GitHub Copilot CLI Introduces Rubber Duck for AI Agent Review

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6046Qhagj #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6040Qf3IA #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6045QfOqt #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6041Q4baP #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6046Q42io #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6047vElwN #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6047QA7Jt #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6041vDPAL #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6046Q4UYm #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

The next big step for AI agents might be self-critique, not just generation. 🦆 We are introducing Rubber Duck in experimental mode for GitHub Copilot CLI - a second model from a different AI family that reviews the agent’s plan and work at key moments. What stood out to me is that this is not positioned as “more AI for the sake of more AI”. It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. I also like the product thinking here. Rubber Duck is invoked sparingly, either automatically at the right checkpoints or on demand when the user asks Copilot to critique its own work, which keeps the workflow focused instead of noisy. For anyone building with AI agents, this is a useful reminder that better outcomes may come not just from a stronger model, but from a better system design around review and correction. If you want to try it, run the /experimental command in Copilot CLI (another great reason for you to have a closer look at the terminal-first software development!), and it works when a Claude family model is selected as the orchestrator and access to GPT-5.4 is enabled. More details: https://msft.it/6046Q4ZcE #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate