“observability”
Monitoring for Anomalous Tool Sequences: Detecting Exfiltration
Detect agent data exfiltration by tracing tool calls as OpenTelemetry spans and flagging anomalous tool sequences per trace.
Stress Testing MCP Servers Under Real Agent Workloads
Load-testing an MCP server like a REST API measures the wrong things. Model agent-like traffic - discovery bursts, chatty tool sequences, SSE subscriptions - run it with k6, and watch per-method latency, tool error rate, and first-event time.
Firebase Performance Monitoring in Flutter: Traces, Screens, and Network Metrics
Install firebase_performance, get app start and HTTP traces for free, then add the Flutter-specific pieces: per-screen traces, slow-frame counters, and console filters that answer real questions.
Session Replay for Agents: Reproducing a Full Run
Logs tell you what happened; replay tells you why. Recording agent inputs, tool calls, and model responses so any run can be deterministically reproduced and debugged.
Timeouts and Deadlines: Human Fallback When Agents Stall
Autonomous agents hang: a tool call never returns, a loop never converges. How to set deadlines, detect stalls, and degrade gracefully to a human.
Dashboarding Agent Fleets: From Spans to Business Metrics
A pipeline for turning OpenTelemetry agent spans into the metrics leadership actually asks about — task success, cost per task, and escalation rate — without losing the drill-down to a single trace.
Explainable Actions: Surfacing Agent Reasoning for Trust
Turn agent runs into structured, auditable action records — sessions, events, and callbacks as the backbone of explainability that users, auditors, and engineers can all read.
Kubernetes Observability: Metrics, Logs, and Traces in Practice
The three pillars of observability applied to Kubernetes — what to collect, which tools to use, and how to wire metrics, logging, and tracing together.
Root Cause Analysis Playbooks for Agent Failures: From Symptom to Fix
A systematic method for debugging agent failures: classify the symptom, walk a trace from the failing span upward, and apply a playbook for each failure class — routing, tool, context, hallucination, and infrastructure — with durable execution and state inspection as first-class techniques.
Correlation IDs Across Agent Handoffs
Keep a single identifier alive as control moves between agents: why log IDs break at handoffs, how OpenTelemetry context propagation and baggage carry correlation across boundaries, the security rules for propagating IDs, and a concrete schema for multi-agent trace correlation.
Trace Spans for Tool Calls: Instrumenting Function Execution
Instrument agent tool calls with OpenTelemetry spans: span structure, trace and span IDs, parent relationships, attributes versus events, status codes, wrapping sync and async tools, and the schema that makes tool spans queryable instead of just present.
Chaos Testing Agent Workflows: Breaking Things on Purpose
How to apply chaos engineering to agent workflows — fault injection for model outages, tool failures, and stalls, plus the observability needed to prove resilience.
Decision Logs as First-Class Data: Post-Mortems for Multi-Agent Systems
Treat agent decisions as structured, queryable data using OpenTelemetry logs and traces — and turn post-mortems into an automated, evidence-driven practice.
Alerting on Agent Anomalies: Drift, Hallucinations, and Stalls
A practical alerting strategy for production agents — what to detect, what thresholds to set, and how to wire LiteLLM alerting into Slack, email, and webhooks.
Trace Sampling in High-Volume Agent Systems
Head-based, tail-based, and parent-based sampling with OpenTelemetry — how to keep 1% of traces that still tell the whole story.
Visualizing Agent Trajectories: Debugging Loops and Dead Ends
Agent logs lie by omission. Learn to render trajectories as graphs, spot infinite loops and dead ends, and turn run visualization into a debugging practice.
Metrics for Agent Health: Error Rates, Tokens, and Turn Length
A concrete OpenTelemetry metric set for LLM agents — golden signals, instrument choice, cardinality discipline, exemplars, and a dashboard layout you can ship.
Tracing MCP Tool Calls with OpenTelemetry
Instrument Model Context Protocol (MCP) tool executions and agent workflows with OpenTelemetry distributed tracing for end-to-end observability.
Structured Logging for Agent Decisions: Events, Inputs, and Outputs
How to implement structured logging for AI agent systems using OpenTelemetry, AgentTrace, and production observability tools.
Distributed Tracing for Multi-Agent Systems
Implement distributed tracing for multi-agent systems using OpenTelemetry. Track tool calls, reasoning chains, and inter-agent communication across your fleet.
Agent Evaluation with OpenTelemetry GenAI Semantic Conventions
Evaluate AI agent performance using OpenTelemetry and GenAI semantic conventions. Build observability pipelines that track tool calls, latency, cost, and quality.
LLM Gateways: Unified Routing, Quotas, and Observability for AI Traffic
Deploy an LLM gateway to unify routing, quotas, and observability across your AI infrastructure. Learn to set up LiteLLM as a production gateway for 100+ LLM providers.
Agent Tracing and Observability: Debugging Every Decision
Master built-in tracing in the OpenAI Agents SDK to debug, visualize, and monitor agent workflows. Learn to create traces, spans, and custom processors for production observability.
Observability for Web Apps: RUM and Error Tracking
A practical guide to real user monitoring and error tracking for web apps — measuring Core Web Vitals, capturing console, page, and network errors, and shipping batched telemetry to your own analytics backend.
Cost Monitoring for Production LLM Applications
Stop guessing what your LLM app costs. Learn how to capture token usage, attribute spend per user and feature, set budget alerts, and build a cost dashboard that scales.
The Agent Auditor: Observability and Compliance for Gemini 3 Workflows
When agents act autonomously, who audits their decisions? Build an Agent Auditor that records, replays, and validates every Gemini 3 workflow step for observability and compliance.
Observability for Agents: Tracing Tool Calls and Reasoning
An agent is a loop, not a single API call. Instrument it with OpenTelemetry AGENT/LLM/TOOL spans and a shared trace_id so you can attribute bad reasoning, slow dependencies, and tool failures.
The Self-Improving Codebase: Gemini 3 Agents that Refactor Based on Performance Logs
Self-improving agents edit their own harness based on mined execution traces. Learn the mine-propose-validate loop with Gemini 3 and strict regression gates.
The "Audit Log" Pattern: Forensic Analysis of Gemini 3 Reasoning Chains
Debugging agents means auditing reasoning, not stack traces. Build a tamper-evident audit log of Gemini 3 reasoning chains for forensics and compliance.
End-to-End OpenTelemetry Tracing for ADK Agents
Instrument Google ADK agent harnesses with OpenTelemetry to capture full-trajectory execution traces, tool latencies, and token metrics.
Monitoring ADK Agent Fleets with BigQuery Analytics
Learn how to stream structured telemetry from your Google ADK agent fleets into BigQuery for SQL-based cost tracking, latency analysis, and quality evaluation.
MLflow Observability for Google ADK
Learn how to use MLflow Tracing to observe and debug Google ADK agents and tools using OpenTelemetry.