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Monitoring and Reliability: Keeping Your AI Agent Running Smoothly
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Monitoring and Reliability: Keeping Your AI Agent Running Smoothly

Michael Brenndoerfer•November 10, 2025•15 min read•2,718 words•Interactive
AI Agent HandbookSoftware EngineeringMachine Learning

Learn how to monitor your deployed AI agent's health, handle errors gracefully, and build reliability through health checks, metrics tracking, error handling, and scaling strategies.

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Part of AI Agent Handbook

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Monitoring and Reliability

Your agent is deployed. Users can access it. Requests are flowing in. Everything seems to be working. But how do you know it's actually working well? And what happens when something goes wrong?

This is where monitoring and reliability come in. Deployment gets your agent running, but monitoring keeps it running well. Think of it like the difference between launching a ship and keeping it seaworthy. You need instruments to tell you how the ship is performing, and you need systems to handle problems when they arise.

In this chapter, we'll explore how to monitor your agent's health, handle errors gracefully, and build reliability into your system so users can depend on it. We'll build on the logging techniques from Chapter 12 and the deployment patterns from Chapter 14.1 to create an agent that doesn't just work today, but keeps working tomorrow and next week.

Why Monitoring Matters

When your agent runs on your development machine and you're the only user, problems are obvious. The terminal shows errors immediately. You notice if responses are slow. You can restart the agent with a single command.

But once your agent is deployed, especially if others are using it, you lose that immediate visibility. Users might be getting errors while you're asleep. The agent might be running slowly, frustrating people without you knowing. A tool might be failing intermittently, causing some requests to work and others to fail mysteriously.

Monitoring solves this by giving you visibility into what's happening with your deployed agent. It answers questions like:

  • Is the agent responding to requests?
  • How long do responses take?
  • Are errors occurring?
  • Is the agent using resources efficiently?
  • Are users experiencing problems?

Without monitoring, you're flying blind. With it, you can spot problems early, understand patterns, and keep your agent healthy.

Health Checks: Is the Agent Alive?

The most basic form of monitoring is a health check. This is a simple endpoint that tells you whether your agent is running and able to respond. Think of it like checking someone's pulse. You're not diagnosing complex issues, just verifying the system is alive.

Let's add a health check to our agent's API:

1## Example (Claude Sonnet 4.5)
2## Using Claude Sonnet 4.5 for its superior agent capabilities
3from flask import Flask, jsonify
4import time
5
6app = Flask(__name__)
7start_time = time.time()
8
9@app.route('/health', methods=['GET'])
10def health_check():
11    """Simple health check endpoint"""
12    uptime = time.time() - start_time
13    return jsonify({
14        "status": "healthy",
15        "uptime_seconds": uptime,
16        "timestamp": time.time()
17    })
18
19@app.route('/chat', methods=['POST'])
20def chat():
21    # Your agent logic here
22    pass

This health check returns a simple response confirming the agent is running and how long it's been up. You can call this endpoint periodically to verify the agent is responsive:

1curl http://your-agent-url.com/health

If you get a response, the agent is alive. If the request times out or fails, something is wrong.

Most deployment platforms can use health checks automatically. You configure them to ping your health endpoint every minute or so. If the health check fails multiple times in a row, the platform can alert you or automatically restart your agent.

Deeper Health Checks

A basic health check tells you the web server is responding, but your agent depends on more than just the web framework. It needs to call the language model API, access any databases or memory stores, and use external tools. A more thorough health check verifies these dependencies too.

Here's an enhanced health check that tests key components:

1@app.route('/health', methods=['GET'])
2def health_check():
3    """Comprehensive health check"""
4    health_status = {
5        "status": "healthy",
6        "timestamp": time.time(),
7        "checks": {}
8    }
9    
10    # Check if we can reach the LLM API
11    try:
12        client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
13        # Make a minimal API call to verify connectivity
14        response = client.messages.create(
15            model="claude-sonnet-4.5",
16            max_tokens=10,
17            messages=[{"role": "user", "content": "test"}]
18        )
19        health_status["checks"]["llm_api"] = "ok"
20    except Exception as e:
21        health_status["checks"]["llm_api"] = f"error: {str(e)}"
22        health_status["status"] = "degraded"
23    
24    # Check database connectivity
25    try:
26        # Test database connection
27        db.execute("SELECT 1")
28        health_status["checks"]["database"] = "ok"
29    except Exception as e:
30        health_status["checks"]["database"] = f"error: {str(e)}"
31        health_status["status"] = "degraded"
32    
33    return jsonify(health_status)

This health check tests the critical dependencies. If the LLM API is unreachable or the database is down, the health check reports "degraded" status. You know the agent is running, but it can't function properly.

Be careful with comprehensive health checks, though. You don't want the health check itself to be expensive or slow. Keep the checks lightweight. A minimal API call to verify connectivity is fine, but don't run a full agent query as part of the health check.

Tracking Key Metrics

Health checks tell you if the agent is alive, but they don't tell you how well it's performing. For that, you need metrics. Metrics are numerical measurements that track your agent's behavior over time.

Here are the most useful metrics to track:

Request Rate: How many requests is your agent handling per minute or per hour? This tells you how much the agent is being used.

Response Time: How long does it take the agent to respond to a request? Users notice if responses are slow, so tracking this helps you maintain good performance.

Error Rate: What percentage of requests result in errors? A sudden spike in errors indicates something is wrong.

Tool Usage: How often is each tool being called? This helps you understand what your agent is doing and can reveal if a tool is being overused or underused.

Let's add basic metrics tracking to our agent:

1import time
2from collections import defaultdict
3
4## Simple in-memory metrics storage
5metrics = {
6    "request_count": 0,
7    "error_count": 0,
8    "response_times": [],
9    "tool_usage": defaultdict(int)
10}
11
12@app.route('/chat', methods=['POST'])
13def chat():
14    start_time = time.time()
15    metrics["request_count"] += 1
16    
17    try:
18        user_message = request.json.get('message')
19        
20        # Process the message with your agent
21        response = process_agent_request(user_message)
22        
23        # Track response time
24        response_time = time.time() - start_time
25        metrics["response_times"].append(response_time)
26        
27        return jsonify({"response": response})
28        
29    except Exception as e:
30        metrics["error_count"] += 1
31        logger.error(f"Request failed: {e}")
32        return jsonify({"error": "Something went wrong"}), 500
33
34@app.route('/metrics', methods=['GET'])
35def get_metrics():
36    """Expose metrics for monitoring"""
37    avg_response_time = (
38        sum(metrics["response_times"]) / len(metrics["response_times"])
39        if metrics["response_times"] else 0
40    )
41    
42    return jsonify({
43        "total_requests": metrics["request_count"],
44        "total_errors": metrics["error_count"],
45        "error_rate": metrics["error_count"] / max(metrics["request_count"], 1),
46        "avg_response_time_seconds": avg_response_time,
47        "tool_usage": dict(metrics["tool_usage"])
48    })

Now you have a /metrics endpoint that shows how your agent is performing. You can check this periodically to see trends. Is the error rate increasing? Are response times getting slower? These signals help you catch problems before users complain.

For production systems, you'd typically use a proper metrics library like Prometheus or send metrics to a monitoring service like Datadog or New Relic. But the principle is the same: track the numbers that matter, and watch for changes.

Using Logs for Monitoring

In Chapter 12, we added logging to make the agent's behavior observable. Those logs are also valuable for monitoring. By analyzing logs, you can detect patterns and problems that metrics alone might miss.

For example, you might notice in the logs that a specific tool is failing frequently:

12025-11-10 14:32:15 - assistant - ERROR - Weather API call failed: Connection timeout
22025-11-10 14:35:22 - assistant - ERROR - Weather API call failed: Connection timeout
32025-11-10 14:38:45 - assistant - ERROR - Weather API call failed: Connection timeout

This pattern tells you the weather API is having problems. Even if your overall error rate is low, this specific tool is unreliable right now.

Many deployment platforms provide log aggregation and search tools. You can set up alerts based on log patterns. For example, "notify me if ERROR appears in the logs more than 10 times in an hour." This turns your logs into an active monitoring system.

You can also use logs to track user experience issues that don't show up as errors. For example, if you log when the agent asks for clarification, you might notice users are frequently confused by a certain type of request:

12025-11-10 14:32:15 - assistant - INFO - Requesting clarification for ambiguous query
22025-11-10 14:35:22 - assistant - INFO - Requesting clarification for ambiguous query
32025-11-10 14:38:45 - assistant - INFO - Requesting clarification for ambiguous query

This pattern suggests your agent's understanding could be improved for these cases, even though nothing is technically broken.

Building Reliability Through Error Handling

Monitoring tells you when problems occur. Reliability is about handling those problems gracefully so users still get a good experience.

The key to reliability is expecting things to go wrong. Network requests fail. APIs have outages. Databases get slow. Your agent needs to handle these situations without crashing or leaving users stranded.

Graceful Degradation

When a non-critical component fails, your agent should continue working with reduced functionality rather than failing completely. This is called graceful degradation.

For example, if your agent uses a weather API but that API is down, the agent should acknowledge the problem and offer what help it can:

1def get_weather(location):
2    try:
3        weather_data = weather_api.fetch(location)
4        return f"The weather in {location} is {weather_data['condition']}, {weather_data['temperature']}°F"
5    except Exception as e:
6        logger.warning(f"Weather API unavailable: {e}")
7        return "I'm having trouble accessing weather information right now. Please try again later or check a weather website directly."

The user doesn't get the weather, but they get a helpful explanation instead of a cryptic error. The agent degrades gracefully.

Retry Logic

Some failures are transient. A network request might fail once but succeed if you try again. For operations that might fail temporarily, adding retry logic can significantly improve reliability.

Here's a simple retry pattern:

1import time
2
3def call_with_retry(func, max_attempts=3, delay=1):
4    """Call a function with automatic retries"""
5    for attempt in range(max_attempts):
6        try:
7            return func()
8        except Exception as e:
9            if attempt < max_attempts - 1:
10                logger.warning(
11                    f"Attempt {attempt + 1} failed: {e}. Retrying in {delay}s..."
12                )
13                time.sleep(delay)
14            else:
15                logger.error(f"All {max_attempts} attempts failed: {e}")
16                raise
17
18## Use it for API calls that might fail temporarily
19def fetch_weather(location):
20    return call_with_retry(
21        lambda: weather_api.fetch(location)
22    )

This function tries up to three times before giving up. If the first attempt fails due to a temporary network hiccup, the second attempt might succeed. This makes your agent more resilient to transient failures.

Be thoughtful about what you retry. Retrying makes sense for network errors or timeouts. It doesn't make sense for errors like "invalid API key" or "malformed request", which will fail the same way every time.

Timeouts

Sometimes an operation doesn't fail, it just hangs. A tool call might wait indefinitely for a response that never comes. Without a timeout, your agent gets stuck, unable to respond to the user or handle other requests.

Always set timeouts for external operations:

1import requests
2
3def call_external_api(url, data):
4    try:
5        # Set a 10-second timeout
6        response = requests.post(url, json=data, timeout=10)
7        return response.json()
8    except requests.Timeout:
9        logger.error(f"API call to {url} timed out")
10        return None
11    except Exception as e:
12        logger.error(f"API call failed: {e}")
13        return None

With a timeout, if the API doesn't respond within 10 seconds, your agent gives up and returns an error. This is better than hanging forever.

Fallback Responses

When all else fails, your agent should still respond to the user. Even if it can't complete the requested task, it can acknowledge the problem and suggest alternatives.

1def process_agent_request(user_message):
2    try:
3        # Try to process normally
4        return agent.process(user_message)
5    except Exception as e:
6        logger.error(f"Agent processing failed: {e}")
7        # Return a fallback response
8        return (
9            "I encountered an unexpected problem and couldn't complete your request. "
10            "Please try again, or rephrase your question if the issue persists."
11        )

This fallback ensures users never see a blank screen or a raw error message. They get a response that explains what happened and suggests what to do next.

Handling Load: When Many Users Arrive

So far we've focused on keeping a single agent instance healthy. But what happens when your agent becomes popular and many people want to use it at once?

A single agent instance can only handle one request at a time (or a small number if you use async programming). If 100 people try to use the agent simultaneously, most of them will wait. If 1000 people try, the agent might crash from the load.

This is where scaling comes in. Scaling means increasing your agent's capacity to handle more requests.

Vertical Scaling

The simplest approach is vertical scaling: giving your agent more resources. If it's running on a server with 1GB of RAM and 1 CPU core, you could upgrade to 4GB of RAM and 4 CPU cores. This lets the agent handle more requests before running out of resources.

Most cloud platforms make vertical scaling easy. You change a setting to use a larger instance size, restart your agent, and it now has more capacity.

Vertical scaling has limits, though. Eventually you hit the maximum size available, and it gets expensive to keep upgrading to bigger machines.

Horizontal Scaling

A more flexible approach is horizontal scaling: running multiple copies of your agent. Instead of one powerful instance, you run several smaller instances. A load balancer distributes incoming requests across all the instances.

For example, if one instance can handle 10 requests per second, running 5 instances gives you capacity for 50 requests per second. If you need more capacity, you add more instances.

Here's the conceptual setup:

1User Requests → Load Balancer → Agent Instance 1
2                               → Agent Instance 2
3                               → Agent Instance 3
4                               → Agent Instance 4

Each agent instance is identical. They all run the same code. The load balancer just picks which one should handle each request, spreading the work evenly.

Most cloud platforms and deployment services support horizontal scaling. You configure how many instances to run, and the platform handles the load balancing automatically.

Stateless Design

For horizontal scaling to work well, your agent should be stateless. This means each request should be self-contained, not depending on information stored in the agent's memory from previous requests.

If your agent stores conversation history in memory (a Python dictionary or list), that history only exists in one instance. If the next request from the same user goes to a different instance, that instance won't have the history.

The solution is to store state externally, in a database or cache that all instances can access:

1## Instead of storing in memory
2conversation_history = {}
3
4## Store in a shared database
5def get_conversation_history(user_id):
6    return database.get(f"history:{user_id}")
7
8def save_conversation_history(user_id, history):
9    database.set(f"history:{user_id}", history)

Now any instance can retrieve the conversation history for any user. The agent instances themselves are stateless, they just read and write to the shared database.

This is a significant architectural change, but it's necessary for scaling beyond a single instance. The good news is that if you design for statelessness from the start, scaling later becomes straightforward.

Monitoring in Production

As your agent runs in production, monitoring becomes an ongoing practice rather than a one-time setup. You'll develop a sense of what's normal for your agent and learn to spot anomalies.

Here are some practices that help:

Set Up Alerts: Configure your monitoring system to notify you when something looks wrong. For example, "alert me if error rate exceeds 5%" or "alert me if average response time exceeds 10 seconds". This way you learn about problems quickly, often before users report them.

Review Metrics Regularly: Even without alerts, check your metrics dashboard periodically. Look for trends. Is usage growing? Are certain features used more than others? Are there patterns in when errors occur?

Analyze Logs: When you see unusual metrics, dig into the logs to understand what's happening. Metrics tell you something is wrong; logs tell you what and why.

Track User Feedback: Metrics and logs show technical health, but user feedback shows whether people are actually satisfied. If users report frustration even when metrics look good, investigate. There might be a usability issue that technical monitoring doesn't capture.

Test Your Monitoring: Occasionally, verify your monitoring is working. Trigger an error intentionally and confirm you get alerted. This ensures your monitoring won't fail silently when you need it.

Reliability as a Mindset

Building a reliable agent isn't about writing perfect code that never fails. It's about expecting failures and handling them well. Every external API will have outages. Every network connection will occasionally drop. Every database will sometimes be slow.

The difference between a fragile agent and a reliable one is how it responds to these inevitable problems. A fragile agent crashes or hangs. A reliable agent catches the error, logs it, retries if appropriate, and gives the user a helpful response.

As you build and operate your agent, think about failure modes:

  • What happens if the LLM API is down?
  • What happens if a tool takes 60 seconds to respond?
  • What happens if the database connection is lost?
  • What happens if the agent receives malformed input?

For each scenario, make sure your agent handles it gracefully. Add error handling, timeouts, retries, and fallback responses. Test these paths to verify they work.

This mindset turns reliability from a feature into a fundamental part of how you build. Every new capability you add, you think about how it might fail and how the agent should respond.

Putting It Together

Let's look at a more complete example that combines monitoring and reliability patterns:

1## Example (Claude Sonnet 4.5)
2from flask import Flask, request, jsonify
3import anthropic
4import logging
5import time
6
7app = Flask(__name__)
8logger = logging.getLogger(__name__)
9
10## Metrics tracking
11metrics = {
12    "requests": 0,
13    "errors": 0,
14    "response_times": []
15}
16
17def call_with_retry(func, max_attempts=3):
18    """Retry failed operations"""
19    for attempt in range(max_attempts):
20        try:
21            return func()
22        except Exception as e:
23            if attempt < max_attempts - 1:
24                logger.warning(f"Attempt {attempt + 1} failed, retrying: {e}")
25                time.sleep(1)
26            else:
27                raise
28
29@app.route('/health', methods=['GET'])
30def health():
31    """Health check endpoint"""
32    return jsonify({"status": "healthy", "timestamp": time.time()})
33
34@app.route('/metrics', methods=['GET'])
35def get_metrics():
36    """Metrics endpoint"""
37    avg_time = sum(metrics["response_times"]) / len(metrics["response_times"]) if metrics["response_times"] else 0
38    return jsonify({
39        "total_requests": metrics["requests"],
40        "error_rate": metrics["errors"] / max(metrics["requests"], 1),
41        "avg_response_time": avg_time
42    })
43
44@app.route('/chat', methods=['POST'])
45def chat():
46    start_time = time.time()
47    metrics["requests"] += 1
48    
49    try:
50        user_message = request.json.get('message')
51        logger.info(f"Processing request: {user_message}")
52        
53        # Call agent with retry logic
54        def make_agent_call():
55            client = anthropic.Anthropic()
56            return client.messages.create(
57                model="claude-sonnet-4.5",
58                max_tokens=1024,
59                messages=[{"role": "user", "content": user_message}],
60                timeout=30.0  # 30 second timeout
61            )
62        
63        response = call_with_retry(make_agent_call)
64        
65        # Track metrics
66        response_time = time.time() - start_time
67        metrics["response_times"].append(response_time)
68        logger.info(f"Request completed in {response_time:.2f}s")
69        
70        return jsonify({"response": response.content[0].text})
71        
72    except Exception as e:
73        metrics["errors"] += 1
74        logger.error(f"Request failed: {e}")
75        return jsonify({
76            "error": "I encountered a problem processing your request. Please try again."
77        }), 500
78
79if __name__ == '__main__':
80    app.run(host='0.0.0.0', port=5000)

This example includes:

  • Health check endpoint for monitoring
  • Metrics tracking for requests, errors, and response times
  • Retry logic for transient failures
  • Timeout on the API call
  • Comprehensive logging
  • Graceful error handling with user-friendly messages

It's not production-ready for a large-scale system (you'd want external metrics storage, more sophisticated error handling, etc.), but it demonstrates the core patterns that make an agent reliable and observable.

What You've Learned

You now understand how to keep your agent healthy after deployment. You know how to monitor its status with health checks and metrics. You know how to build reliability through error handling, retries, timeouts, and graceful degradation. And you understand the basics of scaling when more users arrive.

These practices transform your agent from something that works on a good day to something that works consistently, even when things go wrong. That consistency is what makes users trust and rely on your agent.

In the next chapter, we'll look at maintenance and updates: how to improve your agent over time without disrupting users, and how to keep it running well as requirements change and new capabilities are added.

For now, try adding monitoring and reliability features to your agent. Set up a health check. Track some basic metrics. Add error handling to your tool calls. See how it feels to have visibility into your agent's operation and confidence that it will handle problems gracefully.

Glossary

Health Check: An endpoint or function that verifies a service is running and responsive. Health checks are used by monitoring systems to detect when an agent has stopped working.

Metrics: Numerical measurements tracked over time to understand system behavior. Common metrics for agents include request rate, error rate, and response time.

Graceful Degradation: The practice of continuing to provide reduced functionality when a component fails, rather than failing completely. For example, returning a helpful error message when a tool is unavailable.

Retry Logic: Automatically attempting a failed operation again, useful for handling transient failures like temporary network issues. Retries should be used thoughtfully to avoid retrying operations that will always fail.

Timeout: A maximum time limit for an operation to complete. Timeouts prevent operations from hanging indefinitely when something goes wrong.

Vertical Scaling: Increasing capacity by adding more resources (CPU, memory) to a single instance. Simpler than horizontal scaling but has practical limits.

Horizontal Scaling: Increasing capacity by running multiple instances of the agent. Requires stateless design but scales more flexibly than vertical scaling.

Stateless Design: An architecture where each request is self-contained and doesn't depend on information stored in the agent's memory. Stateless agents can scale horizontally because any instance can handle any request.

Quiz

Ready to test your understanding? Take this quick quiz to reinforce what you've learned about monitoring and reliability for AI agents.

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Reference

BIBTEXAcademic
@misc{monitoringandreliabilitykeepingyouraiagentrunningsmoothly, author = {Michael Brenndoerfer}, title = {Monitoring and Reliability: Keeping Your AI Agent Running Smoothly}, year = {2025}, url = {https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents}, organization = {mbrenndoerfer.com}, note = {Accessed: 2025-11-10} }
APAAcademic
Michael Brenndoerfer (2025). Monitoring and Reliability: Keeping Your AI Agent Running Smoothly. Retrieved from https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents
MLAAcademic
Michael Brenndoerfer. "Monitoring and Reliability: Keeping Your AI Agent Running Smoothly." 2025. Web. 11/10/2025. <https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents>.
CHICAGOAcademic
Michael Brenndoerfer. "Monitoring and Reliability: Keeping Your AI Agent Running Smoothly." Accessed 11/10/2025. https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents.
HARVARDAcademic
Michael Brenndoerfer (2025) 'Monitoring and Reliability: Keeping Your AI Agent Running Smoothly'. Available at: https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents (Accessed: 11/10/2025).
SimpleBasic
Michael Brenndoerfer (2025). Monitoring and Reliability: Keeping Your AI Agent Running Smoothly. https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents
Direct link:
https://mbrenndoerfer.com/writing/monitoring-reliability-ai-agents
AI Agent Handbook Cover
Part of AI Agent Handbook

This article is part of the free-to-read AI Agent Handbook

View full handbook
Michael Brenndoerfer

About the author: Michael Brenndoerfer

All opinions expressed here are my own and do not reflect the views of my employer.

Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.

With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.

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