Understanding System Observability

As AI agents move from experiments into real, multi-agent systems, its important to ask the question: How do we know they’re actually working — and toward the outcomes we care about?    That’s where AI agent observability becomes the next measured leap (https://deloi.tt/4szsO3I).    As organizations move from “human in the loop” to “human on the loop,” agents stop being tools and start behaving more like digital teammates, with execution giving way to supervision. Productivity gives way to accountability. Intuition alone stops being enough.    Within this reframing, observability isn’t just a technical capability. It’s a technology-enabled, discipline that lets organizations see, understand, and continuously improve how agents perform against goals, not just system metrics. We’re seeing this shift across a number of functions:     🟢 From execution to oversight. Agents can take on repeatable work, but humans don’t disappear; their role evolves. Oversight, judgment, and intervention become the differentiators.    🟢 From legacy KPIs to agent-native metrics. Traditional measures don’t translate cleanly to autonomous systems. New KPIs need to appraise impact, productivity, and risk.    🟢 From one-off deployments to agent operations. Observability, governance, and tuning have to scale across use cases, not get rebuilt every time.    When we get human oversight and control right, we can enable our organizations to move faster with confidence (and avoid any surprises) as agents take on more responsibility. Great job Prakul Sharma, Parth Patwari and Brijraj Limbad!

When leaders brainstorm over trackers instead of architectures 😅 If only pipelines ran as smoothly as the meetings about how to track them.. As funny as it sounds, this happens way too often in data teams — hours spent debating Jira structures, story points, epics, and subtasks… Meanwhile a pipeline is quietly failing in production. But behind the humor lies an important reminder: → 𝐺𝑟𝑒𝑎𝑡 𝑑𝑎𝑡𝑎 𝑒𝑛𝑔𝑖𝑛𝑒𝑒𝑟𝑖𝑛𝑔 𝑖𝑠𝑛'𝑡 𝑎𝑏𝑜𝑢𝑡 𝑝𝑒𝑟𝑓𝑒𝑐𝑡 𝑡𝑟𝑎𝑐𝑘𝑒𝑟𝑠—𝑖𝑡'𝑠 𝑎𝑏𝑜𝑢𝑡 𝑡ℎ𝑒 𝑟𝑖𝑔ℎ𝑡 𝑡ℎ𝑖𝑛𝑘𝑖𝑛𝑔. Over the years, one pattern stands out: Teams that obsess over tools often under-invest in architecture. And teams that anchor on architecture naturally simplify everything else—tooling, tracking, delivery. Sharing few learnings that have made difference in my data engineering journey to build robust data systems: 1. Think in systems, not tasks Before assigning story points, ask: → What domain does this belong to? → What data contracts govern it? → Is this transformation even necessary? Clear system thinking > endless subtasks. 2. Architecture over trackers A well-defined: → Data model → Lineage flow → Orchestration pattern → and error strategy removes 80% of ticket back-and-forth. Your Jira gets simpler because your architecture is clearer 3. Invest in observability early Strong quality checks, lineage, and alerts mean: → Faster debugging → Better collaboration → No 2 AM firefighting Observability is invisible until you desperately need it. 4. Document why, not just what Trackers show what you did. Architecture docs explain why. Future you will thank present you. 5. Reduce cognitive load → Simplified schemas. → Modular pipelines. → Automated steps. Less time deciphering = less time debating story points. Maturity isn't measured by tracker maintenance — it's measured by systems that don't require constant firefighting. Here's what separates good data engineers from great ones- → Ask "what breaks if this fails?" before writing code → Think in layers, not monoliths → Build systems their junior teammates can debug → Optimize for the team inheriting their work, not just shipping fast → Know when NOT to over-engineer, Right-sizing matters more than resume-driven development → Understand that 99% vs 99.9% uptime isn't a rounding error—it's millions in cost 👉 Remember: Your Jira board doesn't run your pipelines. Your architecture does. Spend your energy accordingly. 𝗕𝘂𝗶𝗹𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲𝘀, 𝗻𝗼𝘁 𝗲𝗻𝗱𝗹𝗲𝘀𝘀 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝘁𝗮𝗹𝗲𝘀.

🚀 Building Observable Infrastructure: Why Automation + Instrumentation = Production Excellence and Customer Success After building our platform's infrastructure and application automation pipeline, I wanted to share why combining Infrastructure as Code with deep observability isn't optional—it's foundational as shown in screenshots implemented on Google Cloud. The Challenge: Manual infrastructure provisioning and application onboarding creates consistency gaps, slow deployments, and zero visibility into what's actually happening in production. When something breaks at 3 AM, you're debugging blind. The Solution: Modular Terraform + OpenTelemetry from Day One with our approach centered on three principles: 1️⃣ Modular, Well architected Terraform modules as reusable building blocks. Each service (Argo CD, Rollouts, Sonar, Tempo) gets its own module. This means: 1. Consistent deployment patterns across environments 2. Version-controlled infrastructure state 3. Self-service onboarding for dev teams 2️⃣ OpenTelemetry Instrumentation of every application during onboarding as a minimum specification. This allows capturing: 1. Distributed traces across our apps / services / nodes (Graph) 2. Golden signals (latency, traffic, errors, saturation) 3. Custom business metrics that matter. 3️⃣ Single Pane of Glass Observability Our Grafana dashboards aggregate everything: service health, trace data, build pipelines, resource utilization. When an alert fires, we have context immediately—not 50 tabs of different tools. Real Impact: → Application onboarding dropped from days to hours → Mean time to resolution decreased by 60%+ (actual trace data > guessing) → nfrastructure drift: eliminated through automated state management → Dev teams can self-service without waiting on platform engineering Key Learnings: → Modular Terraform requires discipline up front but pays dividends at scale. → OpenTelemetry context propagation consistent across your stack. → Dashboards should tell a story by organising by user journey. → Automation without observability is just faster failure. You need both. The Technical Stack: → Terraform for infrastructure provisioning → ArgoCD for GitOps-based deployments → OpenTelemetry for distributed tracing and metrics → Tempo for trace storage → Grafana for unified visualisation The screenshot shows our command center : → Active services → Full trace visibility → Automated deployments with comprehensive health monitoring. Bottom line: Modern platform engineering isn't about choosing between automation OR observability. It's about building systems where both are inherent to the architecture. When infrastructure is code and telemetry is built-in, you get reliability, velocity, and visibility in one package. Curious how others are approaching this? What's your observability strategy look like in automated environments? #DevOps #PlatformEngineering #Observability #InfrastructureAsCode #OpenTelemetry #SRE #CloudNative

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.

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.

When building a platform, I would argue that observability is the single most important aspect along with the processes around the consumption of that telemetry. Consistent output, taxonomy of keys, and standardized dashboards give you a clear picture of every aspect of your platform. This allows your team to view not only their dashboards with robust understanding, but also those of other teams while diagnosing issues. The common taxonomy allows for a clear understanding without specific domain knowledge. But, what is the endgame for all of this? Many would say its to diagnose issues, and while that isn't wrong, its too late. Your culture around observability has to be proactive in nature. Your processes, especially on-call transitions, should be about looking at the overall health of the system and identify potential issues. Addressing issues before they impact your customer MUST be the primary function of your operations footprint.

Succeeding with observability in the cloud Complexity makes observability a necessary evil The complexity of modern cloud environments amplifies the need for robust observability. Cloud applications today are built upon microservices, RESTful APIs, and containers, often spanning multicloud and hybrid architectures. This interconnectivity and distribution introduce layers of complexity that traditional monitoring paradigms struggle to capture. Observability addresses this by utilizing advanced analytics, artificial intelligence, and machine learning to analyze real-time logs, traces, and metrics, effectively transforming operational data into actionable insights. One of observability’s core strengths is its capacity to provide a continuous understanding of system operations, enabling proactive management instead of waiting for failures to manifest. Observability empowers teams to identify potential issues before they escalate, shifting from a reactive troubleshooting stance to a proactive optimization mindset. This capability is crucial in environments where systems must scale instantly to accommodate fluctuating demands while maintaining uninterrupted service. The significance of observability also lies in its alignment with modern operations practices, such as devops, where continuous integration and continuous delivery demand rapid feedback and adaptation. Observability supports these practices by offering real-time insights into application performance and infrastructure health, allowing development and operations teams to collaborate effectively in maintaining system reliability and agility.

Imagine you’re driving a car with no dashboard — no speedometer, no fuel gauge, not even a warning light. In this scenario, you’re blind to essential information that indicates the car’s performance and health. You wouldn’t know if you’re speeding, running out of fuel, or if your engine is overheating until it’s potentially too late to address the issue without significant inconvenience or danger. Now think about your infrastructure and applications, particularly when you’re dealing with microservices architecture. That's when monitoring comes into play. Monitoring serves as the dashboard for your applications. It helps you keep track of various metrics such as response times, error rates, and system uptime across your microservices. This information is crucial for detecting problems early and ensuring a smooth operation. Monitoring tools can alert you when a service goes down or when performance degrades, much like a warning light or gauge on your car dashboard. Now observability comes into play. Observability allows you to understand why things are happening. If monitoring alerts you to an issue, like a warning light on your dashboard, observability tools help you diagnose the problem. They provide deep insights into your systems through logs (detailed records of events), metrics (quantitative data on the performance), and traces (the path that requests take through your microservices). Just as you wouldn’t drive a car without a dashboard, you shouldn’t deploy and manage applications without monitoring and observability tools. They are essential for ensuring your applications run smoothly, efficiently, and without unexpected downtime. By keeping a close eye on the performance of your microservices, and understanding the root causes of any issues that arise, you can maintain the health and reliability of your services — keeping your “car” on the road and your users happy.

If you’re starting your Cloud / DevOps journey in 2025 — make sure Prometheus and Grafana are on your roadmap.. Here’s a quick overview: Both Prometheus and Grafana are observability tools focused on monitoring — → Prometheus collects and stores metrics (plus alerting). → Grafana visualizes those metrics through rich dashboards. Companies can: - self-host them on VMs or Kubernetes, - or choose managed offerings from major cloud providers (AWS, Azure, GCP) + grafana cloud Let’s dive in further: Prometheus handles: ↳ Collecting and storing metrics (from servers, containers, apps) ↳ Alerting when thresholds are breached (via Alertmanager) ↳ Infrastructure monitoring with exporters ↳ SLA/SLO tracking Grafana shines at: ↳ Building rich, interactive dashboards for metrics ↳ Business KPI monitoring alongside system data ↳ Log & metric correlation for root cause analysis ↳ Visualizing trends for capacity planning Why are they preferred for monitoring: • Real-time visibility across systems • Faster incident response with both historical + live data • Simplified application performance tracking (latency, error rates, throughput) • Deep insights into container & Kubernetes health (pods, resources, clusters) • Open-source and cloud-agnostic → no vendor lock-in That’s why they’ve become the go-to choice for modern observability. ⸻ Monitoring isn’t just about collecting data — it’s about using the right tools to see the bigger picture and act before things break. Question for you: Have you used Prometheus & Grafana mainly for infra monitoring, or also for business KPIs? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

𝗬𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗱𝗱 𝗢𝗽𝗲𝗻𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 𝘁𝗼 𝗲𝘃𝗲𝗿𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 OpenTelemetry has saved my project from a 1,000$ bug 👇 When something breaks in production, do you run through logs hoping to find the issue? 😵 That's where OpenTelemetry steps in - your single tool for tracing, metrics, and logs. On a project I have been working on, we received a Critical Alert 🚨from Seq (a centralized tool for logging and distributed tracing) that some requests started failing with a timeout. As the issue was critical, we jumped into analyzing the issue straightaway. This project was a Modular Monolith - one single deployment unit. Luckily, we had OpenTelemetry in this project, and with the help of distributed traces - we had all database queries right before our eyes. We had 4 database queries, and the slowest was 6 seconds long. However, the overall API request failed with a timeout of 2 minutes. So we clearly understood that the bottleneck is in our code, not the database. We took a SQL query from the distributed traces, ran it on our test database. We saw that it was returning too many rows at once. Which further caused a cartesian explosion in our code as we were comparing each row with the entire result set. With the help of OpenTelemetry, we quickly identified the issue, pinpointed the root cause, and deployed the fix within an hour - minimizing downtime for some API endpoints and demonstrating the power of real-time observability. Which saved our client a 1,000$ dollar loss due to minimal downtime of some API requests. Without OpenTelemetry - finding such an issue using only logs could have taken many hours leading to a huge money loss. 🟡 𝗪𝗵𝗮𝘁 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗗𝗼𝗲𝘀 𝗢𝗽𝗲𝗻𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 𝗦𝗼𝗹𝘃𝗲? Without proper observability, debugging issues across services is guesswork. OpenTelemetry: ✅ Traces Requests across services from start to finish ✅ Captures Metrics like response times and errors ✅ Collects Logs with context for faster diagnosis 🟢 𝗪𝗵𝘆 𝗘𝘃𝗲𝗻 𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝗶𝗰 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗡𝗲𝗲𝗱 𝗜𝘁: Many think OpenTelemetry is for microservices only, but: ✅ Monoliths Still Have Bottlenecks: database and cache queries, API endpoints, or background jobs. ✅ Faster Debugging: Unified tracing across all external dependencies. Are you using OpenTelemetry in your .NET projects? If not, what's holding you back? Let’s discuss! 👇 --- ✅ If you like this post - 𝗿𝗲𝗽𝗼𝘀𝘁 to your network and 𝗳𝗼𝗹𝗹𝗼𝘄 me. ✅ Join 𝟯𝟱𝟬𝟬+ readers of my newsletter to advance your career in .NET and Software Architecture: https://bit.ly/4c6Jy9e --- #dotnet #aspnetcore #opentelemetry #monolith #programming #softwaredevelopment #bestpractices