Your agent needs a runtime health check (or you'll never know when it breaks)

Your agent stops working at 2 AM. You find out at 9 AM when someone asks why their report wasn't ready. By then, you've lost 7 hours and your agent's reputation just took a hit.

The problem isn't that agents break — it's that they break silently. Unlike a crashed web server that throws 500 errors, a broken agent just... stops. No alerts. No logs. Just quiet failure.

Here's the runtime health check pattern that catches agent failures before they become problems:

The Core Pattern: Every 15 minutes, your agent runs a lightweight health check that tests its critical functions and reports status to a monitoring endpoint.

Start with this basic health check routine:

def agent_health_check():
    checks = {
        'api_connection': test_openai_connection(),
        'memory_access': test_memory_read_write(),
        'file_system': test_file_permissions(),
        'external_apis': test_critical_integrations(),
        'last_successful_task': check_recent_activity()
    }
    
    status = 'healthy' if all(checks.values()) else 'degraded'
    
    report_health(status, checks)
    return status

The key insight: test the things that actually matter. Don't just ping your agent — test the specific capabilities your workflows depend on.

Your health checks should cover:

• API connectivity — Can it reach OpenClaw, GitHub, Slack, etc?
• Memory system — Can it read and write to its knowledge store?
• File access — Can it read configs and write outputs?
• Recent activity — Has it completed a task in the last hour?
• Token budget — Is it approaching API limits?

But here's what most people get wrong: they make health checks too heavy. A health check that takes 30 seconds and burns 1000 tokens isn't sustainable. Keep checks lightweight — under 5 seconds, under 50 tokens.

For alerting, use a dead man's switch pattern. Your agent reports "I'm alive" every 15 minutes. If you don't hear from it for 30 minutes, something's wrong:

curl -X POST https://your-monitoring.com/heartbeat \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "coding-agent-1", "status": "healthy", "timestamp": "2024-01-15T10:30:00Z"}'

The real power comes from trending your health data. An agent that's "healthy" but taking 3x longer to complete tasks is showing early signs of degradation. Track response times, token usage, and success rates over time.

Warning: Don't health check during active tasks. Schedule checks during idle periods or you'll interrupt your agent mid-workflow.

I've been running this pattern for 6 months across 12 production agents. It's caught database connection failures, API key expirations, and memory corruption before any users noticed. The 10 minutes to set it up has saved me hours of debugging silent failures.

Your agent needs to be reliable before it needs to be smart. Runtime health checks are how you get there.