Stories from the War Trenches - When AI Can't Debug Your Production Nightmares

The 3 AM Wake-Up Call

“Customer reporting intermittent 503s in production. SEV1 declared.”

While everyone talks about AI revolutionizing software development and how automation will solve all our problems, here I am at 3 AM, coffee in hand, debugging an issue that would make even the most sophisticated AI throw in the towel.

For those of us working in enterprise service mesh support, this isn’t a rare occurrence - it’s Tuesday. Complex production mysteries like this one land on our desk regularly, each one unique in its own maddening way. This particular case is just one story from countless hours spent in the debugging trenches, where human expertise remains irreplaceable.

The Setup: Enterprise Complexity at Its Finest

Our customer, a major financial institution, was experiencing intermittent connection resets. The architecture looked innocent enough on paper:

Client Application (VM) → F5 Load Balancer → Envoy Gateway → Backend Services

But as anyone who’s debugged enterprise systems knows, the devil is always in the details. We had:

• Thousands of routes serving multiple application teams
• Service mesh configuration spanning months of accumulated changes
• Multiple teams involved: Platform, Network, and Application teams (all customer-side)
• Production environment where every debug step needs careful coordination

The symptoms? Classic intermittent 503 errors - the debugging equivalent of a headache that could be caused by anything.

The plot twist: This client application had been working perfectly for months when both client and backend were VMs talking directly to each other. The issues only started after the customer recently migrated their backend application behind the service mesh, introducing Envoy into the path. Classic case of “it worked fine until we added the proxy!”

When Fingers Start Pointing

The customer’s network team packet captures showed TCP resets originating from our Envoy proxies. Naturally, all eyes turned to us - the service mesh vendor. “Your proxy is sending resets!”

This is where the real investigation began. No AI tool would have helped here - we needed to understand the intricate dance between TLS handshakes, proxy configurations, and network behavior.

The Investigation: Old-School Detective Work

Step 1: Rule Out the Obvious

First, we examined Istio configurations. Were service entries getting recreated? Nope - timestamps showed they’d been stable for months.

Step 2: Stats Tell No Tales

Envoy stats showed no obvious errors. Dead end.

Step 3: Confirming the Source

Before diving into expensive trace logging, we needed to be absolutely certain. The customer’s network team had captured packets showing TCP resets, but we had to verify: were these resets actually originating from Envoy, or was F5 the culprit?

Working through the packet captures with the customer’s network team, we analyzed the TCP flow and confirmed the resets were indeed coming from the Envoy pods, not the F5. Only then did we proceed to the next step.

Step 4: Into the Trace Logs Rabbit Hole

Here’s where it gets interesting. Enabling trace logs on a production gateway serving thousands of routes isn’t a decision you make lightly - it significantly impacts performance and generates massive amounts of data.

The real-time coordination challenge via Microsoft Teams:

I’m on a video call, guiding the customer’s platform team member who’s sharing their screen:

1. Me instructing: “Enable trace logs on the gateway, but immediately redirect output to a file to handle the volume”
2. Platform team (customer side): Following my instructions, enables logging and starts file redirection
3. Verbal coordination: Platform team announces to the call “Log capture started!”
4. Network team (customer side): Immediately begins packet capture on hearing the verbal cue
5. Application team (customer side): Starts monitoring their systems for reset errors in real-time
6. Wait for reproduction: All teams watching their respective monitoring while we wait for the issue to occur
7. Synchronized stop: When app team spots the reset, everyone stops capturing simultaneously
8. Performance cleanup: I guide platform team to immediately revert log levels back to info level

This kind of multi-team, real-time coordination through a video call can’t be automated - it requires human judgment, clear communication, and experience managing production debugging sessions.

The Breakthrough: Pattern Recognition in the Noise

Scrolling through the massive trace logs around the reset timestamps, I noticed something interesting. There were many “no matching filter chain found” messages, but I observed a crucial difference:

• When the TLS inspector received smaller messages (< 16KB), the logs showed requestedServerName entries - indicating successful SNI extraction

  envoy/source/extensions/filters/listener/tls_inspector/tls_inspector.cc:137 tls:onServerName(), requestedServerName: server-name-sni-cant-share thread=24

• When the message size was > 16KB, these requestedServerName logs were mysteriously absent

To verify this pattern was consistent throughout the logs, I wrote a custom script:

gawk '
/tls inspector: recv:/ {
    match($0, /recv: ([0-9]+)/, m);
    if (m[1] > 16000) {
        print;
        c = 3;
        next;
    }
}
c > 0 {
    print;
    c--;
    if (c == 0) print "--- end of block ---\n";
}' envoy-logs.log

The script confirmed the pattern:

tls inspector: recv: 19035
closing connection from [client-ip]: no matching filter chain found

Every single time the TLS inspector received more than 16KB, the connection was reset with “no matching filter chain found” - and crucially, no requestedServerName log was present.

Diving Deeper: Local Reproduction and Code Analysis

Spotting the 16KB pattern was just the beginning. To truly understand what was happening, I needed to reproduce this locally and trace through the code.

I modified one of Envoy’s TLS inspector integration tests to send a >16KB ClientHello message and ran it in my local development environment. Sure enough, the same behavior appeared - the requestedServerName log was missing, just like in production.

This gave me the smoking gun I needed. Now I could step through the Envoy source code with a debugger and see exactly where the happy path was deviating. Following the code flow, I traced through the TLS inspector’s logic until I found where it was failing to parse the oversized ClientHello and bailing out without extracting the SNI.

This is the kind of deep-dive debugging that separates real problem-solving from surface-level troubleshooting - taking a production observation, reproducing it locally, and then methodically tracing through the source code to understand the exact failure point.

The Root Cause: A 16KB Limit Nobody Talks About

The cause? BoringSSL’s 16KB limit on TLS ClientHello messages. The client application was sending bloated 19KB ClientHello messages - apparently someone’s client had a serious case of “certificate extension obesity.” When Envoy’s TLS inspector tried to parse these oversized handshakes, it choked and couldn’t extract the SNI (Server Name Indication), defaulting to the classic move of rejecting the connection with a polite “nope, not my problem.”

The real culprit? That chatty client application that couldn’t keep its TLS introduction brief!

Here’s the kicker - Envoy wasn’t even emitting stats for this condition. I ended up contributing a fix to add proper observability: PR #39903. Now, after the fix, the client_hello_too_large stat will be properly reported for cases like this, making future debugging much easier.

What This Case Teaches Us

1. Production Debugging is Still an Art

No amount of AI can replace the intuition needed to:

• Design the right debugging strategy
• Coordinate between multiple teams
• Know which logs to capture and when
• Write custom analysis scripts for unique situations

2. Enterprise Complexity Defies Automation

This wasn’t a textbook scenario you’d find in any training dataset:

• Multi-vendor environment with interdependent components
• Timing-sensitive reproduction requirements
• Custom network configurations
• Legacy applications with unusual TLS behavior

3. The Human Element is Irreplaceable

The solution required:

• Understanding the nuances of TLS protocol implementation
• Knowing BoringSSL internals
• Recognizing patterns in noisy log data
• Making judgment calls about where to focus investigation efforts

The Real MVP: Experience and Persistence

While AI excels at pattern recognition in controlled environments, production debugging often involves:

• Domain expertise that comes from years of troubleshooting similar systems
• Creative problem-solving when standard approaches fail
• Cross-team collaboration that requires human communication skills
• Intuition about where to look when all obvious paths are exhausted

The War Continues

This case represents just one battle in an ongoing war. In enterprise service mesh support, we encounter production mysteries like this weekly - each with its own unique combination of network quirks, protocol edge cases, and multi-vendor complexity. One week it’s HTTP/2 flow control windows causing mysterious slowdowns, the next it’s certificate chain validation failures in multi-cluster setups.

Don’t get me wrong - I’m not anti-AI. These tools are incredibly valuable for code generation, documentation, and routine tasks. But when your production system is down and customers are impacted, you need engineers who can think outside the box, coordinate complex investigations, and dive deep into protocol internals.

Each debugging session teaches us something new, builds our intuition, and prepares us for the next inevitable 3 AM call.

The next time someone tells you AI will replace debugging skills, show them this case. Show them the custom AWK scripts, the multi-team coordination, the deep protocol knowledge, and the persistence required to solve real production mysteries.

The war trenches of production debugging are still very much human territory.

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Have your own war stories from the debugging trenches? I’d love to hear them. The more complex and AI-resistant, the better.