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Shell is a global energy company focused on oil and gas production.

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Copilot CLI GA brings agentic terminal workflows and CI/CD automation

GitHub Copilot CLI is now generally available with agentic Plan/Autopilot modes, stronger session and plugin controls, and first-class automation via GitHub Actions. Copilot CLI graduates from preview to a terminal-native agent that can plan, execute, and iterate within your shell, including interactive Plan mode and hands-off Autopilot, plus agent delegation and session memory as outlined in this GA overview from Visual Studio Magazine ([details](https://visualstudiomagazine.com/articles/2026/03/02/github-copilot-cli-reaches-general-availability-bringing-agentic-coding-to-the-terminal.aspx)). The broader Copilot ecosystem is also moving toward choice of agents, giving teams flexibility in model selection within the Copilot experience ([context](https://tessl.io/blog/github-brings-claude-and-codex-agents-directly-into-copilot/)). The latest release (v0.0.421) adds practical quality-of-life and governance features: a permission dialog that appears when it matters, repo-level config via .github/copilot/config.json, a --plugin-dir flag, COPILOT_CLI=1 detection for git hooks, reasoning-effort controls, and multiple Windows/Linux terminal fixes ([release notes](https://github.com/github/copilot-cli/releases/tag/v0.0.421), [all releases](https://github.com/github/copilot-cli/releases)). For CI/CD, you can run Copilot CLI in programmatic mode inside GitHub Actions to generate daily change summaries, scaffold content, or other scripted tasks using runner-installed CLI and a token with minimal scopes ([how-to](https://docs.github.com/en/copilot/how-tos/copilot-cli/automate-with-actions)).

calendar_today 2026-03-03

github-copilot-cli github github-actions visual-studio-code github-copilot

Custom Copilot agents, IDE arenas, and terminal control planes

AI agent tooling for developers is maturing with customizable Copilot skills, IDE-based model comparisons, and terminal-first control planes, while new research warns multi-agent setups often hurt results. GitHub now documents how to tailor the Copilot CLI and coding agent with project-specific instructions, hooks, and skills, enabling targeted automation for repo chores, build/test flows, and shell tasks directly from your terminal or VS Code Insiders agent mode ([customize Copilot CLI](https://docs.github.com/en/copilot/how-tos/copilot-cli/customize-copilot), [create agent skills](https://docs.github.com/copilot/how-tos/use-copilot-agents/coding-agent/create-skills)). In parallel, IDE workflows are adding native model evaluation and task skills: Windsurf’s terminal and test-generation capabilities are backed by docs and guides, and its recent “Arena Mode” for side-by-side model comparisons surfaced in industry coverage ([terminal guide](https://docs.windsurf.ai/features/terminal), [AI command assistance](https://docs.windsurf.ai/cascade/terminal), [test generation](https://docs.windsurf.ai/features/test-generation), [InfoQ LLMs page](https://www.infoq.com/llms/news/)). Agent orchestration is shifting to the command line as well: Cline CLI 2.0 positions the terminal as an AI agent control plane for multi-file refactors and scripted operations ([DevOps.com](https://devops.com/cline-cli-2-0-turns-your-terminal-into-an-ai-agent-control-plane/)). But a new Google Research study summarized by InfoQ reports that scaling to multiple cooperating agents does not reliably improve outcomes and can reduce performance, so start with single-agent flows and measure before adding complexity ([InfoQ LLMs page](https://www.infoq.com/llms/news/)). Early experiments like xAI’s Grok Build with parallel agents and arena-style evaluation point to where this is heading, but details remain in flux ([TestingCatalog](https://www.testingcatalog.com/xai-tests-parralel-agents-and-arena-mode-for-grok-build/)).

calendar_today 2026-02-17

github-copilot github-copilot-cli visual-studio-code-insiders windsurf cascade

Copilot CLI stabilizes for long sessions as IDEs move to agentic, team‑scoped AI

GitHub Copilot CLI’s latest update focuses on memory reductions and long‑session stability while IDE workflows and AI agents mature around team‑level customization and modernization tasks. GitHub Copilot CLI v0.0.410 ships broad stability improvements—fixing high memory usage under rapid logging, reducing streaming overhead, improving long‑session compaction, and adding ergonomic shell features like Ctrl+Z suspend/resume, Page Up/Down scrolling, repo‑level validation toggles, and an IDE status indicator when connected ([release notes](https://github.com/github/copilot-cli/releases)). The momentum aligns with a wider agentic shift: The New Stack frames VS Code as a “multi‑agent command center” for developers ([coverage](https://thenewstack.io/vs-code-becomes-multi-agent-command-center-for-developers/)), and Microsoft’s Copilot App Modernization details AI agents that assess, upgrade, containerize, and deploy .NET/Java apps to Azure in days ([deep dive](https://itnext.io/how-microsoft-is-using-ai-agents-to-turn-8-month-app-modernizations-into-days-a-technical-deep-8340a33513e7)). For IDE standardization, JetBrains/Android Studio Copilot customizations support workspace‑scoped settings committed under .github so teams can share constraints and conventions across projects ([guide](https://www.telefonica.com/en/communication-room/blog/github-copilot-android-studio-customization/)); also watch cost dynamics—one report shows OpenCode using far more credits than Copilot CLI for the same prompt, warranting usage instrumentation and policy checks ([user report](https://www.reddit.com/r/GithubCopilot/comments/1r2fhs2/opencode_vs_github_copilot_cli_huge_credit_usage/)).

calendar_today 2026-02-12

github-copilot-cli github visual-studio-code android-studio jetbrains

OpenAI Skills + Shell for long‑running agents: patterns and pitfalls

OpenAI’s new Skills and Shell tooling make it easier to ship capability‑scoped, long‑running agents for real backend work, but early adopters report reliability gaps you should engineer around. OpenAI’s cookbook shows how to turn discrete capabilities into reusable Skills that your agent invokes via tool calls, enabling least‑privilege execution and clearer observability ([Skills in API](https://developers.openai.com/cookbook/examples/skills_in_api/)); paired with the “tool‑call render” pattern, this turns a chatty bot into a doer with predictable handoffs ([render pattern explainer](https://dev.to/programmingcentral/the-tool-call-render-pattern-turning-your-ai-from-a-chatty-bot-into-a-doer-4cb2)). For workloads that run minutes to hours, OpenAI’s guidance combines Shell, Skills, and compaction to manage state bloat, retry long steps, and keep transcripts affordable and debuggable ([Shell + Skills + Compaction tips](https://developers.openai.com/blog/skills-shell-tips/)). Plan for rough edges reported by developers: an embedding outage returned all‑zero vectors in text‑embedding‑3‑small, some Assistants API file uploads expired immediately, GPT‑5.2 extended‑thinking had very low tokens/sec for some, and Apps SDK toolInvocation status UI required a widget workaround ([embedding outage](https://community.openai.com/t/embedding-model-outage-text-embedding-3-small-api-ev3-model-name-with-all-0-values/1374079#post_10), [files expiring](https://community.openai.com/t/files-instantly-expiring-upon-upload/1366339#post_5), [slow generation](https://community.openai.com/t/gpt-5-2-extended-thinking-webchat-has-unworkably-slow-token-4-tps-generation/1373185?page=3#post_49), [toolInvocation UI bug](https://community.openai.com/t/bug-meta-openai-toolinvocation-invoking-and-meta-openai-toolinvocation-invoked-not-shown-unless-the-tool-registers-a-widget/1374087#post_1)).

calendar_today 2026-02-12

openai chatgpt assistants-api agents-sdk chatgpt-apps-sdk

OpenAI Codex-Spark debuts on Cerebras for near-instant agentic coding

OpenAI launched GPT-5.3-Codex-Spark, a fast, steerable coding model served on Cerebras hardware to deliver near-instant responses for real-time agentic development. OpenAI and Cerebras unveiled a research preview of Codex-Spark aimed at live, iterative coding with responsiveness over 1,000 tokens/s, enabled by the Cerebras Wafer-Scale Engine, and designed to keep developers “in the loop” during agentic work [Cerebras announcement](https://www.cerebras.ai/blog/openai-codexspark). Independent coverage frames this as OpenAI’s first major inference move beyond Nvidia, positioning Cerebras for ultra-low-latency workloads while acknowledging capability tradeoffs versus the full GPT‑5.3‑Codex on autonomous engineering benchmarks [VentureBeat](https://venturebeat.com/technology/openai-deploys-cerebras-chips-for-15x-faster-code-generation-in-first-major) and broader speed-focused reporting [The New Stack](https://thenewstack.io/openais-new-codex-spark-is-optimized-for-speed/). On the tooling front, the openai/codex v0.99.0 release adds app‑server APIs for steering active turns, enterprise controls via requirements.toml (e.g., web search modes, network constraints), improved TUI flows, and concurrent shell command execution—useful for orchestrating agent runs with higher control and safety [GitHub release notes](https://github.com/openai/codex/releases/tag/rust-v0.99.0). For adoption patterns, a practical guide outlines “agent‑first engineering” using Codex CLI/IDE, cloud sandboxes for parallel tasks, an SDK for programmatic control, and GitHub Actions to plug agents into CI/CD with clear definitions of “done” [agentic workflow guide](https://www.gend.co/fr/blog/codex-agent-first-engineering).

calendar_today 2026-02-12

openai cerebras-systems nvidia gpt-53-codex-spark gpt-53-codex