devJune 9, 2026·GitHub Blog

From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI

Custom agents let GitHub Copilot CLI understand your stack and team workflows, turning one-off terminal prompts into repeatable, reviewable processes. The post From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI appeared first on The GitHub Blog.

Key Takeaways

•Custom agents let GitHub Copilot CLI understand your stack and team workflows, turning one-off terminal prompts into repeatable, reviewable processes. The post From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI appeared first on The GitHub Blog.
•This story was reported by GitHub Blog, covering developments in the dev space.
•AI advancements continue to reshape industries — read the full article on GitHub Blog for complete coverage.

📖 Continue reading the full article:

Read Full Article on GitHub Blog →

Share this article

X Facebook Reddit ☕ Support

Dev.to

Give Your Scheduling Bot Its Own Calendar

A scheduling link makes the human do the work; a scheduling agent with its own calendar does the negotiating. Booking pages outsource the back-and-forth to a UI. The agent model keeps it where it already happens — in email — and answers from a real address with a real calendar behind it. The setup:

Dev.to

Give Your AI Agent Its Own Email Address (Not Access to Yours)

Most "AI agent + email" tutorials start the same way: connect the agent to a human's inbox over OAuth, hope the token doesn't expire mid-run, and pray the agent never replies to the wrong thread on someone's behalf. There's a different model: give the agent its own email address. Nylas recently ship

Dev.to

Build Your Own AI Medical Assistant: Automating Health Report Analysis with AutoGPT & OpenAI

Ever stared at a physical examination report and felt like you were reading ancient hieroglyphics? "Elevated Serum Triglycerides"? "Hypoechoic nodule"? The immediate urge is to Google it, only to be convinced by WebMD that you have three days to live. In the world of AI Agents and Healthcare Automa

GitHub Blog

Making secret scanning more trustworthy: Reducing false positives at scale

Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning. The post Making secret scanning more trustworthy: Reducing false positives at scale appeared first on The GitHub Blog.

Discussion

Loading articles...

From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI

Key Takeaways

Related Articles

Give Your Scheduling Bot Its Own Calendar

Give Your AI Agent Its Own Email Address (Not Access to Yours)

Build Your Own AI Medical Assistant: Automating Health Report Analysis with AutoGPT & OpenAI

Making secret scanning more trustworthy: Reducing false positives at scale

Discussion

From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI

Key Takeaways

Related Articles

Give Your Scheduling Bot Its Own Calendar

Give Your AI Agent Its Own Email Address (Not Access to Yours)

Build Your Own AI Medical Assistant: Automating Health Report Analysis with AutoGPT & OpenAI

Making secret scanning more trustworthy: Reducing false positives at scale

Discussion

Related Articles

Dev.to
Give Your Scheduling Bot Its Own Calendar
A scheduling link makes the human do the work; a scheduling agent with its own calendar does the negotiating. Booking pages outsource the back-and-forth to a UI. The agent model keeps it where it already happens — in email — and answers from a real address with a real calendar behind it. The setup:

Dev.to
Give Your AI Agent Its Own Email Address (Not Access to Yours)
Most "AI agent + email" tutorials start the same way: connect the agent to a human's inbox over OAuth, hope the token doesn't expire mid-run, and pray the agent never replies to the wrong thread on someone's behalf. There's a different model: give the agent its own email address. Nylas recently ship

Dev.to
Build Your Own AI Medical Assistant: Automating Health Report Analysis with AutoGPT & OpenAI
Ever stared at a physical examination report and felt like you were reading ancient hieroglyphics? "Elevated Serum Triglycerides"? "Hypoechoic nodule"? The immediate urge is to Google it, only to be convinced by WebMD that you have three days to live. In the world of AI Agents and Healthcare Automa

GitHub Blog
Making secret scanning more trustworthy: Reducing false positives at scale
Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning. The post Making secret scanning more trustworthy: Reducing false positives at scale appeared first on The GitHub Blog.