Error Handling in Agents Checklist: 10 Things Before Going to Production
I’ve seen 3 production agent deployments fail this month. All 3 made the same 5 mistakes. To avoid being the next statistic, here’s an error handling in agents checklist that you should follow before your deployment.
1. Implement thorough Logging
Why it matters: Good logging allows you to trace problems back to their source. If you can’t see what went wrong, good luck fixing it.
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def sample_function():
try:
# your main code here
pass
except Exception as e:
logger.error("An error occurred: %s", e)
What happens if you skip it: If logging isn’t in place, expect a lack of insight into your agent’s failures. You’ll be left guessing, leading to extended downtime and a frustrated development team.
2. Exception Handling
Why it matters: Catching and handling exceptions gracefully is crucial for any production environment. You need to define what happens when things don’t go as planned.
try:
risky_operation()
except SpecificException as e:
handle_error(e)
except Exception:
handle_general_error()
What happens if you skip it: Skipping exception handling can result in uncaught errors that crash your agents. Imagine having an agent hung and waiting because of a simple division by zero. That’s a nightmare.
3. Circuit Breaker Pattern
Why it matters: When dealing with external services, a circuit breaker can prevent your application from making repeated requests to a failing service. It avoids wasting resources and time.
class CircuitBreaker:
def __init__(self, fail_threshold):
self.fail_threshold = fail_threshold
self.failure_count = 0
def call(self):
if self.failure_count >= self.fail_threshold:
raise Exception("Service is down")
# Normal operation here
What happens if you skip it: Your system could overheat from repeated failures to external services, leading to a cascading failure. Trust me, that’s a recipe for disaster!
4. Retry Logic
Why it matters: Sometimes requests fail due to temporary issues. A retry mechanism gives your system room to breathe and often turns failures into successes.
import time
def retry_request(func, max_retries=5):
for i in range(max_retries):
try:
return func()
except Exception:
time.sleep(2 ** i) # Exponential backoff
What happens if you skip it: Your agents could give up on requests too quickly. The last thing you want is your agent dropping a request when a simple retry would have worked.
5. Graceful Degradation
Why it matters: Your system shouldn’t crash when something fails. Graceful degradation means offering a fallback mechanism or reduced functionality instead of a complete failure.
def perform_action():
try:
# Primary action
pass
except Exception:
# Fallback action
return "Fallback response"
What happens if you skip it: If you don’t have graceful degradation, your users might be met with errors instead of a meaningful fallback. That’s about as user-friendly as a brick wall.
6. User Notifications
Why it matters: If something goes wrong, your users should be informed promptly. This transparency builds trust and allows users to plan accordingly.
def notify_user(error_message):
send_email("Error Notification", error_message)
What happens if you skip it: Ignoring this means users might be left in the dark about issues affecting their experience. This can lead to escalated support tickets and unhappy users.
7. Monitoring and Alerts
Why it matters: Monitoring ensures you’re aware of an issue before it affects many users. Setting up alerts can help you jump on problems immediately.
# Using a basic cron job for monitoring
* * * * * /path/to/monitor_script.sh > /dev/null 2>&1
What happens if you skip it: You risk being unaware of major failures until user complaints pour in. Imagine your system failing, and you’re stuck waiting for complaints instead of being proactive.
8. Testing and Validation
Why it matters: Rigorous testing plays a big part in error prevention. Running tests should become part of your development and deployment process.
pytest test_sample.pyWhat happens if you skip it: Overlooking this leads to releasing error-riddled code. No one likes dealing with last-minute surprises in production. Trust me, I’ve been there.
9. Rate Limiting
Why it matters: Preventing overload from user requests is essential. Rate limiting helps you maintain uptime while managing load effectively.
from flask_limiter import Limiter
limiter = Limiter(app, key_func=get_remote_address)
@limiter.limit("100 per minute")
@app.route("/api")
def api():
return "Hello, world!"What happens if you skip it: Your service could collapse under heavy traffic. I’ve seen a site go down in flames simply because they couldn’t handle users flooding in at once.
10. Documentation
Why it matters: Always document your error handling processes, code, and configuration. It creates a knowledge base for current and future developers.
# Sample README.md
## Error Handling
- Overview of strategy and patterns
- How to add new handlers
- Functions overview
What happens if you skip it: New team members will waste time figuring out how things work. And trust me, being the veteran explaining it for the 100th time gets tiring.
Priority Order
- Must Do Today: 1. Implement thorough Logging, 2. Exception Handling, 3. Circuit Breaker Pattern, 4. Retry Logic
- Nice to Have: 5. Graceful Degradation, 6. User Notifications, 7. Monitoring and Alerts, 8. Testing and Validation, 9. Rate Limiting, 10. Documentation
Tools Table
| Tool | Type | Features | Free Option |
|---|---|---|---|
| Sentry | Error Tracking | Logging, Monitoring, Alerts | Yes |
| Prometheus | Monitoring | Metrics Collection | Yes |
| New Relic | Application Performance Monitoring | Monitoring, Error Tracking | No |
| PagerDuty | Incident Management | Alerts, On-call Management | No |
| Flask-Limiter | Rate Limiting | API Rate Limiting | Yes |
The One Thing
If you only do one thing from this list, set up thorough logging. It’ll provide the insights you need when problems arise, making it easier to troubleshoot issues and prevent them from recurring.
FAQ
What is thorough logging?
thorough logging includes capturing detailed logs that track errors, warnings, and important application events, helping developers understand what the application is doing and where it might fail.
Why is exception handling crucial?
Exception handling ensures that your application can respond to errors gracefully, reducing the impact of those errors on the end-user experience.
What tools can help with error monitoring?
Tools like Sentry, New Relic, and Prometheus are popular choices for tracking errors, monitoring application performance, and sending alerts.
How can I implement retry logic?
Retry logic can be implemented using loops and backoff strategies in your existing functions to handle failures gracefully without overloading system resources.
What if my agent is hitting rate limits?
If rate limits are being hit, consider implementing proper rate-limiting strategies to manage traffic, or optimize the agent’s requests.
Data Sources
Last updated March 25, 2026. Data sourced from official docs and community benchmarks.
🕒 Last updated: · Originally published: March 25, 2026