How Observability Improves System Reliability

Another SRE anti-pattern stems from not having adequate observability which is the practice of understanding how systems behave by collecting and analyzing data from various sources. Without adequate observability, SREs and engineering teams are essentially flying blind, making it difficult to identify, diagnose, and resolve issues effectively. Some of the problems and consequences associated with inadequate observability can be: - Increased Mean Time to Detection (MTTD): With inadequate observability, it takes longer to detect issues in your system. This can lead to increased downtime and negatively impact user experience. - Increased Mean Time to Resolution (MTTR): Once you detect a problem, troubleshooting becomes more challenging without proper observability tools and data. This results in longer downtime and more significant disruptions. - Difficulty in Root Cause Analysis: Without comprehensive data on system performance, it's hard to pinpoint the root causes of incidents. This can lead to "fixing symptoms" rather than addressing underlying issues, leading to recurring problems. - Inefficient Capacity Planning: Inadequate observability can hinder your ability to monitor resource utilization and plan for scaling. This may result in overprovisioning or underprovisioning resources, both of which can be costly. - Limited Understanding of User Behavior: Observability isn't just about monitoring system internals; it also includes understanding user interactions. Without this knowledge, it's challenging to optimize your system for user needs and preferences. What are some of the practices and tools that SREs can use? - Logging: Implement structured logging and ensure that logs are collected, centralized, and easily searchable. Use logging toolings like Elasticsearch, Fluentd, or Loki. - Metrics: Define relevant metrics for your system and collect them using tools like Prometheus or InfluxDB. - Distributed Tracing: Implement distributed tracing to track requests as they traverse various services. Tools like Jaeger and OpenTelemetry can help you gain insights into service dependencies and latency issues. - Event Tracking: Capture important events and errors in your system using event tracking systems like Kafka or RabbitMQ. - Monitoring and Alerting: Set up monitoring and alerting systems that can notify you of critical issues in real time. Tools like Grafana or Prometheus help in this regard. - Anomaly Detection: Consider implementing anomaly detection techniques to automatically identify unusual behavior in your system. - User Analytics: Collect data on user behavior and interactions to better understand user needs and improve the user experience. By investing in observability, teams can proactively identify and address issues, improve system reliability, and provide a better overall user experience. It's a fundamental aspect of SRE principles and practices.

I recently had the opportunity to work with a large financial services organization implementing OpenTelemetry across their distributed systems. The journey revealed some fascinating insights I wanted to share. When they first approached us, their observability strategy was fragmented – multiple monitoring tools, inconsistent instrumentation, and slow MTTR. Sound familiar? Their engineering teams were spending hours troubleshooting issues rather than building new features. They had plenty of data but struggled to extract meaningful insights. Here's what made their OpenTelemetry implementation particularly effective: 1️⃣ They started small but thought big. Rather than attempting a company-wide rollout, they began with one critical payment processing service, demonstrating value quickly before scaling. 2️⃣ They prioritized distributed tracing from day one. By focusing on end-to-end transaction flows, they gained visibility into previously hidden performance bottlenecks. One trace revealed a third-party API call causing sporadic 3-second delays. 3️⃣ They standardized on semantic conventions across teams. This seemingly small detail paid significant dividends. Consistent naming conventions for spans and attributes made correlating data substantially easier. 4️⃣ They integrated OpenTelemetry with Elasticsearch for powerful analytics. The ability to run complex queries across billions of spans helped identify patterns that would have otherwise gone unnoticed. The results? Mean time to detection dropped by 71%. Developer productivity increased as teams spent less time debugging and more time building. They could now confidently answer "what's happening in production right now?" Interestingly, their infrastructure costs decreased despite collecting more telemetry data. The unified approach eliminated redundant collection and storage systems. What impressed me most wasn't the technology itself, but how this organization approached the human elements of the implementation. They recognized that observability is as much about culture as it is about tools. Have you implemented OpenTelemetry in your organization? What unexpected challenges or benefits did you encounter? If you're still considering it, what's your biggest concern about making the transition? #OpenTelemetry #DistributedTracing #Observability #SiteReliabilityEngineering #DevOps

Imagine you’re a data engineer. It’s 3 AM on a Friday. You’re home, asleep, but back in the office, your data pipeline is busy. And tonight, a bug sneaks into production. Just a tiny change, a single wrong script runs. Nobody notices at first (well, cause they’re busy on the weekend) Suddenly, fake transactions start landing in your main tables. Customer data gets mixed up. Dashboards shift, and nobody knows why. Years ago, this would have been a nightmare. By Monday morning, you’d be scrambling to guess what happened and where the mess began. But tonight is different,  Because every step your data takes is recorded. Your system has data lineage. It’s like having security cameras for your entire pipeline. Every row knows where it came from, every script leaves a footprint, and every transformation is logged. So when you wake up and check the dashboard, you see the story: ↬ What script ran ↬ When it started ↬ Which tables it touched ↬ Where the wrong values spread You hit rewind, isolate the problem, and fix only what needs fixing. And as a result, there will be no mass panic or engineers searching endlessly.  You can get answers even at 3 AM! This is the power of data lineage and observability: That’s how you sleep well as a data engineer. That’s how you build pipelines you can trust. – P.S: Did you learn something new with this post? Would you want more posts like this?

Most systems detect node or master failures using simple polling, and while this approach sounds straightforward, it has an interesting reliability issue... The typical approach is to observe a node directly. This usually means pinging it, checking if a port is open, or running a lightweight query to confirm it is alive. On paper, this seems fine, but all of these methods share the same weakness - what if the observer itself is wrong? In a distributed setup, network glitches are normal. Temporary packet loss, routing hiccups, or partial network partitions can easily make a healthy node appear unreachable to the observer. The usual way to deal with this is to retry multiple times and declare failure after the n-th consecutive failure. This creates a classic tradeoff. If n is small (or polling happens frequently), failure detection becomes fast, but false positives increase. A short-lived network blip can trigger an unnecessary failover, which can sometimes be more disruptive than the original issue. If n is large (or polling intervals are longer), false positives decrease, but real failures take longer to detect. That delay directly increases downtime. But there is a more reliable way to think about this problem when you already have a cluster of nodes available. Instead of relying on a single observer repeatedly polling a target node, you can allow multiple nodes in the cluster to independently perform health checks. The system then treats a node as failed only when a majority of observers agree that the node is unreachable. This consensus-based approach reduces the risk of false positives caused by network partitioning. Even if one observer loses connectivity, the rest of the cluster can still provide an accurate view of system health. Consensus is costly, so this approach is not the most cost-efficient. However, it can be very useful if your system is large enough and distributed across multiple geographies.

Three weeks ago, one of our agents routed hundreds of decisions based on data that had quietly gone wrong. No error. No flag. Nothing in the loop to surface it. We caught it because we built for observability before we built for scale. "Agent-first" is the new fetch. Most companies saying it haven't shipped a real agent into production. They've shipped a demo. At Monte Carlo, we're running three agents in our own operations. And customers like Axios are already monitoring all dimensions of agent reliability in theirs: context, behavior, outputs. Here's what we've learned actually breaks: — The data feeding the agent goes stale or drifts silently — The agent's behavior shifts without a model change — The output looks right but isn't The agent loop isn't the hard part. Knowing when it breaks is. The Princeton research co-authored by Sayash Kapoor and Arvind Narayanan on AI agent reliability found that across 14 agentic models, capability gains yielded almost no improvement in reliability. We've seen it firsthand. Production changes everything. A demo can tolerate a silent failure. Your operations can't. Build for observability first. When your agents fail, what are you actually monitoring: the model, the data, or both? #agents #AIobservability

If you can't see what an agent does, you can't improve it, you can't debug it, and you can't trust it. It's crazy how many teams are building agents with no way to understand what they're doing. Literally ZERO observability. This is probably one of the first questions I ask every new team I meet: Can you show me the traces of a few executions of your agents? Nada. Zero. Nilch. Large language models make bad decisions all the time. Agents fail, and you won't realize it until somebody complains. At a minimum, every agent you build should produce traces showing the full request flow, latency analysis, and system-level performance metrics. This alone will surface 80% of operational issues. But ideally, you can do something much better and capture all of the following: • Model interactions • Token usage • Timing and performance metadata • Event execution If you want reliable agents, Observability is not optional.

I'm back in the lab today and I decided to add some resiliency through automation. Today’s focus in my E-University network project was simple: build redundancy where it matters and detect failures fast, with validation and visibility baked in from the start. Here's what I added today: HSRP deployment with pyATS validation: - I deployed HSRP gateway redundancy across three campus networks (Main, Medical, and Research) spanning six PE routers and 11 HSRP groups, with load balancing across the two edge routers per site. Instead of configuring first and hoping it worked, I wrote pyATS/Genie validation tests up front to define the expected end state, automated the deployment with Python and Unicon, and then re-ran the tests to prove compliance. That test-first approach paid off immediately. The pyATS checks caught an IP addressing mistake (10.300.x.x is not valid) before it could turn into a troubleshooting session. BFD for sub-second failure detection: I also implemented BFD on edge links (not inside the MPLS core) to dramatically improve convergence time versus relying on OSPF hello/dead timers. With 100ms interval, 100ms min-rx, and multiplier 3, detection is roughly 300ms, compared to an OSPF dead interval around 40 seconds. Observability integrated into the stack: This is all tied into my containerized telemetry pipeline: - A Python collector (Netmiko) polling 16 devices every 30 seconds. - InfluxDB 2.7 for time-series storage. - Grafana dashboards that now include protocol health and redundancy state, not just CPU/memory/interface counters. The Grafana view includes OSPF neighbor counts, BGP session state and prefix counts, BFD up/down session totals, and the HSRP active/standby state across all 11 groups. How I kept it clean (Git workflow): One thing I’ve been trying to do more of is treating my lab like real engineering work. For the Grafana updates, I created a separate Git branch specifically to test new dashboard panels and provisioning changes so I could iterate without breaking the main lab project. Once everything looked right, it’s easy to merge back in, and if something goes sideways I can roll it back without touching the stable baseline. Why this matters: Network automation is not just about pushing configs faster. It is about building confidence through validation. Writing tests first forces you to define success criteria upfront, and passing tests gives you proof that the change actually worked. #NetworkAutomation #NetDevOps #pyATS

Buying an observability platform doesn't give you observability. Just like buying a gym membership doesn't make you fit. Tools matter, but observability is a system made up of people, processes, and instrumentation. It requires consistency, conventions, and collaboration across teams. Observability becomes a system when you have: 🔹 Instrumentation discipline: Services emit structured, meaningful telemetry and not whatever each developer prefers. 🔹 Semantic conventions: Attributes, span names, and error formats are consistent across services. 🔹 A reliable pipeline: OpenTelemetry Collectors route data predictably and safely. 🔹 Operational workflows: Engineers know how to investigate outages, not just where to click. 🔹 Ownership: Teams maintain what they instrument and review observability as part of delivery. Without these pieces, even the best tool becomes little more than a data sink. 🧩 Example: When Observability Fails as a Tool Imagine a company buys a premium observability platform. They hook up a few logs and metrics. Dashboards are created. Alerts are set. Then an incident happens. Engineers jump into dashboards and see CPU spikes but no correlated traces. They search logs, but every service logs differently. They pull up metrics, but have no context for which user flows are impacted. Everyone spends hours guessing. Why? Because they bought a tool, but never built a system. 🧩 Example: When Observability Works as a System Another team invests in: • Consistent OTel instrumentation across services • Shared semantic conventions • A unified collector pipeline • Playbooks for incident response • Regular observability reviews in sprint cycles When something breaks, engineers instantly see: • The failing service • The impacted user flows • The exact span where latency spikes began • Related logs with matching attributes • Recent deployments that touched that code path They don't just detect the issue, they understand it. That's observability as a system. 🎯 Bottom Line Observability isn't what you buy. It's what you build over time. Tools give you capabilities. Systems give you outcomes. 💬 How have you built observability beyond just tools in your organization? #Observability #OpenTelemetry #PlatformEngineering #SRE #O11yEngineering