OpenAI launches the Agents API public beta with the Codex harness and hosted sandboxes

High-Frequency AI Prompts for Work, Learning, and Teaching guide

High-Frequency AI Prompts for Work, Learning, and Teaching

Practical AI Side Project Course guide

Practical AI Side Project Course

A voice-agent release checklist for VAD, noise, interruptions, transcription, tools, and failure handoffs

A voice-agent release should not be accepted after one successful conversation in a quiet room. Test cases need to cover speech-activity detection start and stop behavior, silence and background noise, user interruption, transcription evidence and correction, spoken status around tool calls, and state recovery after timeouts or failures. The OpenAI Realtime API reference documents capabilities for real-time sessions, audio input and output, transcription, and server-side voice activity detection. This article turns those capabilities into a general acceptance checklist. It is not a release announcement, a claim about a particular product version, or a performance guarantee. Teams should define their own language, device, privacy, latency, escalation, and human-review criteria before deployment.

GitHub Copilot Usage Metrics Adds Agent-App Activity

GitHub announced on August 7, 2026 that the Copilot Usage Metrics API now reports activity from third-party agent apps. Enterprise, organization, enterprise-user, and organization-user reports can expose the activity in one-day and 28-day windows. The new totals_by_3rd_party_agent data includes a stable agent_id and a display name that may change; the identifier should be the join key. This gives administrators a finer view of cost, permissions, and workflow adoption, but it does not automatically explain business value. Start with a read-only sample, reconcile time zones, pagination, and overlapping windows, then associate agent activity with AI credits, members, repositories, and permission changes before changing budgets or access.

AWS Shows AgentCore Policy Workflows with Tenant Isolation and Versioned Skills

AWS’s August 7, 2026 machine-learning case study describes how Cohere Health uses Amazon Bedrock AgentCore to turn clinical prior-authorization policies into structured data. The architecture combines Runtime microVM isolation, Gateway for unified tool access, Memory for session history, and the Agent Skills open standard for versioned domain capabilities. Skills are evaluated with reference data and expert review before release. The reusable lesson is not to automate medical judgment with one prompt. It is to separate tenants, tools, data sources, versions, feedback, and approval, then begin with public or de-identified documents before connecting sensitive business data. Keep the same evidence trail when the workflow changes.

NVIDIA and Palantir turn supply-chain allocation expertise into a governed Nemotron training loop

NVIDIA published a supply-chain case study with Palantir Foundry on September 10. The workflow combines a governed Ontology, cuOpt optimization, planner decisions and rationales, point-in-time backtesting, and post-training of Nemotron 3.5 Lightning for material allocation recommendations. NVIDIA reports that its post-trained 30B model reached 86.7% allocation-decision accuracy on the development benchmark, compared with 55.5% for Nemotron 3 Ultra and 17.5% for the base Lightning model. The company also says a human planner reviews recommendations and makes the final call, while accepted, edited, and overridden outcomes feed future governed retraining. This is an official case study and development benchmark for a bounded allocation task. It does not establish broader general intelligence or general superiority for the 30B model beyond the specialized data, task, and evaluation design.

OpenAI introduces the Data agent for governed analysis and dashboards in ChatGPT Work

OpenAI introduced the Data agent for ChatGPT Work on September 10. The plugin connects approved company data sources, files, and business context so users can investigate changes in natural language, inspect evidence, and create interactive dashboards that teams can edit, share, and refresh. It can also share findings through connected Slack or email tools and carry out actions that users approve. Administrators control which connections and roles are available, while queries enforce the connected account's existing table, row, and column permissions. Data is listed in the ChatGPT Work Plugins directory; teams install it, complete required account connections, and start with @Data. Before operational use, teams should verify metric definitions, evidence, data scope, and each downstream action.

Salesforce unifies enterprise AI context, action, governance, security, and models in a new Harness

Salesforce introduced its Trusted Enterprise AI Harness on September 10, combining six capabilities for context, agency, action, governance, security, and model choice with an AI Control Plane for managing agents and AI across an enterprise. The company says customers can use the architecture with Salesforce technology, existing technology, or third-party models and systems, and describes headless access through MCP, APIs, Skills, and Plug-ins. Many technologies forming the foundation are available today. However, Salesforce explicitly says new capabilities and the unified experience are planned to begin rolling out in early fiscal FY28; pricing, packaging, regional availability, and upgrade paths will follow closer to general availability. Teams should treat this as an integration direction and validate current product availability before making architecture or purchasing commitments.

Introducing the Agents API