On October 29, 2026, ModelBest OpenBMB announced that its agent, starting from scratch in just four hours, has reached performance very close to the frontier level. The post also cited an earlier post about ForgeKernel Live optimizing the DeepSeek kernel. At present, this claim appears only in content published by OpenBMB itself, and there are no independent reports or follow-up developments from other sources.
Arena released measured results based on nearly 9,000 agent sessions: OpenAI's GPT-6.1 Terra (Max) ranks 8th in Agent Arena, with a net improvement of +10.6% and a median cost per task of $0.62. This cost is lower than the $1.09 of 7th-place Gemini 4 Argon (High), the $0.89 of 6th-place GPT-6.1 Sol (Max), and the $5.16 of 5th-place GPT-6 Astra (Max). In the subcategory rankings, it places 3rd in Bash Reliability (+13.2%), 7th in Steerability (+13.3%), and 9th in Praise vs Complaint (+16.8%). There are currently no further developments.
Arena released measured results based on nearly 9,000 agent sessions: OpenAI's GPT-6.1 Terra (Max) ranks 8th in Agent Arena, with a net improvement of +10.6% and a median cost per task of $0.62. This cost is lower than the $1.09 of 7th-place Gemini 4 Argon (High), the $0.89 of 6th-place GPT-6.1 Sol (Max), and the $5.16 of 5th-place GPT-6 Astra (Max). In the subcategory rankings, it places 3rd in Bash Reliability (+13.2%), 7th in Steerability (+13.3%), and 9th in Praise vs Complaint (+16.8%). The
Arena.ai published a per-task cost comparison of models on Agent Arena on October 30, 2026. Statistics show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% lower than Claude Opus 5.5 (Max) at $12.51 and 87% lower than GPT-6 Astra (Max) at $14.93. Arena.ai noted that these two cost advantages come from different sources: relative to Astra, Sol's advantage mainly comes from a lower per-token price; relative to Opus, it mainly comes from consuming fewer tokens per task. Per-task cost is calculated by multiplying the number of input, output, and cache-read tokens by their respective unit prices, and Arena.ai identifies the reasons for the cost reduction by comparing prices and token usage.
Arena.ai published a per-task cost comparison of models on Agent Arena on October 30, 2026. Statistics show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% lower than Claude Opus 5.5 (Max) at $12.51 and 87% lower than GPT-6 Astra (Max) at $14.93. Arena.ai noted that these two cost advantages come from different sources: relative to Astra, Sol's advantage mainly comes from a lower per-token price; relative to Opus, it mainly comes from consuming fewer tokens per task. Per-task cost is c
Arena.ai published a per-task cost comparison of models on Agent Arena on October 30, 2026. Statistics show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% lower than Claude Opus 5.5 (Max) at $12.51 and 87% lower than GPT-6 Astra (Max) at $14.93. Arena.ai noted that these two cost advantages come from different sources: relative to Astra, Sol's advantage mainly comes from a lower per-token price; relative to Opus, it mainly comes from consuming fewer tokens per task. Per-task cost is calculated by multiplying the number of input, output, and cache-read tokens by their respective unit prices, and Arena.ai identifies the reasons for the cost reduction by comparing prices and token usage.
Herdr has launched a desktop app for terminals and coding agents that organizes work by space and summarizes each agent's Working, Blocked, Done, and Idle status in a sidebar. After the app window is closed, terminals continue running in the core, and spaces can also be placed on remote machines for execution via SSH. The app supports Linux, macOS (Apple Silicon), and Windows x64, includes three built-in layouts—Herdr, Terminals, and Herdr 98—and allows mod panels to be written in JavaScript. There are currently no reports of further developments.
Herdr has launched a desktop app for terminals and coding agents that organizes work by space and summarizes each agent's Working, Blocked, Done, and Idle status in a sidebar. After the app window is closed, terminals continue running in the core, and spaces can also be placed on remote machines for execution via SSH. The app supports Linux, macOS (Apple Silicon), and Windows x64, includes three built-in layouts—Herdr, Terminals, and Herdr 98—and allows mod panels to be written in JavaScript. Th
Herdr has launched a desktop app for terminals and coding agents that organizes work by space and summarizes each agent's Working, Blocked, Done, and Idle status in a sidebar. After the app window is closed, terminals continue running in the core, and spaces can also be placed on remote machines for execution via SSH. The app supports Linux, macOS (Apple Silicon), and Windows x64, includes three built-in layouts—Herdr, Terminals, and Herdr 98—and allows mod panels to be written in JavaScript. There are currently no reports of further developments.
On October 29, Akamai released beta versions of two products, Akamai Inference and Akamai Agent Sandbox, allowing users to run AI models on Akamai Cloud and securely execute code generated by AI agents. According to The Elec, Akamai Inference supports 12 open-weight models, including OpenAI's gpt-oss-120b, Google's Gemma 4, and Nvidia's Nemotron 3 Ultra, and provides an OpenAI-compatible API; Agent Sandbox is based on WebAssembly isolation technology and restricts code access to computing resources, network connections, and system functions. Analytics India Magazine separately reported that Agent Sandbox is released as an open-source component of the Spin framework, and that Akamai is also expanding its NVIDIA Blackwell Ultra GPU cluster, with each eight-GPU node equipped with 2.3 TB of HBM3e memory. Both services are currently in the beta stage.
On October 29, Akamai released beta versions of two products, Akamai Inference and Akamai Agent Sandbox, allowing users to run AI models on Akamai Cloud and securely execute code generated by AI agents. According to The Elec, Akamai Inference supports 12 open-weight models, including OpenAI's gpt-oss-120b, Google's Gemma 4, and Nvidia's Nemotron 3 Ultra, and provides an OpenAI-compatible API; Agent Sandbox is based on WebAssembly isolation technology and restricts code access to computing resour
ConnectWise has released RMM Agent, a product that can check associated devices within service tickets, test possible causes, and stage remediation plans from a vetted catalog, with all device changes requiring explicit technician approval. The simultaneously launched Voice, Teams, and Email Agents are used to collect customer information before technical work begins, and all four products are now generally available. Partner RealBytes reports that across more than 500 live tickets, 91% received useful diagnostics, typically taking about one minute, with first response time shortened from one hour to five minutes.
ConnectWise has released RMM Agent, a product that can check associated devices within service tickets, test possible causes, and stage remediation plans from a vetted catalog, with all device changes requiring explicit technician approval. The simultaneously launched Voice, Teams, and Email Agents are used to collect customer information before technical work begins, and all four products are now generally available. Partner RealBytes reports that across more than 500 live tickets, 91% received
Emro has signed a contract with GS Caltex to develop a procurement automation AI Agent for the latter. According to The Elec, the agent is used to handle approximately 2,700 small-value material procurement transactions per year at GS Caltex, and can analyze procurement requests and complete the entire process from inquiry, quote evaluation, to supplier selection and purchase order issuance, while cases requiring additional review are forwarded to procurement personnel for manual handling. The project is based on Emro's self-developed AI Studio platform and is integrated with GS Caltex's existing procurement system, with plans to expand to other procurement operations beyond small-value procurement in the future.
Emro has signed a contract with GS Caltex to develop a procurement automation AI Agent for the latter. According to The Elec, the agent is used to handle approximately 2,700 small-value material procurement transactions per year at GS Caltex, and can analyze procurement requests and complete the entire process from inquiry, quote evaluation, to supplier selection and purchase order issuance, while cases requiring additional review are forwarded to procurement personnel for manual handling. The p
At the Android Dev Summit, Google announced that Android Studio and the Android CLI are being opened to third-party coding agents. According to The New Stack, Bring Your Own Agent already supports more than 40 agents that follow the Agent Client Protocol, including Claude Agent, Codex, and Google's own Antigravity, and it has been made stable in Android Studio Rabbit 2. The new android preview command lets agents render Jetpack Compose previews in the terminal; Google says this completes UI tasks 77% faster and cuts token usage by up to three times, but it did not explain how this was measured. android device remote, meanwhile, gives agents access to physical devices in Google's labs. In a demo, Claude Code used LeakCanary to investigate a memory leak and then fixed and retested it on its own; Google says such investigations are nearly twice as fast and use up to 25% fewer tokens. Google also launched Android Bench 2.0 to evaluate how models and agents perform on complex Android tasks.
At the Android Dev Summit, Google announced that Android Studio and the Android CLI are being opened to third-party coding agents. According to The New Stack, Bring Your Own Agent already supports more than 40 agents that follow the Agent Client Protocol, including Claude Agent, Codex, and Google's own Antigravity, and it has been made stable in Android Studio Rabbit 2. The new android preview command lets agents render Jetpack Compose previews in the terminal; Google says this completes UI task
At the Android Dev Summit, Google announced that Android Studio and the Android CLI are being opened to third-party coding agents. According to The New Stack, Bring Your Own Agent already supports more than 40 agents that follow the Agent Client Protocol, including Claude Agent, Codex, and Google's own Antigravity, and it has been made stable in Android Studio Rabbit 2. The new android preview command lets agents render Jetpack Compose previews in the terminal; Google says this completes UI tasks 77% faster and cuts token usage by up to three times, but it did not explain how this was measured. android device remote, meanwhile, gives agents access to physical devices in Google's labs. In a demo, Claude Code used LeakCanary to investigate a memory leak and then fixed and retested it on its own; Google says such investigations are nearly twice as fast and use up to 25% fewer tokens. Google also launched Android Bench 2.0 to evaluate how models and agents perform on complex Android tasks.
On October 29, 2026, James Ward, Principal Developer Advocate at AWS, proposed that MCP servers should not merely be APIs that "fetch data," but should attach guidance when returning data, telling the agent what it has just done and what it can do next. He also noted that MCP has no trace ID by default; AWS's approach is to add a session ID parameter to tools, having the agent pass it in on every call, thereby stringing individual calls into a complete chain. The same report mentions that AWS opened MCP Server to everyone in May this year and added Lambda diagnostics in September. Currently, this view has been reported by The New Stack, with no cross-verification from other sources or subsequent developments yet.
James Ward, Principal Developer Advocate at AWS, proposed that MCP servers should not merely be APIs that "fetch data," but should attach guidance when returning data, telling the agent what it has just done and what it can do next. He also noted that MCP has no trace ID by default; AWS's approach is to add a session ID parameter to tools, having the agent pass it in on every call, thereby stringing individual calls into a complete chain. The same report mentions that AWS opened MCP Server to ev
On October 30, 2026, Microsoft opened the public beta of the GitHub Copilot agent harness through the Foundry Agent Service in Microsoft Foundry. The harness provides built-in sandboxing, reusable skills, MCP integration, and sub-agent support; the same harness already supports Copilot Code, Copilot Studio, GitHub Copilot, CLI, and VS Code. When creating a prompt agent, this harness must be selected and cannot be changed afterward; CPU, memory, and idle duration are controlled by Microsoft, and bring-your-own-model is not supported. The service is free until December 1, 2026, after which it is billed at $0.0994 per vCPU-hour and $0.0118 per GiB-hour, with model inference and some tools billed separately.
On October 30, 2026, Microsoft opened the public beta of the GitHub Copilot agent harness through the Foundry Agent Service in Microsoft Foundry. The harness provides built-in sandboxing, reusable skills, MCP integration, and sub-agent support; the same harness already supports Copilot Code, Copilot Studio, GitHub Copilot, CLI, and VS Code. When creating a prompt agent, this harness must be selected and cannot be changed afterward; CPU, memory, and idle duration are controlled by Microsoft, and
On October 30, 2026, Microsoft opened the public beta of the GitHub Copilot agent harness through the Foundry Agent Service in Microsoft Foundry. The harness provides built-in sandboxing, reusable skills, MCP integration, and sub-agent support; the same harness already supports Copilot Code, Copilot Studio, GitHub Copilot, CLI, and VS Code. When creating a prompt agent, this harness must be selected and cannot be changed afterward; CPU, memory, and idle duration are controlled by Microsoft, and bring-your-own-model is not supported. The service is free until December 1, 2026, after which it is billed at $0.0994 per vCPU-hour and $0.0118 per GiB-hour, with model inference and some tools billed separately.
On October 29, 2026, ModelBest OpenBMB announced that its agent, starting from scratch in just four hours, has reached performance very close to the frontier level. The post also cited an earlier post about ForgeKernel Live optimizing the DeepSeek kernel. At present, this claim appears only in content published by OpenBMB itself, and there are no independent reports or follow-up developments from other sources.
Arena released measured results based on nearly 9,000 agent sessions: OpenAI's GPT-6.1 Terra (Max) ranks 8th in Agent Arena, with a net improvement of +10.6% and a median cost per task of $0.62. This cost is lower than the $1.09 of 7th-place Gemini 4 Argon (High), the $0.89 of 6th-place GPT-6.1 Sol (Max), and the $5.16 of 5th-place GPT-6 Astra (Max). In the subcategory rankings, it places 3rd in Bash Reliability (+13.2%), 7th in Steerability (+13.3%), and 9th in Praise vs Complaint (+16.8%). There are currently no further developments.
On October 29, Akamai released beta versions of two products, Akamai Inference and Akamai Agent Sandbox, allowing users to run AI models on Akamai Cloud and securely execute code generated by AI agents. According to The Elec, Akamai Inference supports 12 open-weight models, including OpenAI's gpt-oss-120b, Google's Gemma 4, and Nvidia's Nemotron 3 Ultra, and provides an OpenAI-compatible API; Agent Sandbox is based on WebAssembly isolation technology and restricts code access to computing resources, network connections, and system functions. Analytics India Magazine separately reported that Agent Sandbox is released as an open-source component of the Spin framework, and that Akamai is also expanding its NVIDIA Blackwell Ultra GPU cluster, with each eight-GPU node equipped with 2.3 TB of HBM3e memory. Both services are currently in the beta stage.
ConnectWise has released RMM Agent, a product that can check associated devices within service tickets, test possible causes, and stage remediation plans from a vetted catalog, with all device changes requiring explicit technician approval. The simultaneously launched Voice, Teams, and Email Agents are used to collect customer information before technical work begins, and all four products are now generally available. Partner RealBytes reports that across more than 500 live tickets, 91% received useful diagnostics, typically taking about one minute, with first response time shortened from one hour to five minutes.
Emro has signed a contract with GS Caltex to develop a procurement automation AI Agent for the latter. According to The Elec, the agent is used to handle approximately 2,700 small-value material procurement transactions per year at GS Caltex, and can analyze procurement requests and complete the entire process from inquiry, quote evaluation, to supplier selection and purchase order issuance, while cases requiring additional review are forwarded to procurement personnel for manual handling. The project is based on Emro's self-developed AI Studio platform and is integrated with GS Caltex's existing procurement system, with plans to expand to other procurement operations beyond small-value procurement in the future.
On October 29, 2026, James Ward, Principal Developer Advocate at AWS, proposed that MCP servers should not merely be APIs that "fetch data," but should attach guidance when returning data, telling the agent what it has just done and what it can do next. He also noted that MCP has no trace ID by default; AWS's approach is to add a session ID parameter to tools, having the agent pass it in on every call, thereby stringing individual calls into a complete chain. The same report mentions that AWS opened MCP Server to everyone in May this year and added Lambda diagnostics in September. Currently, this view has been reported by The New Stack, with no cross-verification from other sources or subsequent developments yet.