Debugging Workflows

Debugging GitHub Agentic Workflows

Use this guide to debug GitHub Agentic Workflows: download and analyze logs, audit runs, and trace workflow behavior.

Table of Contents

• Quick Start
• Downloading Workflow Logs
• Auditing Specific Runs
• How Agentic Workflows Work
• Common Issues and Solutions
• Advanced Debugging Techniques
• Reference Commands

Quick Start

Download Logs from Recent Runs

# Download logs from the last 24 hours
gh aw logs --start-date -1d -o /tmp/workflow-logs

# Download logs for a specific workflow
gh aw logs weekly-research --start-date -1d

# Download logs with JSON output for programmatic analysis
gh aw logs --json

Audit a Specific Run

# Audit by run ID
gh aw audit 1234567890

# Audit from a GitHub Actions URL
gh aw audit https://github.com/owner/repo/actions/runs/1234567890

# Audit with JSON output
gh aw audit 1234567890 --json

Downloading Workflow Logs

The gh aw logs command downloads workflow run artifacts and logs from GitHub Actions for analysis.

Basic Usage

# Download logs for all workflows (last 10 runs)
gh aw logs

# Download logs for a specific workflow
gh aw logs <workflow-name>

# Download with custom output directory
gh aw logs -o ./my-logs

Filter Options

# Filter by date range
gh aw logs --start-date 2024-01-01 --end-date 2024-01-31
gh aw logs --start-date -1w                    # Last week
gh aw logs --start-date -1mo                   # Last month

# Filter by AI engine
gh aw logs --engine copilot
gh aw logs --engine claude
gh aw logs --engine codex

# Filter by count
gh aw logs -c 5                                # Last 5 runs

# Filter by branch/tag
gh aw logs --ref main
gh aw logs --ref feature-xyz

# Filter by run ID range
gh aw logs --after-run-id 1000 --before-run-id 2000

# Filter firewall-enabled runs
gh aw logs --firewall                          # Only firewall-enabled
gh aw logs --no-firewall                       # Only non-firewall

Output Options

# Generate JSON summary
gh aw logs --json

# Parse agent logs and generate Markdown reports
gh aw logs --parse

# Generate Mermaid tool sequence graph
gh aw logs --tool-graph

# Set download timeout
gh aw logs --timeout 300                       # 5 minute timeout

Downloaded Artifacts

When you run gh aw logs, the following artifacts are downloaded for each run:

Example: Analyze Recent Failures

# Download failed runs from last week
gh aw logs --start-date -1w -o /tmp/debug-logs

# Check the summary for patterns
cat /tmp/debug-logs/summary.json | jq '.runs[] | select(.conclusion == "failure")'

Auditing Specific Runs

The gh aw audit command investigates a single workflow run in detail, downloading artifacts, detecting errors, and generating a report.

Basic Usage

# Audit by numeric run ID
gh aw audit 1234567890

# Audit from GitHub Actions URL
gh aw audit https://github.com/owner/repo/actions/runs/1234567890

# Audit from job URL (extracts first failing step)
gh aw audit https://github.com/owner/repo/actions/runs/1234567890/job/9876543210

# Audit from job URL with specific step
gh aw audit https://github.com/owner/repo/actions/runs/1234567890/job/9876543210#step:7:1

Output Options

# JSON output for programmatic analysis
gh aw audit 1234567890 --json

# Custom output directory
gh aw audit 1234567890 -o ./audit-reports

# Parse agent logs and firewall logs
gh aw audit 1234567890 --parse

# Verbose output
gh aw audit 1234567890 -v

Audit Report Contents

The audit command provides:

• Error Detection: Errors and warnings from workflow logs
• MCP Tool Usage: Statistics on tool calls by the AI agent
• Missing Tools: Tools the agent tried to use but weren't available
• Execution Metrics: Duration, token usage, and cost information
• Safe Output Analysis: What GitHub operations were attempted

Example: Investigate a Failed Run

# Get detailed audit report
gh aw audit 1234567890 --json > audit.json

# Extract key information
cat audit.json | jq '{
  status: .status,
  conclusion: .conclusion,
  errors: .errors,
  missing_tools: .missing_tools,
  tool_usage: .tool_usage
}'

How Agentic Workflows Work

Understanding the workflow architecture helps in debugging.

Workflow Structure

Agentic workflows use a markdown + YAML frontmatter format:

---
on:
  issues:
    types: [opened]
permissions:
  issues: write
timeout-minutes: 10
engine: copilot
tools:
  github:
    mode: remote
    toolsets: [default]
safe-outputs:
  create-issue:
    labels: [ai-generated]
---

# Workflow Title

Natural language instructions for the AI agent.

Use GitHub context like ${{ github.event.issue.number }}.

Execution Flow

1. Trigger Event (issue opened, PR created, schedule, etc.)
     ↓
2. Activation Job
   - Validates permissions
   - Processes mcp-scripts
   - Sanitizes context
     ↓
3. AI Agent Job
   - Loads MCP servers and tools
   - Executes AI agent with prompt
   - Agent makes tool calls
   - Agent produces output
     ↓
4. Safe Outputs Job
   - Processes agent output
   - Creates GitHub resources (issues, PRs, etc.)
   - Applies labels, comments
     ↓
5. Completion
   - Workflow summary generated
   - Artifacts uploaded

Key Components

Compilation Process

# Compile workflow to GitHub Actions YAML
gh aw compile <workflow-name>

# Result: .github/workflows/<name>.md → .github/workflows/<name>.lock.yml

The .lock.yml file is the actual GitHub Actions workflow that runs.

Common Issues and Solutions

Missing Tool Errors

Symptoms:

• Error: "Tool 'github:read_issue' not found"
• Agent cannot access GitHub APIs

Solution: Add GitHub MCP server configuration:

tools:
  github:
    mode: remote
    toolsets: [default]

Permission Errors

Symptoms:

• HTTP 403 (Forbidden) errors
• "Resource not accessible" errors

Solution: Add required permissions:

permissions:
  contents: read
  issues: write
  pull-requests: write

Safe-Input Errors

Symptoms:

• "missing tool configuration for mcpscripts-gh"
• Environment variable not available

Solution: Configure mcp-scripts:

mcp-scripts:
  issue:
    script: |
      return { title: process.env.ISSUE_TITLE, body: process.env.ISSUE_BODY };
    env:
      ISSUE_TITLE: ${{ github.event.issue.title }}
      ISSUE_BODY: ${{ github.event.issue.body }}

Safe-Output Errors

Symptoms:

• Agent tries to create resources but fails
• "Safe output not enabled" errors

Solution: Enable safe-outputs:

safe-outputs:
  staged: false  # Set to false to actually create resources
  create-issue:
    labels: [ai-generated]

Cascading Safe-Output Message Failures (Process Safe Outputs step)

Symptoms:

• Process Safe Outputs reports multiple failed messages in one run
• One failed update_pull_request message includes a 403 workflows-permission warning
• Other failed messages (for example add_comment) include Bad credentials

What this means:

• Do not assume all safe-output failures share one root cause.
• A 403 workflows-permission error on update_pull_request can be expected/non-fatal in some workflows.
• A 401-style Bad credentials error on other messages is a separate authentication failure that needs its own fix.

Diagnostic steps:

# Summarize failed safe-output messages and types
gh aw audit <run-id>

# Include additional artifacts when diagnosis needs more context
gh aw audit <run-id> --artifacts usage,github-api,mcp,agent

# Escalate to full artifact collection for hard-to-classify failures
gh aw audit <run-id> --artifacts all

# Inspect full failing job logs to classify each message failure
gh run view <run-id> --job=<job-id> --log

• Triage each failed message by its own HTTP status code and tool/action name.
• Check permissions: for missing scopes when 403 errors appear.
• Compare the "failed message count" against the individual failed message lines to confirm whether there are multiple independent failures.
• If several credential failures cluster together in time, investigate token freshness/expiry and token source for the run.

Network Access Errors

Symptoms:

• Firewall denials
• URLs appearing as "(redacted)"

Solution: Configure network access:

network:
  allowed:
    - defaults
    - python    # For PyPI
    - node      # For npm
    - "api.example.com"  # Custom domains

Timeout Errors

Symptoms:

• Workflow exceeds time limit
• Agent loops or hangs

Solution: Increase timeout or optimize prompt:

timeout-minutes: 30  # Increase from default

Advanced Debugging Techniques

Polling In-Progress Runs

When a run is still executing:

# Poll until completion
while true; do
  output=$(gh aw audit <run-id> --json 2>&1)
  if echo "$output" | grep -q '"status":.*"\(completed\|failure\|cancelled\)"'; then
    echo "$output"
    break
  fi
  echo "⏳ Run still in progress. Waiting 45 seconds..."
  sleep 45
done

Inspecting MCP Configuration

# Inspect MCP servers for a workflow
gh aw mcp inspect <workflow-name>

# List all workflows with MCP servers
gh aw mcp list

Checking Workflow Status

# Show status of all agentic workflows
gh aw status

Downloading Specific Artifacts

# Download only the agent log artifact
GH_REPO=owner/repo gh run download <run-id> -n agent-stdio.log

Inspecting Job Logs

# View specific job logs
gh run view <run-id>
gh run view --job <job-id> --log

Analyzing Firewall Logs

# Parse firewall logs for network issues
gh aw logs --parse

# Check firewall-enabled runs
gh aw logs --firewall

Debug Mode Compilation

# Compile with verbose output
gh aw compile --verbose

# Compile with strict security checks
gh aw compile --strict

# Run security scanners
gh aw compile --actionlint --zizmor --poutine

Reference Commands

Log Analysis Commands

Audit Commands

MCP Commands

Status Commands

Workflow Execution Commands

Additional Resources

• Workflow Health Monitoring Runbook - Step-by-step investigation procedures
• Common Issues Reference - Frequently encountered issues
• Error Reference - Error codes and solutions
• GitHub MCP Server Documentation - Tool configuration reference