Articles tagged “observability”
16 articles
How to Measure Cost Per Successful Outcome for AI Agents
Most teams measure AI agent quality by pass rate. The metric that actually predicts ROI is cost per successful outcome: what each resolution costs paired against whether it actually resolved. Here's how to build it.
How to Run the Agent Development Lifecycle (ADLC) in Production
Shipping an AI agent is easy. Keeping it reliable after launch is hard. The ADLC walks you through Intent, Build, Evaluate, Deploy, Observe, then back around.
Your Agent Re-reads Its Own Manual on Every Call
Datadog's 2026 State of AI Engineering report found that 69% of input tokens go to system prompts, yet only 28% of LLM calls use prompt caching. Here's how to diagnose the problem and fix it without rewriting your agent.
Your Agent Has Observability. It Doesn't Have Measurement.
89% of AI teams added observability. 52% added evals. But only 31% can say whether their agent is getting better or worse. Here's the difference between watching your agent and actually measuring it.
Your voice agent's P95 is lying. The real problem is P99.9
Per-stage P95 hides the tail customers feel. How variance compounds across STT, LLM, and TTS, and how to SLO the joint distribution.
Multi-Agent Systems Don't Fail at Reasoning. They Fail at Handoff.
Multi-agent systems don't fail at reasoning. They fail at handoff. Command objects, memory transfer, and the 8-10 handoff cliff, plus the telemetry that catches drift.
Reasoning Tokens Are Showing Up on the Bill
GPT-5 and Claude thinking tokens bill as output and stay invisible. A 200-token reply can hide 8,000 billable ones. How to measure, cap, and budget.
The Modern Data Stack Wasn't Built for Agents
Snowflake, dbt, and Fivetran were built for humans asking batch questions. Agents need streaming signals, per-entity memory in under 100ms, and write-back.
Every Failed Call Is a Test Case You Haven't Written Yet
The gap between staging and production for AI agents is measured in surprise. Here's how to close the loop from live failure to regression gate.
Is monitoring your AI agent actually enough?
Research shows 83% of agent teams track capability metrics but only 30% evaluate real outcomes. Here's how to close the gap with multi-turn scenario testing.
Online vs. Offline Evals: Close the Production Gap
89% of teams have observability but only 37% run online evals. Here's why that gap is where production failures hide, and how to close it with a practical online eval pipeline.
MCP Servers in Production: Observability from Day One
Instrument your MCP servers with OpenTelemetry for production-grade observability. Covers tracing tool calls, detecting loops, cost attribution, and alerting.
Build an AI Agent Observability Pipeline from Scratch
Build a production observability pipeline for AI agents using TypeScript and the Chanl SDK. Covers metrics, traces, quality scoring, drift detection, and alerting.
What to Trace When Your AI Agent Hits Production
OpenTelemetry GenAI conventions are the production standard for agent tracing. What to instrument, what to skip, and what breaks — from a 2 AM debugging war story.
IA Agentica en Produccion: De Prototipo a Servicio Confiable
Lleva IA agentica a produccion sin que se rompa a las 2 AM. Cubre patrones de orquestacion (ReAct, bucles de planificacion), manejo de errores, circuit breakers, degradacion elegante, observabilidad y escalamiento, con implementaciones en TypeScript que puedes reutilizar.
AI Agent Observability: What to Monitor When Your Agent Goes Live
Build a production observability pipeline for AI agents. Covers latency, token usage, tool success rates, conversation quality, drift detection, structured logging, alerting strategies, and the critical difference between LLM and agent observability.
The Signal Briefing
Un email por semana. Cómo los equipos líderes de CS, ingresos e IA están convirtiendo conversaciones en decisiones. Benchmarks, playbooks y lo que funciona en producción.
