If you ship features powered by large language models, observability is not optional. You need traces, evaluations, cost, and safety signals on every call — provider-agnostic, queryable, and tied to your business outcomes.
LLM observability is the discipline of capturing what your large language model actually did on every request: the full prompt, the model's response, any tool calls, evaluation scores, latency, token usage, dollar cost, and safety signals like hallucination or prompt-injection indicators.
It is the foundation underneath every reliable LLM product. Without it, you are debugging in the dark — and you cannot prove to a customer, an auditor, or your CFO what the model is doing in production.
1. Traces. Every prompt, completion, system message, retrieved context, and tool call — joined into a single timeline per user request. Without traces, you cannot reproduce a bug.
2. Evaluations. Automated quality scoring: groundedness against retrieved sources, factuality, toxicity, PII leakage, format compliance, and task-specific rubrics. Run online (on production traffic) and offline (on regression sets).
3. Cost and performance. Tokens in and out, dollar cost per call, p50/p95/p99 latency, retry rates, and provider error rates. Sliced by feature, tenant, and model so you can answer "what is this customer costing me?"
4. Safety. Prompt-injection detection, jailbreak attempts, policy violations, and abnormal output patterns. Safety signals must be inline so you can block, not just log.
Datadog, New Relic, and Honeycomb were built for HTTP services. They see spans and errors. They do not see whether your model hallucinated, whether the response was grounded in your knowledge base, or whether the cost per active user just doubled because a prompt template grew by 800 tokens overnight.
LLM observability tools are purpose-built for this shape of data: long string payloads, evaluation pipelines, model-version diffs, and per-token economics.
A production-grade stack has five layers: capture (SDK in every service that calls a model), storage (queryable trace store with retention policy), evaluation (online evals on sampled traffic, offline evals on labeled sets), alerting (regressions, cost spikes, safety triggers), and governance (policy engine that can block calls in flight).
The last layer is where WatchTower differentiates. Observability that only reports is half a product. Observability that can prevent — block a tool call, refuse a high-impact action, quarantine an agent — is what enterprise security teams actually buy.
LLM observability captures a single model invocation. AI agent monitoring captures the entire autonomous loop — the agent's plan, every tool it calls, the state it carries across turns, and the side effects on your systems.
If you ship a chatbot, observability is enough. If you ship an agent that can act on your behalf, you need monitoring on top of observability.
A plain-English beginner's guide to AI agent monitoring — what it is, why it matters, the key metrics to track, the tools to know, and a step-by-step starter plan to keep your AI agents safe, reliable, and performing at their best.
LLM observability has become the operational backbone of enterprise AI. This guide explains what it is, why traditional APM falls short, the four signals every team must track, and how to build an observability stack that survives production traffic.
Logs are not enough. This deep dive explains why the next generation of observability has to model intent, not just events, and shows the architecture, instrumentation, and detection patterns that work in production agent stacks.
Hallucinations stop being a curiosity the moment an agent uses one to call an API. This guide explains the architecture, validation patterns, and policy gates that drive consequential hallucinations toward zero in production agent stacks.
Get production-grade LLM observability in days, not quarters.