DeterministicOps

GitHub Agentic Workflows can combine deterministic computation (steps: and jobs:) with AI reasoning, enabling hybrid agentic data preprocessing. This pattern can reliably collect and prepare data, then the AI agent reads the results and generates insights. Use this for data aggregation, report generation, trend analysis, auditing, and any hybrid pipeline.

When to Use

Combine deterministic steps with AI agents to precompute data, filter triggers, preprocess inputs, post-process outputs, or build multi-stage computation and reasoning pipelines.

Example: Release Highlights Generator

This workflow generates release highlights for new tags. It uses deterministic steps to fetch structured data about the release and recent PRs, then the AI agent synthesizes this into a release summary.

When using steps: or jobs:, files placed in /tmp/gh-aw/agent/ are automatically uploaded as artifacts and available to the AI agent.

flowchart TD
    det[Deterministic steps] -- artifacts --> agent[AI agent]
    agent -- safe-outputs --> so[Safe output jobs]

Example workflow:

---
on:
  push:
    tags: ['v*.*.*']

safe-outputs:
  update-release:

steps:
  - run: |
      gh release view "${GITHUB_REF#refs/tags/}" --json name,tagName,body > /tmp/gh-aw/agent/release.json
      gh pr list --state merged --limit 100 --json number,title,labels > /tmp/gh-aw/agent/prs.json
    env:
      GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
---

# Release Highlights Generator

Generate release highlights for `${GITHUB_REF#refs/tags/}`. Analyze PRs in `/tmp/gh-aw/agent/prs.json`, categorize changes, and use update-release to prepend highlights to the release notes.

Data Caching

For workflows that run frequently or process large datasets, use GitHub Actions caching to avoid redundant API calls:

---
cache:
  - key: pr-data-${{ github.run_id }}
    path: /tmp/gh-aw/pr-data
    restore-keys: |
      pr-data-

steps:
  - name: Check cache and fetch only new data
    run: |
      if [ -f /tmp/gh-aw/pr-data/recent-prs.json ]; then
        echo "Using cached data"
      else
        gh pr list --limit 100 --json ... > /tmp/gh-aw/pr-data/recent-prs.json
      fi
---

Deterministic Trigger Filtering

Deterministic steps can also be used for Custom Trigger Filtering, to control whether the agentic workflow should run based on complex conditions that are easier to express in code than in workflow expressions.

Deterministic Post-Processing

Custom Safe Outputs can also be used for deterministic post-processing of AI outputs.

---
on:
  pull_request:
    types: [opened]

safe-outputs:
  jobs:
    format-and-notify:
      description: "Format and post review"
      runs-on: ubuntu-latest
      inputs:
        summary: {required: true, type: string}
      steps:
        - ...
---

# Code Review Agent

Review the pull request and use format-and-notify to post your summary.

Pre-Activation Steps — Inline step injection into the pre-activation job
Pre-Activation Permissions — Grant additional scopes for on.steps: API calls
Custom Safe Outputs — Custom post-processing jobs
Frontmatter Reference — Configuration options
Compilation Process — How jobs are orchestrated
Imports — Sharing configurations across workflows
Templating — Using GitHub Actions expressions