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TodayNovember 13Friday27 items
  1. ElevenLabs Blog18

    7 customer service skills every human and AI agent needs

    ElevenLabs outlines seven customer service skills — communication, problem-solving, conflict resolution, empathy, adaptability, personalization and product knowledge — that both human and AI support agents need. It cites a Zendesk study finding over 50% of customers switched to competitors after one bad experience, and a Gartner forecast that at least 70% of customers will start their journey with an AI interface by 2028. The guide stresses evaluating conversational metrics during training and after deployment, plus seamless handoff to humans for de-escalation.

  2. Modal Blog40

    Modal Sandboxes adopt burst pricing, cutting costs 58% versus reservations

    Modal now bills Sandboxes by active resource use instead of fixed reservations, letting each Sandbox burst past its default 0.125-core and 128 MiB request and pay only for what it uses each second. In a 20-minute coding agent example, bursting cost 8.4¢ per Sandbox-hour versus 20.0¢ for a fixed reservation, a 58% saving. CPU can burst up to 16 cores above request by default, with memory uncapped unless a limit is set.

  3. Factory55

    Factory launches Cyber Defense Network linking security tools to Security Factory

    Factory launched the Cyber Defense Network, an ecosystem of cybersecurity companies whose findings flow into Security Factory so Factory Droids can investigate vulnerabilities, implement code changes and validate fixes through testing. Founding partners include Snyk, Palo Alto Networks, Semgrep, Cogent, Casco, DepthFirst and Detail. The workflow covers signals from partner tools, triage of reachable vulnerabilities, and continuous remediation that produces fixes ready for engineering teams to review and merge, while organizations keep their existing security tools and workflows.

  5. GitHub Blog · AI & ML58

    GitHub guide explains how to build agent skills that run across Claude Code, Cursor and Copilot

    GitHub published a practical guide to agent skills, versioned directories centered on a SKILL.md file that describe when and how to perform a repeatable task. The guide uses two examples, Proof Agent for adversarial verification of AI-generated code and Data Forge for a multi-skill data pipeline, to show how skills share a project manifest, reuse one execution path, and place human approval at each phase. It also describes a packaging script that generates client-specific files for Claude Code, Cursor and GitHub Copilot from a single skills source.

  6. AWS Machine Learning Blog40

    How Natera built SWARM, an agentic AI system for lab ops on Amazon Bedrock AgentCore

    Natera built SWARM, an agentic AI system for lab operations, on Amazon Bedrock AgentCore, cutting first response time by about 130x and automating 96.2 percent of support tickets. The system pairs a swarm-agent that drafts diagnostic reports before engineers open a ticket with a swarm-hive for real-time engineer interaction, both sharing one skill library. It addresses over 65 daily Jira tickets across three queues that previously averaged 17.4 hours for first response.

  8. LangChain Blog45

    What Is Eval-Driven Development? How to Improve AI Agents

    LangChain explains eval-driven development (EDD), a three-step process for building AI agents: define what good looks like, write the eval before you build, and compare results after each change. Because agents are non-deterministic, evals score runs across a dataset of examples and can use LLM-as-a-judge, with real agent runs in LangSmith as a source of test inputs.

  9. AWS Machine Learning Blog66

    AWS updates AgentCore runtime with 15x faster session starts and 4x more compute

    AWS announced updates to Amazon Bedrock AgentCore runtime, its managed compute layer for agents, with sessions now starting 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, while each session stays in its own hardware-isolated Firecracker MicroVM. Sessions start in under 2 seconds regardless of image size or concurrency, idle agents scale to zero, and agents can mount and save work to a filesystem such as an Amazon S3 bucket with FUSE support so data stays inside the trust boundary. The runtime is available today in AWS Regions across North America, South America, Europe, and Asia Pacific.

    Why it matters: AgentCore runtime 的新版本把会话启动、算力上限和内存计费一起调整，为需要长时间运行的智能体提供了隔离与成本之间的新取舍。

  10. AWS Machine Learning Blog34

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

    A self-service LLM gateway built on Amazon Bedrock Application Inference Profiles and LiteLLM running on AWS Fargate exposes Bedrock foundation models behind an OpenAI-compatible API, giving developers a single base URL and per-user key. The setup adds per-user spend controls, per-model quota isolation, and CloudWatch RPM/TPM observability, with Langfuse for prompt-level telemetry. Self-service key issuance requires LiteLLM Enterprise, since the free tier is limited to five users.

  11. Google DeepMind90

    Google DeepMind releases Gemini 4 Hydrogen, a faster Gemini 4 model for coding and agentic workflows

    Google DeepMind introduced Gemini 4 Hydrogen, a Gemini 4 model built for fast everyday coding and agentic execution rather than the long-horizon reasoning of Gemini 4 Carbon and Argon. It scores 51 on the Artificial Analysis Intelligence Index at its default high thinking level versus 50 for Argon, reaches 54 at a new xhigh setting, and is priced flat at $1.50 per million input tokens and $7.50 per million output tokens across its 1 million-token context window. Google also launched Orchestrator in Google Antigravity, which switches between models like Carbon and Hydrogen within a session and reached near-Carbon task success on Terminal-Bench 4.0 while using 24% less quota and finishing 19% faster.

    Why it matters: Google's Gemini 4 Hydrogen targets fast everyday coding and agentic work, with flat $1.50/$7.50 per million token pricing across a 1M context window and a new Orchestrator that switches between Gemini 4 models.

  12. AWS Machine Learning Blog34

    Optimizing cost per task and proving agent ROI on Amazon Bedrock

    This AWS post argues that Amazon Bedrock spend should be judged by cost per task rather than cost per token, since tokenizer efficiency, output verbosity, agentic loop count and reasoning overhead can make a lower-priced model cost more. A worked example shows a model with 2x the list price finishing 65% cheaper per task, and the post adds caching, routing and output constraints plus a five-dimension scorecard for measuring agent ROI against human baselines.

  14. Databricks Blog22

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

    H6.ai automates accounts payable on top of customers' existing ERPs, using a dedicated Lakebase Postgres database per customer as its system of record. Because invoice data keeps changing, the team uses Lakebase copy-on-write branches to freeze snapshots of customer databases and run each regression test on its own short-lived branch, so the live database is never written to. Each run is judged by deterministic assertions plus a panel of AI judges from different model providers.

  15. AWS Machine Learning Blog38

    Amazon Bedrock AgentCore Evaluations: Built-in, DeepEval and AutoEval evaluators

    Amazon Bedrock AgentCore Evaluations now lets teams run AWS built-in evaluators alongside managed DeepEval and AutoEval metrics, plus custom evaluators derived from either. The built-in catalog spans 15 evaluators across response quality, goal completion, tool use, skills and safety, with three trajectory checks for expected tool paths. DeepEval adds multi-turn metrics such as ConversationCompleteness and KnowledgeRetention, while AutoEval contributes specialized signals including security.

  16. AWS Machine Learning Blog36

    Building a Daily Dependency Watcher with LangGraph and Amazon Bedrock AgentCore Web Search

    The Web Search API in Amazon Bedrock AgentCore is now available, giving agents current web results through a single SigV4-signed API call served from a web index Amazon operates. A walkthrough builds a daily dependency watch as a LangGraph graph that researches npm, PyPI, and container base images in parallel, with date windows, blocklists, and search budgets enforced in code. In testing, the three branches finished in 36 seconds versus 99 seconds run sequentially.

  17. AWS Machine Learning Blog58

    Moonshot AI's Kimi K3.1 Pro Highspeed launches on Amazon Bedrock

    Moonshot AI's Kimi K3.1 Pro Highspeed is now available on Amazon Bedrock, a post-trained version of Kimi K3 with the same 3T-class open-weight architecture and 1-million-token context window, trained with more post-training compute for long-horizon agentic work and fewer factual errors. Moonshot AI reports 2–3 times the output speed of Kimi K3.1 Pro, and existing Kimi K3 users can switch by changing the model ID. The post shows Chat Completions, Responses and Converse API calls, the Flex tier at half the Standard price, explicit prompt caching with prompt_cache_breakpoint, and connecting the model to the OpenCode coding agent.

  18. AWS Machine Learning Blog33

    Amazon SageMaker AI bidirectional streaming gains usage-based pricing, Deepgram as launch partner

    Amazon SageMaker AI bidirectional streaming now supports usage-based metering, letting model providers report actual consumption to AWS Marketplace software charges while endpoint infrastructure fees still apply. Deepgram is the launch partner, bringing usage-based Speech-to-Text and Text-to-Speech billing to AWS Marketplace. The design adds a companion WebSocket metadata stream, capped at one message per second and 512 bytes, so providers can send incremental metering records during live sessions.

  19. AWS Machine Learning Blog60

    AWS open-sources UAEF, a framework-agnostic evaluation system for AI agents

    AWS has open-sourced UAEF (Universal Agent Evaluation Framework), a framework-agnostic system that uses adapters to normalize agent traces from LangGraph, LangChain, Strands Agents, Amazon Bedrock AgentCore and Langfuse into one canonical AgentTrace, then scores them with 36 built-in metrics across 7 dimensions. Results are stored per experiment in Amazon DynamoDB and Amazon S3, so teams can compare agent versions against a baseline, flag regressions with a default 5 percent threshold, and generate statistical reports plus LLM-written insights. The post says customers across 12 organizations in telecom, healthcare and financial services used UAEF before release, and the Apache 2.0 code, Python library, CDK deployment and per-framework notebooks are in the awslabs/uaef GitHub repository.

    Why it matters: The AWS team has open-sourced UAEF, which uses adapters to unify agent trajectories from different frameworks into a single format, then scores them across 7 dimensions using 36 metrics, providing a reusable evaluation layer for comparing agent versions across frameworks.

  20. AWS Machine Learning Blog36

    AWS Quick AI-PDLC: Six Agents for AI-Driven Product Development

    AWS Quick introduces AI-PDLC, a framework of six specialized agents—led by an Orchestrator covering Discovery, Prioritize, Working Backwards, Spec Generator, and Designer—that turns scattered enterprise signals from Slack, Jira, Salesforce and SharePoint into a traceable prototype_spec.md. Every phase requires human approval, and the spec can be handed to coding agents like Kiro or Claude Code via the open Agent Client Protocol (ACP).

  21. AWS Machine Learning Blog40

    AWS opens catalog of 23 reusable generative AI assets from its Innovation Center

    AWS has published a catalog of 23 production-ready, open source generative AI assets from its Generative AI Innovation Center, now part of AWS Frontier AI Engineering & Services. The building blocks cover RAG pipelines, document processors, agent scaffolding and evaluation frameworks, and AWS says they can cut development from months to weeks. DoorDash reported a 50x increase in testing capacity and Ricoh a 7-fold throughput increase after building on the assets.

  22. AWS Machine Learning Blog38

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

    DXC Technology built the model substrate for its DXC OASIS IT-operations harness on Amazon Bedrock, reaching Anthropic Claude, Amazon Nova, Meta Llama and open-weight models through one managed interface. Amazon Bedrock Guardrails apply content filtering, PII redaction and denied-topic policies uniformly across every model, while AWS PrivateLink keeps inference traffic inside the customer VPC. DXC OASIS has been generally available since March 2026 and cut resolution time by 95% on average across 56 measured use cases.

  23. AWS Machine Learning Blog40

    Agent DLC: Engineering AI Agents from Prototype to Production

    AWS introduces the Agent Development Lifecycle (Agent DLC), a framework with one lifecycle, two loops, and a single explicit standard for judging every agent change. It addresses three agent properties that break conventional CI/CD: non-determinism, prompts as source code, and independently moving dependencies. A reference HR policy assistant shows chunk-level retrieval precision of 1.00 across all three test questions while fact-level precision fell to 0.09 and retrieval coverage hit 0.00 on one question.

  24. AWS Machine Learning Blog36

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

    AWS shows how to give a shared AI agent per-user document access on Amazon Bedrock Managed Knowledge Base by exposing search as MCP tools through AgentCore Gateway. A request interceptor injects the verified user's email from the Cognito JWT into userContext, and ACL awareness returns only documents that user may view. A Cedar policy in AgentCore and a resource-based policy on the knowledge base add secondary defenses against spoofed or unauthorized searches.

  25. AWS Machine Learning Blog58

    AWS walks through building cross-organization multi-agent systems with A2A

    AWS published a reference architecture in which a LangChain-based enterprise orchestrator on Amazon ECS calls a Strands Agents-based compliance SaaS agent on Amazon Bedrock AgentCore over the A2A protocol, with the two sides in separate AWS accounts and no shared secrets. Each side authenticates with tokens from its own Amazon Cognito user pool, and the SaaS agent calls back to an enterprise context agent for account metadata, so the agents act as peers rather than client and server. The system deploys from three Jupyter notebooks that provision five CloudFormation stacks and run an end-to-end test plus seven trust boundary checks.

  26. AWS Machine Learning Blog34

    Migrating to Amazon Bedrock Managed Knowledge Base with a coding agent

    AWS added a knowledge base migration guide to the amazon-bedrock skill in the Agent Toolkit for AWS, letting a coding agent plan a move from a customer-managed knowledge base to Amazon Bedrock Managed Knowledge Base. The agent starts with read-only discovery and a line-by-line reviewed plan, then creates the new knowledge base and data sources alongside the original or writes a migrated IaC template. Managed Knowledge Base raises default quotas to 200 data sources per knowledge base and 10,000 knowledge bases per account per Region, and adds agentic retrieval, AgentCore Gateway integration, and resource-based policies.

  27. Replit Blog65

    Replit Agent adds Link and Visa purchasing plus Google Ads campaign creation

    Replit Agent now has two new jobs: buying business supplies through Link and Visa, and building Google Ads campaigns across Search, YouTube, Display and Discover. In the purchasing flow, a browser subagent navigates the merchant site and prepares checkout, then waits for the owner to approve the final total; shell access and general browsing are disabled during payment and approval checks are enforced in code. Agentic Commerce is available for Core and Pro users starting in the US with one-time purchases only, while Agentic Ads is available for Pro users worldwide.

    Why it matters: Replit Agent now handles purchasing and Google Ads campaigns, with code-enforced approval gates that show how agent spending limits can be designed.

November 12Thursday
  2. Databricks Blog58

    How Block uses Buzz and Databricks Unity Gateway to run collaborative AI agents

    Block built Buzz, an open source collaboration workspace where employees and AI agents work together in group discussions, focused conversations and one-on-one exchanges, and uses Databricks Unity Gateway as the model access layer. Employees can create agents, pick the model or coding harness behind each one, and select Databricks as their inference provider to reach frontier models from OpenAI, Anthropic and Google plus select open-weight models through a single integration. Block engineers are also experimenting with multi-agent setups where a more capable model coordinates agents running smaller, more efficient models, and have built an internal approach to benchmarking these agent teams.

  4. LangChain Blog40

    How LangChain's GTM Agent builds consistent, high-quality sales artifacts

    LangChain's internal GTM Agent generated 1,540 sales artifacts over 12 weeks, saving reps 337 hours on documents like account plans and proposal decks. 47% of deals with a sent artifact advanced to a later stage, versus 16% of matched deals without one, and 46% of artifacts were followed by a customer call within 14 days. The agent runs on the Deep Agents harness, whose sandbox, skills, memory, and scheduled runs are available in Managed Deep Agents.

  5. AWS Machine Learning Blog36

    Build autonomous AI agents with Amazon Bedrock and Spring AI

    AWS shows Java teams how to build autonomous AI agents using Spring AI and Amazon Bedrock's AgentCore, with a spring-ai-agentcore integration. Two reference systems are detailed: a single-agent travel assistant with tool calling and memory, and a multi-agent intelligent document processing platform with five specialist agents, a rules engine, and alerting. The stack requires JDK 21, Spring Boot 4.1 with Spring AI 2.0, and deploys via AgentCore runtime on ARM64 containers.

  6. LiveKit Blog55

    LiveKit launches Cloud API in open beta so coding agents can manage LiveKit Cloud

    LiveKit released the LiveKit Cloud API in open beta, letting coding agents create and configure projects, manage members and webhooks, and pull transcripts, logs, traces and recordings from agent calls. The API is a REST API that runs signed in as the user, with short-lived tokens checked against project roles and sensitive actions limited to project admins. In a test on LiveKit's docs assistant, a coding agent read 4,321 sessions from the past week and returned a ranked list of 8 issues after about 30 minutes. Session data requires Agent Observability, which is off by default for projects created through the API, and API requests are free but rate limited by plan.

  7. OpenRouter Announcements58

    OpenRouter 发布教程：如何阻止 AI Agent 陷入无限循环

    OpenRouter published a guide on stopping AI agents that get stuck retrying a failing tool, using four guards tested on six current models. With doomLoop detection enabled, all six models were blocked on their third identical tool call, and across 18 runs tool calls fell from 314 to 64 while spend dropped from $0.29 to $0.05. The guide adds a loopKey to normalize arguments, stopWhen caps on steps and cost, and a credit limit on the API key that returns a 402 with limit_source openrouter_key_limit.

  8. OpenRouter Announcements28

    How to Benchmark LLMs: A Code-Graded Guide from OpenRouter

    OpenRouter published a guide for benchmarking LLMs on your own application's inputs, grading outputs with code and comparing accuracy, billed cost and request time. On its 60 synthetic tasks, GPT-6.1 Sol passed 180/180 trials across three repeats, while DeepSeek had the lowest response cost at 157/180 passes. Agreement with the public ranking was weak, with a Spearman correlation of 0.109.

  9. AWS Machine Learning Blog36

    Running OpenAI Codex on Amazon Bedrock: Access Paths and Rollout Guide

    This post shows how to route OpenAI Codex model requests to Amazon Bedrock while reusing existing AWS identity, IAM, Regional, and observability practices. It recommends starting with Native AWS Access for a small least-privilege pilot, with AgentCore Gateway or an LLM Gateway as alternatives, and walks through IAM Identity Center sign-in plus a direct Bedrock Runtime check using GPT-5.6 Sol.

  10. AWS Machine Learning Blog34

    Semantic diffing of formal policies with anti-unification

    An open source engine computes semantic diffs of formal policies using anti-unification over an e-graph, showing shared structure once and keeping both versions of each differing part. It targets autoformalization outputs in SMT-LIB, Cedar, Dogwood, OpenShell policies and CEL, where an LLM writes the artifact but plays no part in computing the diff.

  11. AWS Machine Learning Blog41

    Amazon Bedrock AgentCore Web Search API Released

    Amazon Bedrock AgentCore now offers a standalone Web Search API, letting developers call InvokeWebSearch directly from AWS SDKs or the AWS CLI with a single request and no resources to create. It returns ranked results from an Amazon-operated web index, each with a text extract and, when available, title, URL, and publication time, with domain and date filters. Requests are signed with SigV4 and IAM, using the same permission as the AgentCore Gateway route.

  12. AWS Machine Learning Blog36

    What landed for AI builders on AWS in October 2026: Claude Haiku 5.5, GPT-6.1 Terra, AgentCore CLI 1.0

    AWS's October 2026 roundup adds Claude Haiku 5.5, which Anthropic says costs around 75 percent less than Claude Haiku 4.5 for most tasks, plus OpenAI GPT-6.1 Terra on Amazon Bedrock with up to 1 million input tokens and prices at least 50 percent lower than GPT-6.1 Sol and GPT-5.6 Terra. AgentCore CLI reached version 1.0, and AgentCore Gateway added model fallback and Step Functions workflows as agent tools.

  13. AWS Machine Learning Blog12

    AWS re:Invent 2026 AI/ML Track: Attendee's Guide

    AWS will hold its annual re:Invent conference from November 30 to December 4, with the AI/ML track offering more than 140 unique sessions out of over 2,000 total learning sessions. The guide maps sessions to the AWS AI stack, spanning AI chips (AWS Trainium and NVIDIA GPUs), models and inference on Amazon Bedrock and SageMaker AI, and agent development with Amazon Bedrock AgentCore. Sessions are organized from newcomer content to technical deep dives, and many can be viewed on demand.