Deploy Agent

Deploy Agent

Quick Reference — see parent for full agent ecosystem.

The Deploy Agent ships artifacts to staging and production through a controlled pipeline with health checks, migration execution, automated rollbacks, and post-deploy monitoring. Its core design principle is reversibility: every deploy must have a tested rollback path before it begins.

When Not to Use

• Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
• Already established workflows — follow existing team conventions rather than introducing new frameworks.
• When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.

Dependencies

• Python 3.8+ or Node.js 18+
• Access to relevant APIs/services for your specific use case
• Basic understanding of the domain concepts

Commands

# Refer to the skill's usage section for specific commands
# Adapt these to your workflow

Key Responsibilities

• Execute deployments with strategy: Support blue-green, rolling, canary, and hotfix strategies with zero-downtime guarantees
• Run database migrations: Apply schema changes in the correct order with dry-run validation and automated rollback scripts
• Verify post-deploy health: Run health checks, smoke tests, and monitor error rates for a configurable observation window

Code Example

"""Minimal deploy agent pattern — ship with verification."""

import json, subprocess, sys
from pathlib import Path

def deploy(target: str, tag: str, strategy: str = "rolling") -> dict:
    # 1. Pre-deploy checks
    assert subprocess.run(["git", "diff", "--quiet"], cwd=".").returncode == 0, "Dirty working tree"
    assert subprocess.run([sys.executable, "-m", "pytest", "-x", "-q"]).returncode == 0

    # 2. Build artifact
    build = subprocess.run(["docker", "build", "-t", f"app:{tag}", "."], capture_output=True, text=True)
    if build.returncode != 0:
        return {"status": "failed", "error": build.stderr}

    # 3. Run migrations (dry-run first)
    dry = subprocess.run([sys.executable, "-m", "alembic", "upgrade", "--sql", "head"], capture_output=True, text=True)
    print(f"Migration SQL:\n{dry.stdout}")

    # 4. Deploy
    push = subprocess.run(["docker", "push", f"app:{tag}"])
    if target == "production":
        subprocess.run(["kubectl", "set", "image", f"deployment/app=app:{tag}"])
        subprocess.run(["kubectl", "rollout", "status", "deployment/app"])

    return {
        "target": target, "tag": tag, "strategy": strategy,
        "migration_applied": True, "rollback": f"kubectl rollout undo deployment/app"
    }

if __name__ == "__main__":
    result = deploy(sys.argv[1], sys.argv[2], sys.argv[3] if len(sys.argv) > 3 else "rolling")
    print(json.dumps(result, indent=2))

Checklist

• Rollback plan documented and tested before deploy begins
• Database migrations validated: dry-run + reversible (include downgrade)
• Health check endpoint responds 200 after deploy completes
• Smoke test suite passes against the deployed target
• Post-deploy monitoring configured with alert threshold (error rate, latency P95)

Workflow

1. Identify the task or trigger.
2. Prepare inputs and configure parameters.
3. Execute the core routine.
4. Verify the output against expected results.
5. Iterate based on feedback or new data.

Anti-Rationalization Table

When to Use

Use when shipping code to staging or production, running database migrations, performing rollbacks, deploying hotfixes, or setting up CI/CD pipelines. Do NOT use for experimental features needing manual verification first, changes requiring coordinated multi-service releases without a release train, or when the target environment is unreachable by the agent.