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Claude Marketplace adds security, productivity, and creative agents and products
Claude Marketplace adds partner agents and products spanning security, development infrastructure, creativity, and productivity, bringing the total number of partner agents and products on the platform to more than 25. Eligible enterprise customers can apply a portion of their Anthropic committed spend to these solutions, sharing a single committed amount across Claude and partner products. New products include Chainguard Harness, Checkmarx One, Datadog Agent Observability, Runway, Asana AI Teammates, and Zoom Agentic Search, among others.
Claude Marketplace adds partner agents and products spanning security, development infrastructure, creativity, and productivity, bringing the total number of partner agents and products on the platform to more than 25. Eligible enterprise customers can apply a portion of their Anthropic committed spend to these solutions, sharing a single committed amount across Claude and partner products. New products include Chainguard Harness, Checkmarx One, Datadog Agent Observability, Runway, Asana AI Team
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AI Voice Agent Pricing: Cost Structure and Planning Methods
The cost of ElevenAgents' AI voice agents consists of three parts: platform per-minute fees, LLM usage, and phone lines, with LLM and telephony costs billed separately at cost. The platform rate for all paid plans is a uniform $0.08 per minute; the free tier includes 15 minutes per month, the Pro plan at $99/month includes 1,238 minutes, and the Scale plan at $299/month includes 3,738 minutes. A plan's concurrent call limit is just as important as its per-minute rate; usage beyond standard capacity is billed at $0.16 per minute.
The cost of ElevenAgents' AI voice agents consists of three parts: platform per-minute fees, LLM usage, and phone lines, with LLM and telephony costs billed separately at cost. The platform rate for all paid plans is a uniform $0.08 per minute; the free tier includes 15 minutes per month, the Pro plan at $99/month includes 1,238 minutes, and the Scale plan at $299/month includes 3,738 minutes. A plan's concurrent call limit is just as important as its per-minute rate; usage beyond standard capac
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Claude Platform natively supports OpenTelemetry, with trace data integrated into Databricks MLflow
Claude Platform's native OpenTelemetry export has entered Public Beta. Traces from the Anthropic SDK, Messages API, and Claude Managed Agents can be written to Unity Catalog tables via Zerobus Ingest and viewed in Databricks MLflow. Developers can use Analyze trace and the Beta-stage MLflow Issues to pinpoint incorrect answers and duplicate tool calls, then evaluate the effect of changes using the same set of test cases; inputs and outputs are captured only when content mode is set to full.
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Claude Code adds effort control and /usage usage details
Claude Code now supports typing ultrathink in a message to raise the effort of a single turn to xhigh, and allows specifying a separate effort level for subagents. The CLI's /usage now explains in plain language what is consuming your quota, and if a quick fix is available, you can press f to apply it directly.
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Claude Code announces several small October updates
In reviewing the October Claude Code updates, Claude Devs added several smaller changes: cloud sessions begin preparing as soon as the user types a prompt; users can leave comments on any line of a cloud session diff, and the comments are sent to Claude with the next message; the Max, Ultra, and Team plans now include a monthly API allowance for the Claude API and Agent SDK; MCP servers that support the feature can ask follow-up questions mid-task and can run long tasks in the background.
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Behind the Scenes of Krea's Biggest Launch Video
Krea explained how @viccpoes used Krea to create its biggest launch video: first using Claude + Krea MCP to storyboard in Figma, then setting keyframes with the Krea Agent in After Effects, and finally letting the agent take full control of After Effects.
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Smartsheet launches AI planning and execution platform vsActual, using Execution Graph to track project deviations
At the Engage 2026 conference, Smartsheet announced vsActual, an AI-driven planning and execution platform that uses AI agents to continuously monitor work progress across enterprise systems, compare it against strategic goals, and recommend adjustments. Its core Execution Graph is built on roughly 20 years of workflow data and 9 PB of information, underpinned by an AWS Neptune knowledge graph of about 100 million nodes, and can connect to systems such as Oracle, SAP, Workday, Atlassian, and ServiceNow. The platform also adds Agentic Workflows, multiple integrations, and ISO/IEC 42001 certification, while retaining human approval by default.
At the Engage 2026 conference, Smartsheet announced vsActual, an AI-driven planning and execution platform that uses AI agents to continuously monitor work progress across enterprise systems, compare it against strategic goals, and recommend adjustments. Its core Execution Graph is built on roughly 20 years of workflow data and 9 PB of information, underpinned by an AWS Neptune knowledge graph of about 100 million nodes, and can connect to systems such as Oracle, SAP, Workday, Atlassian, and Ser
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AWS Details the Four-Layer Context Engineering and Deep Insight Implementation for Production-Grade AI Agents
The AWS Machine Learning Blog uses the open-source multi-agent data analysis solution Deep Insight as a reference implementation and breaks the context engineering of production-grade AI agents into four layers: isolating context through a multi-agent architecture, constraining output with prompts, externalizing heavy content to the file system, and using a validation agent as a fallback. The write_and_execute_tool presented in the article reduces the tokens each script adds to the context by about 95%, and loading Skills on demand shrinks the system prompt by about 97%. Code examples using the Strands Agents SDK are also included.
The AWS Machine Learning Blog uses the open-source multi-agent data analysis solution Deep Insight as a reference implementation and breaks the context engineering of production-grade AI agents into four layers: isolating context through a multi-agent architecture, constraining output with prompts, externalizing heavy content to the file system, and using a validation agent as a fallback. The write and execute tool presented in the article reduces the tokens each script adds to the context by ab
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What Is Call Transcription? A Practical Guide for Businesses
Call transcription is the process of converting phone or video calls into written records, in most cases done automatically by AI models, either transcribed in real time during the call or batch-transcribed after recording. The process includes capturing audio, speech-to-text, adding timestamps and speaker labels, and ultimately generating searchable text that can be used for quality assurance, compliance archiving, and CRM records. Voice AI agents built with ElevenAgents save a transcript with every conversation.
is the process of converting phone or video calls into written records, in most cases done automatically by AI models, either transcribed in real time during the call or batch-transcribed after recording. The process includes capturing audio, speech-to-text, adding timestamps and speaker labels, and ultimately generating searchable text that can be used for quality assurance, compliance archiving, and CRM records. Voice AI agents built with ElevenAgents save a transcript with every conversation.
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In the year when AI databases were released in droves, what did products such as the Electric Power Golden Warehouse KES AI upgrade?
At the end of October, Electric Power Golden Warehouse released the KES AI database, incorporating semantic construction, computational reasoning, and native data security control into the database kernel, emphasizing that AI applications can be supported without data leaving the database. Alibaba Cloud PolarDB launched capabilities such as the AI data lakehouse (Lakebase) in January this year and launched PolarDB Agentic Database in July, while OceanBase packaged its AI direction into the OceanBase AI Database product portfolio in June. IDC data shows that the market size of China's AI data infrastructure in 2025 is approximately 14.9 billion US dollars, and will reach 73.8 billion US dollars by 2030.
sizing that AI applications can be supported without data leaving the database. Alibaba Cloud PolarDB launched capabilities such as the AI data lakehouse (Lakebase) in January this year and launched PolarDB Agentic Database in July, while OceanBase packaged its AI direction into the OceanBase AI Database product portfolio in June. IDC data shows that the market size of China's AI data infrastructure in 2025 is approximately 14.9 billion US dollars, and will reach 73.8 billion US dollars by 2030.
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How Circle K Cut DevOps Incident Triage to Under 15 Minutes with Amazon Bedrock
Circle K built an agentic automated triage system with Amazon Bedrock, reducing average DevOps incident triage time to under 15 minutes—a 67–75% reduction according to internal team metrics. In the system, Grafana alerts trigger AWS Lambda via Amazon SNS and Amazon SQS to invoke Amazon Bedrock Flows, which orchestrates multiple specialized agents in parallel to query logs and infrastructure, and pushes structured summaries to Slack; it has since migrated to Amazon Bedrock AgentCore.
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Using Amazon Nova Multimodal Embeddings with Bedrock…
AWS demonstrated a multi-agent engineering assistant based on Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open-source Strands Agents SDK, designed to reduce manual cross-checking in parts substitution evaluations. In the system, a planner routes tasks to three expert agents—Catalog, Evaluation, and Knowledge—which are respectively responsible for multimodal retrieval, scoring according to Python tolerance rules, and verifying lifecycle information using real-time web data, with a supervisor ultimately aggregating the results. The system only assembles evidence and drafts review documents; it does not itself recommend or approve substitutions, and every evaluation writes an audit record aligned with AS9100 8.4 and 8.5.6.
AWS demonstrated a multi-agent engineering assistant based on Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open-source Strands Agents SDK, designed to reduce manual cross-checking in parts substitution evaluations. In the system, a planner routes tasks to three expert agents—Catalog, Evaluation, and Knowledge—which are respectively responsible for multimodal retrieval, scoring according to Python tolerance rules, and verifying lifecycle information using real-time web dat
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Pydantic AI Compares Nine Python Agent Frameworks in 2026
Pydantic AI officially released a 2026 comparison of Python AI agent frameworks, evaluating nine frameworks item by item against criteria such as model switchability, durable execution after crashes, human approval, frontend and voice integration, MCP, and observability, and giving the applicable scenarios for each. The article states that Pydantic AI supports 30+ model providers, 8 durable execution engines (Temporal, DBOS, Prefect, Restate, AWS Lambda, Absurd, pgtask, Apache Airflow), 5 speech-to-speech providers, and 10 sandboxes, while also noting that it supports only Python, does not offer a hosted platform, and has fewer retrieval-related integrations than LangChain. The comparison also covers LangGraph/LangChain, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, Strands Agents, CrewAI, Claude Agent SDK, and Agno, and separately lists the two voice frameworks LiveKit Agents and Pipecat.
Pydantic AI officially released a 2026 comparison of Python AI agent frameworks, evaluating nine frameworks item by item against criteria such as model switchability, durable execution after crashes, human approval, frontend and voice integration, MCP, and observability, and giving the applicable scenarios for each. The article states that Pydantic AI supports 30+ model providers, 8 durable execution engines (Temporal, DBOS, Prefect, Restate, AWS Lambda, Absurd, pgtask, Apache Airflow), 5 speech
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Apple proposes an online dense reinforcement learning framework: a planning-grounding agent navigating real iOS applications
Apple proposes an online reinforcement learning framework that directly trains a UI navigation agent on real iOS applications, splitting the agent into a Planning agent and a UI Grounding model, and uses an LLM-as-a-Judge to provide step-level dense rewards and task-completion terminal rewards, optimizing with PPO plus step-level advantage estimation. Its 8B agent achieves nearly a 7x improvement over the SFT baseline when generalizing out-of-distribution to the Android environment, and on real iOS applications surpasses Gemini-3.5-Flash and matches Gemini-3.1-Pro.
Apple proposes an online reinforcement learning framework that directly trains a UI navigation agent on real iOS applications, splitting the agent into a Planning agent and a UI Grounding model, and uses an LLM-as-a-Judge to provide step-level dense rewards and task-completion terminal rewards, optimizing with PPO plus step-level advantage estimation. Its 8B agent achieves nearly a 7x improvement over the SFT baseline when generalizing out-of-distribution to the Android environment, and on real
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How Figma brought Hello Kitty and Friends into FigJam
Figma partnered with Sanrio to launch Hello Kitty and Friends in FigJam for one week starting November 1. Users can choose one of 12 Sanrio characters to join brainstorming, and each character comes with its own cursor, washi tape, and sticky note theme. The team used Figma Motion to create the loading animation and used Figma agent to complete batch edits; a free Hello Kitty birthday pop-up event will also be held in New York from November 6–8.
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Tencent Hunyuan Releases Research Agent Hyra 2.0
Tencent Hunyuan has released Hyra 2.0, the next-generation version of the Hunyuan Research Agent, positioned to shift from solving problems to building reusable tools, models, and workflows in order to accelerate subsequent discoveries. The official team also published its views on the next stage of AutoResearch, with the link https://t.co/u2JSslKYai.
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Warm Transfer vs. Cold Transfer: How to Choose the Right Call Transfer Method
The ElevenLabs blog compared the differences between warm transfer and cold transfer in call centers: warm transfer provides the receiving party with background information about the caller before connecting, while cold transfer connects directly without prior notice. The article recommends warm transfer for complex or sensitive issues and cold transfer for simple, high-frequency routing, and notes that ElevenAgents supports three transfer types, with Conference Transfer on its native Twilio integration able to pass warm transfer information to human agents.
er and cold transfer in call centers: warm transfer provides the receiving party with background information about the caller before connecting, while cold transfer connects directly without prior notice. The article recommends warm transfer for complex or sensitive issues and cold transfer for simple, high-frequency routing, and notes that ElevenAgents supports three transfer types, with Conference Transfer on its native Twilio integration able to pass warm transfer information to human agents.
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