Observability

Prerequisite: This is the starting part of the series — no prior part is required. Later parts assume the concepts introduced here. Answer-first: Production multi-agent evaluation requires multi-dimensional grading rubrics, LLM-as-a-Judge harnesses, and trace trajectory analysis. Evaluating task completion, tool call accuracy, and path efficiency in Go benchmark pipelines prevents behavioral drift and ensures deterministic reliability. Evaluating autonomous agent systems presents unique architectural challenges distinct from traditional software testing and static model benchmarking. While standard software unit tests verify deterministic inputs against exact output matches, multi-step LLM agents exhibit non-deterministic reasoning paths, dynamic tool selection, and complex conversation states. Relying solely on final answer accuracy masks hidden regressions such as redundant tool invocations, inefficient context expansion, or subtle prompt injection vulnerabilities. ...

Answer-first: Observability in modular monoliths leverages in-process OpenTelemetry span propagation across module boundaries without network serialization overhead. Combining in-memory context tracking with structured logging reduces telemetry ingestion costs while retaining microservice-level latency visibility. Prerequisite: Before reading this part, please review Part 4: CI/CD Simplified. Part 5: Observability in Memory – When Everything Shares a Single Call Stack What You’ll Learn: In-Memory Trace Propagation: How Go context propagation handles tracing across package lines without network calls (~15ns vs 1.2µs). Cardinality Reduction: Techniques to strip connection attributes from logs, saving thousands in observability SaaS bills. Sampling & eBPF Profiling: How Pyroscope/Parca eBPF engines continuously profile Go runtimes under 1% overhead compared to heavy APM agents. When operating a production system, observability determines whether an engineer resolves an outage within minutes or spends hours troubleshooting distributed failure modes. Microservices architectures make telemetry expensive and complex through distributed network span propagation. ...

Go’s built-in pprof profiler provides CPU sampling, heap allocation analysis, goroutine stack inspection, and blocking profiler — all available as HTTP endpoints in running production services with minimal overhead. Heap diff between two snapshots is the fastest way to identify memory leaks. Prerequisite: This is Part 10 of the System Design Masterclass. Previous parts built the architecture — this part teaches you how to see inside a running system and diagnose production performance issues. ...

Prerequisite: Familiarity with the concepts introduced in Part 5 — Security. Review it first if the terminology in this part is unfamiliar. Part 6 — MCP Observability & Tracing: Auditing the Control Plane Answer-first: Operating Model Context Protocol (MCP) servers without telemetry logging creates compliance vulnerabilities (violating OWASP MCP08: Lack of Audit & Telemetry). Instrumenting MCP servers with vendor-agnostic OpenTelemetry (OTel) tracing captures JSON-RPC 2.0 tool execution durations, argument metadata, and error rates in real-time Prometheus dashboards. ...

Goroutine Leak Detection and Fix in Production Go Services Writing automated test cases that detect goroutine leaks before deploying. Analyzing production runtime stack traces to locate orphaned channels. A Kubernetes pod abruptly restarts with exit code 137. The memory metrics dashboard shows a slow, perfectly linear staircase pattern stretching over three days. There are no panic logs in stdout, no database errors, and no abnormal CPU spikes. Just a slow, silent OOM (Out Of Memory) death. ...

Prerequisite: Familiarity with the concepts introduced in Part 8 — Inference Optimization Vllm. Review it first if the terminology in this part is unfamiliar. Part 9 — Agentic Observability: OpenTelemetry, Tracing & Cost Monitoring Debugging traditional microservices involves tracking HTTP status codes and database query latency. Debugging AI agent architectures demands tracking non-deterministic reasoning chains, LLM API token costs, prompt context inflation, and multi-turn tool loops. Without standardized distributed tracing, identifying why an agent query took 8.5 seconds or cost $1.20 per invocation becomes an impossible troubleshooting task. ...