What it is
Agent telemetry is the stream of data an AI agent emits as it runs: which tools it called, which data sources it touched, what it decided, how long each step took, how many tokens it burned, and whether each step succeeded. It extends the traditional observability model (metrics, events, logs, traces — known as MELT) with AI-specific signals like token usage, tool invocation chains, agent decision paths, hallucination detection, and drift tracking. The OpenTelemetry (OTel) project has emerged as the vendor-neutral standard for emitting this data, so teams can instrument once and route to any compatible backend.
Why it matters
An agent running in production without telemetry is a black box — when it hallucinates, drifts, over-spends, or silently breaks, you have no way to know or fix the cause. Telemetry turns the agent into an auditable, debuggable system: you can see which specific tool call produced a bad answer, track cost per workflow, catch quality regressions before customers do, and prove compliance when regulators ask. For Salesforce customers deploying Agentforce, native Agent Observability surfaces exactly this data inside the Salesforce platform — so every agent run is traceable, tunable, and accountable.
Key components
- Traces — the full step-by-step chain of what the agent did from input to output
- Metrics — latency, error rate, tokens used, cost per run, hallucination and drift scores
- Logs — per-step contextual data including prompts, tool responses, and decision rationale
- Events — significant state transitions like tool failures, escalations, and agent handoffs
- OpenTelemetry (OTel) integration — the vendor-neutral standard for emitting all of the above
How it works
- The agent is instrumented with hooks that emit telemetry events at every meaningful step — tool call start and end, LLM call, state change, decision branch
- Events stream to an observability backend (Arize, LangSmith, Salesforce Agent Observability, Datadog, etc.) in real time
- Dashboards aggregate traces, metrics, and anomaly detection across every agent run
- Operators set alerts on metrics (cost spike, drift detected, error rate climb) and drill into individual traces when something goes wrong
Good to know
For Agentforce customers, Salesforce's native Agent Observability (part of the Agentforce platform) covers most of this out of the box — traces, metrics, and audit trails surface directly in the Agentforce console, so you don't need to integrate a third-party vendor for basic observability. A common trap: teams confuse telemetry (the data) with observability (the practice of using that data). You need both — emit the data, and build the review rhythm into how your team runs the agent in production.
Related terms
A2A (Agent-to-Agent Protocol)
Google's open protocol that allows AI agents from different vendors to communicate and collaborate with each other.
Agentforce
Salesforce's AI agent platform that enables businesses to build, customize, and deploy autonomous AI agents across sales, service, marketing, and commerce.
MCP (Model Context Protocol)
Anthropic's open standard for connecting AI models to external data sources and tools. Think of it as a universal adapter for AI.
AI Agent
An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals - without constant human direction.