What it is
Agent observability is the end-to-end capability of knowing what your agents are doing in production: reviewing the full trace of every run, alerting on anomalies like cost spikes or drift, evaluating output quality over time, and drilling from a bad answer all the way back to the specific tool call that caused it. Tools in this space include Arize, LangSmith, Datadog LLM Observability, Salesforce Agent Observability (native to Agentforce), and OpenTelemetry-based stacks that emit to any compatible backend.
Why it matters
Telemetry is the raw data; observability is the discipline and tooling that turns that data into decisions. Without observability, agents running in production drift, break silently, and burn tokens you can't explain — and when a customer complains, you have no way to trace the specific run that went wrong. With it, your team ships agents with confidence: every run is auditable, cost is predictable per workflow, quality regressions are caught before customers see them, and compliance auditors get the evidence trail they need.
Key components
- Trace review — walking the full step-by-step chain of a single agent run to see exactly what happened
- Metric dashboards — aggregate latency, cost, token usage, error rate, hallucination score across many runs
- Alerting — automated warnings when metrics cross thresholds (cost spike, drift detected, error rate climb)
- Evaluation — systematic grading of agent outputs against expected answers or quality rubrics
- Tool ecosystem — Arize, LangSmith, Datadog, Salesforce Agent Observability, or custom OpenTelemetry stacks
How it works
- Agents emit telemetry data at every meaningful step (tool calls, LLM calls, decisions, state transitions)
- An observability backend ingests that data and organizes it into traces, metrics, logs, and events (MELT)
- Dashboards aggregate across runs for trend analysis; trace views zoom into individual runs for debugging
- Alerts trigger when metrics cross operator-defined thresholds, pointing responders to the offending traces
- Evaluation pipelines score outputs against expected behavior, catching drift and regression early
Good to know
For Agentforce customers, Salesforce's native Agent Observability covers the core observability workflow — traces, metrics, and audit trails surface directly in the Agentforce console. Teams operating outside Agentforce typically pair OpenTelemetry instrumentation with a vendor like Arize or LangSmith. A common trap: buying an observability tool without building the review rhythm into how your team actually runs the agent day-to-day. The tool without the habit delivers little value.
Related terms
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.
Agent Telemetry
The runtime data emitted by an AI agent — every decision, tool call, input, output, latency, and cost — used to monitor reliability, quality, and spend in production.