Agentic observability

Agentic observability platforms: what teams should evaluate

A practical guide to observing AI agents and LLM applications across model calls, retrieval, tool execution, business APIs, infrastructure, permissions, cost, and outcomes.

Explore Agent Teams

Model calls
Tool execution traces
Tokens and cost
Production context

Direct answer

Agentic observability explains how an agent perceived, decided, and acted

Model latency and token usage are only part of the story. Teams also need the prompt and retrieval path, tool calls, business APIs, permissions, retries, errors, cost, downstream system behaviour, and final outcome—connected to conventional logs, traces, metrics, alerts, and incidents.

Start evaluating when

Customer-facing or internal LLM and agent workflows are entering production
Agents call tools, APIs, knowledge stores, scripts, or operational systems
Teams need to explain latency, failures, unexpected cost, unsafe actions, or inconsistent outcomes

Do not reduce it to model monitoring

Tokens and model latency do not explain a failed business workflow
Prompt records alone do not reveal tool, API, database, or infrastructure failures
Automation without scoped access, audit evidence, and approval boundaries increases operational risk

Evaluation criteria

Use one operating scenario and one evidence standard

Capture model requests, prompts or approved prompt metadata, tokens, latency, errors, retries, and trace context according to the privacy policy

Trace retrieval, tool calls, business APIs, queues, databases, and infrastructure around each agent run

Enforce identity, scoped permissions, approval gates, audit history, and high-risk action controls

Measure cost, failure rate, response quality signals, tool outcomes, and downstream business impact separately

Require every AI-assisted diagnosis or action to link back to reviewable telemetry and execution evidence

Decision view

Choose the operating model that fits your team

Observation scope

Service observability

Agentic observability

Execution path

Services, infrastructure, requests, and user journeys

Models, retrieval, tools, business APIs, memory, and automated actions

Primary question

Why is the system slow, failing, or unavailable?

Why did the agent produce this response, call this tool, or take this action?

Governance focus

Alerts, incidents, changes, access, and recovery

Cost, identity, approvals, auditability, action risk, and reviewable evidence

Put model calls back into the business transaction

One agent response may span a user request, prompt assembly, retrieval, model calls, tools, business APIs, queues, and databases. Model timing alone cannot explain the end-to-end result.

Propagate trace context across model, retrieval, tool, and service boundaries
Correlate tool execution with application logs, metrics, traces, and resource state
Record failure, retry, fallback, and downstream outcome without exposing prohibited data

Give agents useful context while preserving reviewability

An agent may assist investigation, but its conclusion must remain traceable to authorised queries, source evidence, tool results, and an execution record.

Limit every agent to the data and actions its role requires
Retain queries, evidence links, decisions, approvals, and execution results
Put consequential actions behind explicit policy and human approval where required

Extend LLM observability into an operating loop

LLM observability covers model behaviour, latency, tokens, and errors. Agentic observability adds tools, business actions, collaboration, memory, policy, and their impact on production systems.

Separate model, tool, service, and business outcome measurements
Connect agent runs to alerts, changes, incidents, and post-incident review
Turn proven diagnostic patterns into reusable workflows with clear automation boundaries

Evaluation path

Test a real production workflow before expanding scope

Inventory the models, retrieval systems, tools, APIs, data, and actions available to each agent
Add trace context, error, latency, and cost signals across the complete run
Define identity, data scope, approval, audit, retention, and prohibited-action policies
Replay a real failure and verify that every conclusion can be traced to evidence
Expand automation only after quality, safety, operational, and rollback gates pass

FAQ

Frequently asked questions

How is agentic observability different from LLM observability?

LLM observability focuses on model inputs, outputs, tokens, latency, and errors. Agentic observability also covers retrieval, tools, APIs, memory, permissions, approvals, actions, system impact, and the evidence used to justify them.

What data is needed to observe an AI agent?

Typical evidence includes run and trace IDs, model calls, approved prompt metadata, retrieval, tool inputs and outputs, API and service traces, errors, retries, tokens, cost, permissions, decisions, and final outcomes. Privacy policy determines what may be retained.

How does Guance support agentic observability?

Guance provides AI Agent Observability, Obsy AI, OWL CLI, MCP Server, and Agent Teams capabilities that can connect authorised observability context with diagnostic and controlled-action workflows. Exact access and automation boundaries must be configured for the deployment.

Next step

Evaluate Guance with one of your real production scenarios

Bring your current tools, telemetry volume, incident workflow, operating constraints, and success criteria. We will help define a bounded evaluation and a reversible adoption path.