tests each defense.

  18. AWS Machine Learning Blog52

    Build composable, interoperable multi-agent systems with A2A on AWS

    This AWS post presents a deployed reference architecture in which a LangChain-based enterprise orchestrator in one AWS account calls a Strands Agents-based compliance SaaS agent in another account using the Agent-to-Agent (A2A) protocol, with neither side depending on the other's framework or SDK. It covers cross-framework interoperability, bidirectional agent-to-agent callbacks, and OAuth authentication in both directions using separate Amazon Cognito user pools, with deployment from three Jupyter notebooks and an end-to-end test.

  19. AWS Machine Learning Blog34

    Migrate to Amazon Bedrock Managed Knowledge Base with your coding agent

    AWS added a knowledge base migration guide to the amazon-bedrock skill in the Agent Toolkit for AWS, which lets a coding agent plan a move from a customer-managed knowledge base to Amazon Bedrock Managed Knowledge Base, starting with read-only discovery and a plan you review line by line. On approval, the agent creates the new knowledge base and data sources alongside the original or writes a migrated infrastructure as code template, while ingestion and updating the application's Retrieve calls remain with your team. The post covers why AWS recommends the move, prerequisites, installation, planning, updating retrieval calls, and validating retrieval before cutover.

Nov 12Thu
  1. Databricks Blog42

    Genie in Slack is now Generally Available: Governed AI cowork, right where work happens

    Databricks announced the General Availability of the Genie app for Slack, which answers questions in channels, threads, or DMs using the same governed context and permissions as Genie in Databricks, with every answer citing its sources. Users can @mention Genie in channels or DM it, and through MCP connectors Genie can create or update documents, comment on tickets, and update records with approval before each change, as well as schedule recurring analyses and work with files. Genie in Slack follows the same governance as other Genie surfaces, running each question with the asker's identity across Unity Catalog and connected sources, with admins able to govern AI usage centrally in Unity Gateway.

  2. NVIDIA Newsroom12

    A New Dawn in the Cloud: ‘Warhammer 40,000: Dawn of War IV’ Is Coming to GeForce NOW

    Warhammer 40,000: Dawn of War IV, developed by KING Art Games and published by Deep Silver, is coming to GeForce NOW when it launches on Steam Thursday, Dec. 3, featuring large-scale battles, base building and more than 70 campaign missions playable solo or in co-op. NVIDIA DLSS 5 with 3D-Guided Neural Rendering is coming to War Thunder in its mid-November Eagle's Shadow update and to Where Winds Meet on Monday, Nov. 23. Dragon's Dogma 2: Dark Arisen is streaming on GeForce NOW, and 10 games join the library this week.

  3. PyTorch Blog40

    Integrating SGLang into Spyre in Under a Week with AI

    A two-person team used AI coding agents to integrate SGLang with IBM Spyre in under a week, producing a plugin of roughly 1,500 lines built on existing PyTorch Spyre support and SGLang's extension points. SGLang delivered about 3.6× the throughput of the reference PyTorch inference implementation using torch-spyre and was on par with vLLM on the measured workloads for Granite 3.3 8B and Gemma 4 26B A4B. Prefix sharing through RadixCache worked without any Spyre-specific changes.

  4. PyTorch Blog58

    Training a 1T-Parameter MoE on EU public infrastructure with PyTorch and TorchTitan

    The EUROPA consortium (Domyn, Fraunhofer IAIS and IIS) describes scaling pretraining of a ~951.9B-parameter MoE model (32.8B active per token) on JUPITER, the EuroHPC exascale supercomputer at Forschungszentrum Jülich, raising throughput from 90 to ~1,300 tokens/sec per GPU across 384 NVIDIA GH200 GPUs per DP replica. Key optimizations built on PyTorch and TorchTitan include FlexShard placement for the Muon optimizer, activation-checkpoint offloading to the Grace CPU, and running FSDP's all-gather setup on the compute stream via unshard_async_op, freeing ~14 GiB of HBM per GPU. The fixes were upstreamed to PyTorch and TorchTitan, with training reaching ~1,300 tokens/s per GPU on 1,536 GPUs across 4 DDP replicas.

  5. vLLM Blog63

    H3-Image Day-0 Support: 4K in 0.87s with Vera Rubin

    vLLM-Omni brought MiniMax H3-Image, an open-weight text-to-image and editing model, to NVIDIA Vera Rubin, reaching 0.87 seconds median for native 4096×4096 generation on four GPUs with SP4 and MXFP8 in a short-prompt serving benchmark. A separate SP1×DP4 throughput configuration delivers 6,115 images/hour, which the authors project to 146,755 images/day and, at an assumed USD 0.06879 per image, USD 105.16/GPU/hour gross revenue at full billable utilization before operating costs. The post also documents VAE decoding gains from CUDA Graph replay plus multiple streams (2.635 s to 0.213 s on four GB300 GPUs) and community validations on AMD, Intel, Huawei Ascend and Moore Threads hardware.

    Why it matters: Useful for readers weighing open-weight 4K image serving, since it separates latency and throughput operating points and labels its revenue projections as extrapolations before operating costs.

  6. OpenRouter Announcements58

    How We Built a Self-Correcting Localization System for OpenRouter

    OpenRouter launched beta localization into Simplified Chinese, Spanish, Brazilian Portuguese, German, French, Japanese, and Korean, using a daily pipeline that detects changed English strings, translates them with GPT-6.1 Sol, runs code checks, and has a second model verify meaning against the English, retrying with objections attached. Translations are committed as JSON catalogs in Git with fingerprints of their English source, and reader reports become glossary rules, style notes, overrides, or eval cases. A full retranslation of 16,918 strings into 7 languages costs about $460, versus about $7,200 at Smartling's published machine translation rate.

  7. AWS Machine Learning Blog36

    Build autonomous AI agents with Amazon Bedrock and Spring AI

    This post shows how Java teams can build autonomous AI agents on the JVM using Spring AI and Amazon Bedrock AgentCore, with a single-agent travel assistant demonstrating tool calling, streaming, and memory, and a multi-agent intelligent document processing system with five specialist agents, a SpEL rules engine, and alerting. It covers the AgentCore runtime contract (@AgentCoreInvocation for POST /invocations and GET /ping), @Tool methods, structured output to Java records, memory advisors, and production patterns including Resilience4j retries, twelve-factor configuration, least-privilege IAM, Bedrock Guardrails, and Micrometer observability. Deployment uses an ARM64 image pushed to Amazon ECR and provisioned through modular Terraform, with an optional Amazon EKS and IRSA path.

  8. OpenRouter Announcements58

    Self-Hosted LLM vs API: When Does Self-Hosting Pay Off

    At October 8, 2026 prices, a 27B model on one rented RTX 3090 breaks even against API pricing at roughly 6–12% load, while DeepSeek V4.1 Flash on four rented H200s never beats the cheapest zero-data-retention API endpoint. The post computes break-even load as GPU hourly rent divided by the API cost of the same hour of tokens, using live per-provider prices and benchmark throughput, and notes that published self-hosting costs near a cent per million tokens mostly count cached input tokens that are also nearly free via API. It also covers privacy options such as provider.zdr and EU/US in-region domains, and suggests self-hosting a steady base load while sending peaks to the API.

  9. OpenRouter Announcements58

    How to Stop an AI Agent Stuck in an Infinite Loop

    OpenRouter describes four guards against runaway agent loops, tested on six models with its Agent SDK (@openrouter/agent 0.11.0): doom-loop detection, a loopKey that normalizes tool arguments, stopWhen caps on steps and cost, and a credit limit on the API key. With doomLoop: true, all six models were blocked on their third identical tool call, and across 18 runs detection cut tool calls from 314 to 64 and spend from $0.29 to $0.05. The article notes that loops vary between runs and that only the key-level limit caps total spend when agent code is buggy or caps are missing.

  10. AWS Machine Learning Blog58

    Introducing the Web Search API on Amazon Bedrock AgentCore

    AWS announced the Web Search API on Amazon Bedrock AgentCore, which lets developers call the InvokeWebSearch operation directly from AWS SDKs or the AWS CLI with SigV4-signed IAM credentials, without creating a gateway or other resources. It returns up to 25 ranked results from an Amazon-operated web index, each with a text extract and, when available, title, URL, and publication time, and supports domain and publication-date filtering. It is available in six Regions and billed at $7 per 1,000 queries, complementing the existing AgentCore Gateway path.

  11. AWS Machine Learning Blog46

    Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

    This post describes adding three checks to a PyTorch DDP training loop to detect silent data corruption (SDC), where hardware such as a GPU returns a wrong but plausible number without raising an error: a non-finite gradient check, a ring replay audit that recomputes another node's micro-batch and compares loss bits and gradient fingerprints, and a weight fingerprint comparison after the optimizer step. When a check fires, the suspect rank is mapped to its node via the Kubernetes node name, the node is cordoned, and SageMaker HyperPod replaces it, with training resuming from a checkpoint saved before the suspect window. The checks were tested with software-injected faults on a HyperPod cluster with 4 ml.p5.48xlarge instances (32 NVIDIA H100 GPUs) and repeated on three instances, with full code in an accompanying GitHub repository.

  12. AWS Machine Learning Blog38

    OpenAI Codex on Amazon Bedrock

    This AWS post explains how developers can run OpenAI Codex on the workstation while routing model inference through Amazon Bedrock using supported OpenAI models such as GPT-5.6 Sol, Terra, and Luna. It compares three access paths—Native AWS Access with IAM Identity Center, AgentCore Gateway, and an LLM Gateway—and recommends starting with a small, least-privilege pilot before expanding. It also covers validation steps, identity-isolation testing, OpenTelemetry and CloudWatch observability, cost allocation, plugins, hooks, MCP servers, phased rollout, and cleanup.

  13. AWS Machine Learning Blog38

    ICYMI: What landed for AI builders in October 2026

    AWS recaps its October 2026 releases for AI builders, including new Amazon Bedrock models such as Claude Haiku 5.5, OpenAI GPT-6.1 Terra, Amazon Nova 2.5 Sonic, GLM 5.3, and TwelveLabs Pegasus 1.5. Amazon Bedrock AgentCore gains a generally available CLI 1.0, model fallback and InvokeModel support in Gateway, Step Functions workflows as agent tools, and usage-based memory pricing, while Strands adds Bidi Agents, the Box local sandbox, red teaming in Strands Evals, and Decider on Gemma 4. The post also covers structured outputs for Claude models, Web Search domain filtering, a new Guardrails Standard tier for contextual grounding, and Amazon Bedrock cost breakdowns in AWS billing tools.

  14. Modal Blog58

    Modal becomes a gVisor maintainer

    Google is donating gVisor, including its name and trademarks, to the CNCF, and the project is moving to a maintainer-led governance model; Modal is one of three companies, alongside Ant Group and Tines, committing to join gVisor's maintainers, with OpenAI, Tencent and NVIDIA continuing their contributions. Modal says it has run gVisor as its major container runtime since the beginning, launching upwards of 40 million gVisor containers a day for GPU inference, multi-node training, AI coding agents and sandboxes, and has contributed upstream since 2023. The post details upstreamed work including nvproxy GPU support, an rdmaproxy device proxy for RDMA, snapshot/restore, and filesystem and memory performance improvements, and outlines future plans such as multi-GPU memory snapshots and GPUDirect Storage.

  15. Browser Use Blog34

    How to Use Custom Proxies with Browser Use

    This tutorial shows how to connect a third-party (bring-your-own) proxy to Browser Use Cloud by supplying the provider's host, port, protocol, and credentials when creating an agent run or cloud browser, then verifying the browser's exit IP and country via ipwho.is. It also demonstrates using the same credentials with Playwright both locally and against a Browser Use cloud browser over CDP, and compares managed proxy pricing ($5/GB) with BYO (provider rate plus $0.20/GB Browser Use egress). The guide includes setup steps, code samples, a troubleshooting table, and production tips such as preserving cookies alongside sticky sessions and stopping unused browsers.

  16. Pydantic AI Articles34

    What we found testing OpAMP clients against the spec

    Pydantic describes how its Logfire Fleet Management OpAMP clients for Rust, Go, Node.js, Python, Java and .NET share one Rust protocol core, run as WebAssembly or a native library, and are checked against roughly 300 normative spec statements, TLA+ models, Kani and Verus proofs, black-box conformance and fault testing. Running the same scenarios against upstream OpenTelemetry clients surfaced bugs in the Collector's OpAMP extension and Supervisor and in the Java, Python and .NET clients, including instance UID handling, reconnect and status reporting, capability declarations and startup panics, plus one critical security vulnerability that the core is not affected by.

  17. AWS Machine Learning Blog12

    The attendee’s guide to the AWS re:Invent 2026 AI/ML track

    AWS will hold re:Invent from November 30 to December 4, offering over 2,000 learning sessions, with more than 140 in the AI/ML track spanning breakouts, chalk talks, builder sessions, workshops, code talks, and lightning talks. The guide organizes AI/ML sessions around the AWS AI stack—global infrastructure, AI chips (Trainium and NVIDIA GPUs), data foundation, models/training/inferencing (Amazon Bedrock and SageMaker AI), agent development and management (AgentCore), and agentic solutions—plus sessions on trust in agents, sustainability, and Local Zones and AI Factories. Breakout sessions are recorded and posted to the AWS Events YouTube channel a few days later, while other session types are live and not recorded.

  18. AWS Machine Learning Blog40

    How DXC Technology built a multi-model AI substrate on Amazon Bedrock

    DXC Technology built DXC OASIS, a harness for mission-critical IT operations, on a governed multi-model substrate in Amazon Bedrock that provides access to Anthropic Claude, Amazon Nova, Meta Llama, and open-weight models through one managed interface, with Bedrock Guardrails, AWS PrivateLink, and serverless scaling as system-level controls. DXC separates the substrate from its own orchestration, governance, and assurance layer, and reports that since general availability in March 2026, DXC OASIS has reduced resolution time by 95% on average across 56 measured use cases. The post describes the architecture, model routing, and authority tiers, and states that DXC is evaluating Amazon Bedrock AgentCore alongside its current orchestration layer.

  19. AWS Machine Learning Blog34

    Automating AWS Agent Registry updates with push-based architecture

    This post describes an event-driven pipeline using AWS CloudTrail, Amazon EventBridge, and AWS Lambda that detects Amazon Bedrock AgentCore UpdateAgentRuntime events and syncs MCP server tool schemas into AWS Agent Registry records, supporting both single- and multi-account setups. The Lambda extracts the runtime ARN, obtains an OAuth bearer token via AgentCore Identity, calls the MCP server's initialize and tools/list methods, then updates the matching APPROVED registry record only when normalized tool definitions differ. Updated records revert to Draft status and require curator approval before becoming discoverable, and the post notes limitations such as no automatic registration of new servers and no retry on cold starts.

  20. AWS Machine Learning Blog42

    Accelerating generative AI delivery with reusable assets

    AWS's Generative AI Innovation Center, now part of AWS Frontier AI Engineering & Services, offers a catalog of 23 production-ready open source reusable assets such as RAG pipelines, document processors, and agent scaffolding. The post cites DoorDash and Lyft using the Contact Center GenAI Agent and Ricoh using the GenAI Intelligent Document Processing accelerator, reporting results including a 50x increase in testing capacity and a 7-fold throughput increase. It also walks through deploying the Contact Center GenAI Agent and lists the full asset catalog on GitHub and AWS Labs.