What's Next? Keep Exploring
August 9, 2026 · View on GitHub
You've built a real, scheduled AI workflow — here's how to keep growing from here.
:dart: What You'll Do
Take stock of everything you've learned, then choose a direction for what to build or explore next. This node is a hub: it links to deeper dives, community resources, and ideas for your own projects.
:clipboard: Before You Start
- You have a scheduled daily-status workflow running in GitHub Actions from Refine, Test, and Improve Your Workflow.
Steps
Celebrate what you've shipped
You've gone from zero to a fully automated, AI-powered workflow that:
- Runs on a schedule in GitHub Actions
- Uses gh-aw to call an AI model from a simple YAML file
- Posts a daily summary without any manual intervention
That is a real, production-capable workflow. Nicely done.
Reflect and Plan
Answer each question (in your notes or a new GitHub issue in your practice repository), then check the box:
- What was the hardest part of this workshop, and why?
- How would you change your daily-status workflow prompt to get better output?
- What is the next workflow you want to build, and what data source would it need?
Review what you've learned
Here's a quick recap of the concepts you've touched. The diagram below shows how all the pieces connect in the workflow you just built.
| Concept | Where you used it |
|---|---|
| GitHub Actions triggers | on: schedule and workflow_dispatch |
| gh-aw workflow syntax | Every .md workflow file you wrote |
| AI model calls | The Markdown body (agent instructions) of your daily-status workflow |
| Natural-language schedules | schedule: daily on weekdays |
| Iterative debugging | Running, reading output, tweaking, repeating |
Go deeper
- :arrow_right: Make Your Workflow Smarter with Conditional Logic — add conditions so your workflow only runs when there is meaningful activity to report.
- :arrow_right: Connect a Live Data Source to Your Workflow — fetch live repository data and pass it into your AI prompt as workflow context.
- :arrow_right: Give Your Agent More Tools with MCP — connect the GitHub MCP server so your agent can read live repository data as it runs.
- :arrow_right: Share and Reuse Your Agentic Workflows — publish your workflow to a catalog so others can install it with one command.
- :arrow_right: Make Your Workflow Remember Across Runs — add cache-backed memory so your workflow skips items it has already reported on.
- :arrow_right: Split Complex Workflows with Inline Sub-Agents — use the planner-worker pattern to keep your main prompt lean and reduce token cost.
- :arrow_right: Make Your Workflows Resilient to Failure — add defensive briefs, timeouts, and fallback outputs so unattended runs stay reliable.
- :arrow_right: Test Your Prompt Ideas with A/B Experiments — compare prompt variants across runs and let data decide which one to keep.
- :arrow_right: Run Your Agentic Workflow on a Self-Hosted Runner — target your organisation's runner fleet instead of GitHub-hosted machines (enterprise teams).
- :arrow_right: Audit and Monitor Your Agentic Workflows — read run artifacts, understand token usage, and build an audit trail for enterprise compliance.
- :arrow_right: Manage Costs and AI Credit Budgets — measure AIC consumption, set spending limits, and keep your workflows within budget (enterprise teams).
✅ Checkpoint
- Your scheduled workflow has completed at least one successful automated run
- You can describe, in plain English, what agentic workflows are and why they're useful
- You have at least one idea for the next workflow you want to build
- You drafted a two-sentence brief for your next agentic workflow
- You know where to find the gh-aw docs when you need them
You've reached the end of the scheduled-workflow path — but there is one more step on the main track before you head into advanced territory. Come back to explore any of the deeper topics when you're ready.
Next: Build Your First Event-Driven Workflow: PR Auto-Reviewer