#Observability

It’s The End Of Observability… as we know it. Traditional monitoring tells you: “𝘌𝘳𝘳𝘰𝘳 𝘳𝘢𝘵𝘦 𝘴𝘱𝘪𝘬𝘦𝘥 𝘢𝘵 2:47 𝘗𝘔” AI-powered observability tells you: “𝘠𝘰𝘶𝘳 𝘙𝘈𝘎 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦 𝘧𝘢𝘪𝘭𝘦𝘥. 𝘏𝘦𝘳𝘦’𝘴 𝘵𝘩𝘦 𝘦𝘹𝘢𝘤𝘵 𝘱𝘳𝘰𝘮𝘱𝘵 𝘵𝘩𝘢𝘵 𝘵𝘳𝘪𝘨𝘨𝘦𝘳𝘦𝘥 𝘪𝘵, 𝘢𝘯𝘥 𝘩𝘦𝘳𝘦’𝘴 𝘩𝘰𝘸 𝘵𝘰 𝘧𝘪𝘹 𝘪𝘵.” This is the future of observability in the age of AI. The old playbook worked when APIs were predictable and errors had stack traces. But LLMs are non-deterministic black boxes that can fail silently, hallucinate subtly, or degrade gradually without traditional metrics even noticing. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴: • AI debugging assistants that read your traces 🧠 • Real-time correlation across millions of high-cardinality events ⚡ • Semantic understanding of LLM failures, not just HTTP status codes 🔍 • Proactive anomaly detection that catches issues before customers do 🎯 𝗔𝗻𝗱 𝗶𝘁’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗹𝗽𝗶𝗻𝗴 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. • Pinterest processes 1M+ events daily with sub-second insights. • Stripe debugs payment flows across 50+ countries in real-time These aren’t just monitoring. This is next-level observability that thrives on speed and access to terabytes of data with context. Speed + Context = two things that make AI better. Come to learn more and check out demos during the Observability Day (September 11, SF). 🔗 Get your free spot! Link in bio _______ #aws #ai #observability #freeevent #cloudcomputing

🚨 Cloud engineers without observability are flying blind. It’s not enough to deploy infrastructure—you need to know what’s happening inside it. Observability helps you monitor performance, detect issues, and fix them fast. But here’s the key: observability isn’t just one tool—it’s a mindset. You need to monitor everything from your application, database, networking, and cloud resources. This video introduces a powerful workshop that shows you how. Comment “Observability” if you want the full list of resources! 🚀 #CloudEngineering #Observability #CloudMonitoring #DevOps #LogsMetricsTraces #TechSkills #techtokwithkriti #techtalkwithkriti #techwithkriti

Something broke. Users noticed before you did. No logs. No visibility. Added logging to a 3-month-old app: • 14% silent error rate • APIs timing out daily • One endpoint taking 18 seconds Production isn’t “working” if nobody can see what’s failing. Real logging = real operations. 😅 if you’re not sure your app has it. #VibeCoding #Observability #Logging #ProductionReady #FactionGroup

Layers of observability in AI systems, explained visually 🔍 If you're deploying LLM-powered apps to real users, you need to know what's happening inside your pipeline at every step. Here's the mental model (see the diagram): Think of your AI pipeline as a series of steps. For simplicity, consider RAG. A user asks a question, it flows through multiple components, and eventually, a response comes out. Each step takes time, each step can fail, and each step has its own cost. If you're only looking at input and output of the entire system, you'll never have full visibility. This is where traces and spans come in: → A Trace captures the entire journey, from user query to response. One continuous bar that encompasses everything. → Spans are individual operations within that trace. Each colored box represents a span. What each span captures: 1️⃣ Query span User submits question. Captures raw input, timestamp, session info. 2️⃣ Embedding span Query hits embedding model, becomes vector. Tracks token count and latency. 3️⃣ Retrieval span Vector goes to database for similarity search. Most RAG problems hide here - bad chunks, low relevance scores, wrong top-k values. 4️⃣ Context span Retrieved chunks get assembled with system prompt. Shows exactly what's fed to the LLM. 5️⃣ Generation span LLM produces response. Usually longest and most expensive. Logs input tokens, output tokens, latency. Without span-level tracing, debugging is almost impossible. You'd know the response was bad, but never know if it was due to bad retrieval, bad context, or LLM hallucination. Cost tracking is another big one. Span-level tracking shows where money is actually going. AI systems degrade over time. Span-level metrics help catch drift early and tune each component independently. 👉 Over to you: How do you monitor your AI systems? #ai #observability #llm

Get a quick recap of the news and innovations announced today at #CiscoLive: 🟠 @Splunk 🟣 #observability 🔵 @Webex 🟢 #security

Famous Production Pattern: Your Spring API handles 1M requests/min, but some are slow. How do you log every request and track slow API time—live in production? This is not random logging. This is structured observability for high-throughput Spring APIs. ⸻ 1️⃣ Filter / Interceptor Logging • Triggered before controller → record start time • Controller executes logic → record end time → log duration • Pro tip: Use OncePerRequestFilter (best for per-request logging in Spring) ⸻ 2️⃣ Measure & Flag Slow Requests • Latency > 500ms → ⚠️ Slow API detected • Example: GET /api/products → 602 ms ⚠️ POST /api/checkout → 780 ms ⚠️ ⸻ 3️⃣ Enable Distributed Tracing • Tools: Spring Cloud Sleuth + Zipkin / OpenTelemetry • Trace a request across microservices automatically • See exactly which service or DB call is slow Production lesson: Isolates bottlenecks in milliseconds. ⸻ 4️⃣ Centralized Log Aggregation • Ship logs from Spring Boot → ELK Stack / Splunk / Datadog • Create dashboards & alerts for slow APIs Production lesson: Enables real-time monitoring of production traffic at scale. ⸻ 5️⃣ Spring-Specific Optimizations • Use @Async for non-critical processing • Enable connection pooling (HikariCP) for DB • Add caching (Spring Cache + Redis) to reduce load Production lesson: Combine observability with Spring best practices for speed. ⸻ 🔥 Interview Ready One-Liner: Log every request with a filter, measure latency, trace slow calls, and monitor via centralized dashboards to debug high-throughput Spring APIs live in production. ⸻ Please follow @codedsoul_05 ❤️ #springboot #backend #observability #logging #tracing performance scalability productionready microservices techindia developers interviewquestions

It’s very critical to have a well designed monitoring system in place after model goes live. 1️⃣ In production, you watch three things at once I’ll explain this from an Azure lens. Every real system monitors: ➤ model quality ➤ data health ➤ service + business health Miss any one, and you’ll ship silent failures. ⸻ 2️⃣ Layer 1: model behavior You don’t check offline metrics once and move on. ➤ Periodic precision / recall when fresh labels arrive ➤ Prediction distribution over time ➤ Agreement with a simple baseline when labels lag In Azure, this is logged via Azure ML + Log Analytics. ⸻ 3️⃣ Layer 2: data & drift (where most failures start) Models fail because data changes. ➤ Feature distributions vs training data ➤ Missing values, schema changes ➤ Population / concept drift alerts Once drift hits, offline metrics stop meaning much. ⸻ 4️⃣ Layer 3: system + business metrics A “correct” model that times out is still broken. ➤ API latency p95 / p99 ➤ Error rates, retries ➤ Business KPIs: fraud caught, CTR, churn risk, ticket deflection, etc. Very important as they track business KPIs. These live alongside model logs in Log Analytics. ⸻ 5️⃣ Make monitoring actionable Dashboards aren’t enough. ➤ Thresholds → alerts ➤ Alerts → rollback or retrain ➤ Retraining triggered via Azure ML pipelines / SDK Monitoring without action is just logging. Also, In most cases, you retrain model based on a fixed frequency depending on your usecase on the latest data. ⸻ Bottom line: In real ML systems, you don’t “trust” a deployed model. You continuously verify model behavior, data stability, and business impact — that’s how production ML actually stays healthy. TAGS: #mlmonitoring #azureml #mlops #productionml #aiengineering #datadrift #observability #loganalytics #systemdesign #machinelearning #ai #datascience #ml #trend #engineering

Logs vs Metrics vs Traces 👇 📜 Logs → what happened 📊 Metrics → system health over time 🔗 Traces → request journey across services 💡 Use together for debugging production systems Tools: ELK • Prometheus • Grafana • Jaeger #devops #systemdesign #softwareengineering #backenddeveloper #observability

What is observability in devops? How it is different from monitoring? #devops #tech #interview #job #observability Good?

Does kubernetes have observability for observability? My Loki pod was crashing for weeks. I guess I need better monitoring so I can fix this stuff faster. #kubernetes #homelab #observability #techtok #learning

Day 107 | A log tells you what happened. A trace tells you why the agent thought it was a good idea. Debugging isn't about looking at error codes—it's about Reasoning Forensics. In 2026, when an agent takes 15 steps, calls three different tools, and still gives the wrong answer, you don't have a "bug"; you have a Traceability Gap. Here is the Strategic Blueprint for Agent Tracing: 1️⃣ The Hierarchical Trace (Parent-Child spans) In 2026, we don’t look at flat logs. We use Unified Traces where every user request is the "Parent" and every internal thought, tool call, and RAG retrieval is a "Child Span." Tools like LangSmith or Arize Phoenix allow you to visualize the Tree of Execution. If the agent gets sidetracked in step 8, you can pinpoint the exact prompt or tool output that caused the "Reasoning Drift." 🕵️‍♂️ 2️⃣ Decision-Step Monitoring Tracing isn't just for errors; it's for Performance Engineering. By injecting correlation IDs at the AI Gateway, you can monitor the latency and cost of each individual "Reasoning Step." You might find that your agent is spending 80% of its budget on a "Self-Reflection" loop that isn't actually improving the output. Trace data allows you to prune these inefficient paths and optimize the Total Cost of Reasoning. 📉 3️⃣ Production-to-Eval Feedback Loop The ultimate power of a trace is its ability to become a Test Case. In a mature 2026 LLMOps stack, any "failed" production trace is automatically exported into your evaluation dataset. This allows you to run Regression Tests against new prompt versions, ensuring that a fix for one "reasoning error" doesn't break three other successful workflows. This is how you move from "Trial and Error" to Scientific Iteration. 🧪 🏗️ IF YOU CAN'T TRACE IT, YOU CAN'T TRUST IT. In 2026, observability is the only way to move from "Agent Prototypes" to "Autonomous Platforms." The architect's job is to ensure the Evidence Trail is as robust as the agent itself. FOLLOW @Harsha_Selvi to master the elite AI infrastructure of 2026. ⬇️ #AIInfrastructure #DevOps2026 #AgentTracing #Observability #LLMOps HarshaSelvi SRE SystemDesign OpenTelemetry Bu

Lots of observability this week with Braintrust & Resolve AIs new funding rounds #venturecapital #vcmoney #observability