Search “Agents”

Avante has completed a $30 million Series A funding round led by Frist Cressey Ventures, bringing total funding to $40 million, with the funds to be used for global expansion and agent platform development. The company has launched a beta version of Ava Agents that runs in the background and outputs for teams to review, and says it has more than 70 customer accounts within 18 months of launch, with the platform covering more than 385,000 employees and dependents. Its technology calls models from

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Replit CEO Amjad Masad has proposed a "self-driving company" vision, arguing that AI agents will compress internal coordination costs within enterprises, and predicts that by the end of 2030 all Fortune 500 companies will become self-driving companies. To this end, Replit has launched Replit for Business and the "Agent Habitat" concept; Cloud Agents are already available to Pro and Enterprise customers, while skills remain in early access. Replit's internal testing shows token consumption droppe

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OpenAI Codex 0.165.0 adds a built-in Google Cloud Vertex AI provider (configured via model provider = "gcp") and adds GPT-6.1 Terra to the model catalog. This version also enables memory v2 by default and supports importing external agent memory, while allowing reasoning effort to be adjusted during conversations by default. The session selector adds repository filtering, and the agent command center supports grouping by directory.

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3.5 Lightning with them. The workflow includes defining behavior contracts, generating synthetic conversations with personas, fine-tuning with LoRA on NVIDIA NeMo, merging adapters, and then comparing and evaluating item by item against the base model on a frozen test set. The France dataset was built in collaboration with Pleias, and the Belgium dataset involved Pleias and KU Leuven; the Nemotron repository provides reusable steps and agent skills for data generation, training, and evaluation.

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ased on intelligence per dollar, and API changes such as requiring thinking blocks to be retained across turns to prevent distillation. The interview also described Claude Managed Agents' approach of separating sessions and the harness from the sandbox that executes code, with credentials mounted separately through a proxy so that a sandbox failure does not take down the entire session; Dreaming, meanwhile, is implemented as one managed agent inspecting another agent's history and writing back t

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general-purpose privacy library that runs within clients across hundreds of data and processing systems and performs billions of checks per second. A system only needs to integrate it once, and new privacy requirements can reuse that integration as modules, without the need to reimplement them in each system. The newly added Agent Observability module can observe data access events of internal AI agents, and systems that have already integrated it can enable it without additional modifications.

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LangChain's GTM engineering team, working with the design team, built Content Studio, a design agent inside Slack, on Managed Deep Agents. It can generate videos, social graphics, UI mockups, and slide decks, and has already produced more than 3,000 assets and over 80 slide decks. The article breaks down its four-layer knowledge structure (skills, wiki, code validation, memory), the dedicated LangSmith Sandbox for each Slack thread, the two rendering engines HyperFrames and the in-house motion-s

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Analysis Intelligence Index v5 with a score of 59, leading in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with the Antigravity CLI scores 59, trailing Claude Code paired with Claude Opus 5.5 (max, 62 points) by 3 points. The model has a 1M-token context window, supports text, image, video, and speech input, and is priced at a standard rate of $4/$20 per million input/output tokens

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formats, among which A2UI Express, as the new default format, can reduce latency by up to 3x and cut the tokens needed to generate comparable UIs by up to 3x; when combined with decision models such as Jev for layout decisions, latency reductions can reach up to 80x. Express is now available in the Python agent SDK (a2ui-agent-sdk 0.9.0), the TypeScript SDK (@a2ui/agent), and the Dart SDK (a2ui agent 0.0.1-wip004); developers can switch formats with a single parameter when creating a generator.

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practical guide on how to build an AI voice assistant by chaining speech-to-text, an LLM, and text-to-speech into a real-time pipeline. The guide recommends keeping round-trip responses within 500 ms and pairing this with VAD, while also connecting to internal knowledge bases and business tools, and putting safety measures such as verbal confirmation and handoff to a human agent in place. Developers can build directly with ElevenAPI, or configure and launch on the ElevenAgents managed platform.

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f 1.05 million tokens, a maximum input of 922,000 tokens, and a maximum output of 128,000 tokens. Reasoning effort can be selected from none to max, with medium as the default. On Agents' Last Exam, the medium setting scores 15 points higher than GPT-5.6 Terra while costing about one-third as much; under the max setting, the average speed for completing tasks is more than 3 times that of GPT-6 Astra and GPT-6.1 Sol. The model is already available to Plus, Pro, Enterprise, and Edu users of ChatGP

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the previous generation, and only half that of GPT-6.1 Sol. The company claims its task completion rate on Terminal-Bench 4.0 increased from 21.5% to 50.3%, and it scored 60.0% on Agents' Last Exam, close to Claude Opus 5.5's 63.2%. Artificial Analysis measured an intelligence index of 48 at the highest reasoning tier, lower than GPT-6.1 Sol's 52 and Claude Opus 5.5's 58, but the average cost per task dropped from $1.40 to $0.40. Terra is now available via API, and Plus, Pro, Enterprise, and Edu

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a, focused on completing complex computer operation tasks at lower cost, with particular strength in visual reasoning and desktop software operation. At medium reasoning effort on Agents' Last Exam, its score is 15 percentage points higher than GPT-5.6 Terra, at about one-third the cost of the latter; at the highest reasoning effort, the cost per task is about one-ninth that of Claude Opus 5.5. The model is now available to Plus, Pro, Enterprise, and Edu users, can be used in ChatGPT Work and Co

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g capabilities. The standard API price is $1 per million input tokens and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. At medium reasoning effort on Agents' Last Exam, its computer operation score is 15 percentage points higher than GPT-5.6 Terra, performing on par with GPT-6 Astra while costing only one-tenth as much per token. The model is now available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, but GPT-6.1 Sol and GPT-6 Astra are still re

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.6 Terra, with API pricing at $1 per million input tokens and $5 for output, half that of GPT-6.1 Sol. The model supports a 1.05M token context and a maximum output of 128K, targets visual reasoning, computer operation and writing, and is offered only through OpenAI-hosted API, ChatGPT Work and Codex. On Agents' Last Exam v1 it scores 60.0% at the highest effort tier at $1.01 per task, and 85.5% on MMMU-Pro vision, but only 50.3% on Terminal-Bench 4.0, trailing GPT-6.1 Sol and Claude Sonnet 5.5.

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visual understanding and computer operation capabilities, with the standard API price reduced by 50% compared to both GPT-5.6 Terra and GPT-6.1 Sol. At medium reasoning effort on Agents' Last Exam, its score is 15 percentage points higher than GPT-5.6 Terra, at about one-third the cost; at the highest reasoning effort on Terminal-Bench 4.0, it completes 50.3% of tasks. The model is available starting today to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, with the API model nam

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, visual reasoning, coding, and everyday office work. API pricing is $1 per million input tokens and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. On Agents' Last Exam, at medium reasoning effort it scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost, and completes tasks more than 3x faster than GPT-6 Astra and GPT-6.1 Sol. Internal benchmarks show the error rate at low reasoning effort dropped from 53.6% to 16.7%, and it scores 50.3% o

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the Artificial Analysis Intelligence Index, at the highest effort level it scores 6 points higher than GPT-5.6 Terra, with a cost per task of $0.40 versus $1.40; at the low and medium effort levels it sits at the frontier of intelligence and cost. On the AA-Omniscience benchmark, the model's guess rate when it does not know the answer dropped from 88% to 38%, while accuracy simultaneously improved by 2 points; Terminal-Bench 4.0 improved by about 20 points, and Coding Agent Index v2.0 scored 48.

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