Articles tagged “observability”
26 articles
Why 40% of multi-agent systems fail in production
Nearly half of multi-agent CX pilots fail within six months of production — not because the AI is wrong, but because teams don't understand how multi-agent systems break. Here are the five failure modes and how to diagnose each.
The Six Agent Observability Platforms That Matter in 2026
Most observability platforms were built for LLM apps, not agentic CX systems. Here's how the six that matter in 2026 hold up against the criteria that count.
The trace-to-dataset loop: turning live conversations into eval cases
Your best eval cases are already in your production traces. Here's how to automatically curate the interesting ones into a test suite that gets better every week without manual effort.
Why most AI agents in production are flying blind
57% of organizations have AI agents in production. Only a third are satisfied with their observability. Here's what's wrong with single-call LLM logging and how to build a monitoring stack that actually works.
How to debug the agent conversation you can't reproduce
AI agents fail in ways you can't reproduce by resending the same message. Deterministic replay captures what the agent saw and did so you can step through the failure exactly as it happened.
Tool receipts: verifiable proof of what your agent actually did
When your AI agent claims it called a tool and got a result, can you verify that? Tool receipts are structured execution proofs that catch fabricated tool outputs before they damage customer trust.
When your agent spends money: the A2A payments era
The A2A Agent Payments Protocol (AP2) lets AI agents initiate transactions autonomously. Here's what CX builders need to know: the use cases, the controls that make it safe, and how to keep spending observable alongside conversation quality.
Tracing AI agent failures across multi-step tool chains
When a production CX agent returns the wrong answer, the bug rarely lives in the last LLM call. Here's how to trace failures back to their root cause across multi-step tool chains.
Five things your CX agent needs that its framework won't provide
LLM frameworks handle conversations. They don't handle memory, tool reliability, pre-production testing, or observability. Here's what every production CX agent actually needs.
Managed Agents in 2026: Three Runtimes, Three Trade-Offs
Google, Anthropic, and OpenAI all shipped 'managed agents' in May 2026, and they mean completely different things. Here's what each runtime trades away for CX teams.
Cost Per Successful Outcome: The AI Agent Metric Teams Miss
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.
The Agent Development Lifecycle: Ship, Observe, Improve
Shipping an AI agent is easy. Keeping it reliable after launch is where most teams struggle. The ADLC gives you a structured approach: Intent, Build, Evaluate, Deploy, Observe -- and then do it again.
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.
Agentic AI in Production: From Prototype to Reliable Service
Ship agentic AI that doesn't break at 2 AM. Covers orchestration patterns (ReAct, planning loops), error handling, circuit breakers, graceful degradation, observability, and scaling — with TypeScript implementations you can steal.
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.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
