Layer A — Model & request health
Latency, errors, cost, and provider availability. Tools like Helicone, Langfuse, or cloud monitors often start here.
Guide · LLM Monitoring
LLM Monitoring that leads to recovery
Model and request metrics matter — production agent fleets also need behaviour signals, anomalies, and Runtime Cases when tools and intent fail.
What LLM Monitoring covers
LLM Monitoring typically tracks generations, latency, token cost, error rates, and sometimes prompt/version quality. That is necessary. It is not sufficient when multi-step agents choose the wrong tool with a successful HTTP 200 from the model provider.
Monitoring layers — from tokens to behaviour
Layer B — Generation quality
Scores, eval datasets, prompt experiments — Braintrust, LangSmith, Arize/Phoenix-style loops improve outputs before and after ship.
Layer C — Agent behaviour runtime
Sessions, decisions, tool selection, policy gaps, and recovery. PUVINoise specializes here as Behaviour Runtime Intelligence.
Layer D — Governed outcomes
Runtime Cases, MTTR, audit trails, and human override so monitoring closes into trusted remediation — not only dashboards.
Stack choices
Where PUVINoise fits in an LLM monitoring stack
Keep LLM tracers
Langfuse, LangSmith, Helicone, and similar tools remain valuable for prompt and request visibility.
vs Langfuse →Keep eval platforms
Braintrust and eval harnesses strengthen quality loops; production still needs behaviour ops.
vs Braintrust →Add behaviour runtime
PUVINoise for Command Centre, anomalies, Runtime Cases, and governed recovery across tenants.
Product overview →Signals to instrument
Provider, model, latency, and token/cost attributes on spans
Decision telemetry: intent, tool candidates, confidence
Tenant and agent identity on every resource
Anomaly hooks that open Runtime Cases
Recovery actions with policy and audit context
FAQ
LLM Monitoring — common questions
Does PUVINoise replace Langfuse or Helicone?
Usually no. Those tools excel at LLM tracing or gateway analytics. PUVINoise adds behaviour runtime and recovery for agent fleets. See Compare for fair framing.
How is this different from Agent Observability?
LLM Monitoring emphasizes model/request quality. Agent Observability emphasizes multi-step agent operations and MTTR. Read both guides — they reinforce each other.
How do we try it?
Start a free evaluation on app.puvilabs.com and emit SDK telemetry from a sample agent. Use Pricing when you need commercial clarity.
Monitor LLMs — and recover when agents drift
Start free on app.puvilabs.com, review Pricing for plans and AI credits, or browse comparisons for Langfuse, Helicone, Arize, and more.