LLM Observability and AI Application Monitoring

What LLM observability should answer

Which step produced the delay, error, cost, or unexpected output?

A useful LLM trace preserves the model, input and output, token use, duration, status, retrieval and tool steps, and downstream service context that were actually instrumented. Operational telemetry does not by itself prove response quality or factual accuracy.

Solution overview

Guance LLM Explorer organises documented LLM Trace and Span data so teams can search and inspect model calls and their execution path. Available fields, providers, frameworks, scores, and correlation depend on the selected instrumentation and current integration documentation.

Operational challenges

One response contains many steps: Retrieval, tools, model calls, APIs, and application code can each introduce latency or failure.

Token use lacks context: A total does not explain which model, prompt path, tool, tenant, or release generated the usage.

Unexpected output is difficult to reproduce: Model behaviour depends on input, context, parameters, retrieval results, tools, and versioned application logic.

Prompt data may be sensitive: Inputs, outputs, user attributes, and retrieved content require minimisation, masking, access, and retention decisions.

How Guance supports the workflow

Instrument documented LLM operations: Capture supported Trace and Span fields for model, retrieval, tool, and application steps.

Analyse performance and usage in context: Compare duration, status, token consumption, models, routes, releases, and workflow steps.

Continue into application evidence: Use shared trace and service context to inspect downstream APIs, logs, databases, and infrastructure where configured.

Govern captured content: Define whether inputs and outputs are collected, masked, sampled, retained, and visible to each role.

Investigation workflows

Inspect the complete instrumented request path

Use Trace and Span hierarchy: See the temporal relationship among model, retrieval, tool, API, and application operations.

Find latency and token-heavy steps

Compare like with like: Segment by model, operation, release, status, route, and workflow before drawing a conclusion.

Connect AI behaviour with the application stack

Keep trace context: Continue from an LLM operation into supported service, log, dependency, and runtime evidence.

Treat prompts and outputs as governed data

Collect deliberately: Minimise sensitive content, configure masking and access, and set retention according to policy.

Continue exploring

AI Agent observability

Observe documented Agent, Skill, Tool, model, and execution context.

Application Performance Monitoring

Connect LLM operations with application services and dependencies.

Log management and analytics

Search application and model-operation evidence with controlled access.

Frequently asked questions

What data does Guance LLM Explorer show?

The current documentation describes LLM Trace and Span views with input, output, model, token consumption, duration, status, execution hierarchy, metrics, and scores where those fields are instrumented and reported.

How can teams find a slow or failed LLM request?

Start with duration or status, inspect the Trace tree, and compare model calls, retrieval, tools, APIs, and downstream service evidence. The available path depends on configured instrumentation.

Does LLM observability measure answer quality automatically?

Operational telemetry can carry configured scores and evaluation results, but it does not automatically establish factual accuracy or business quality. Define evaluation criteria, datasets, review, and ownership separately.

How should prompt and response data be protected?

Collect only what the investigation requires, mask or omit sensitive content, restrict access, define retention, and verify the controls available for the selected deployment and instrumentation.