What it is
An agent SLA is a written commitment for how an AI agent must perform, covering accuracy, response time, escalation to a human, and uptime. It works like a traditional customer service SLA applied to digital labor, so teams can hold an agent to the same standard they would a human rep.
Why it matters
Without clear benchmarks, it's hard to know if an agent is actually performing or quietly eroding customer trust. SLAs give mid-market teams a concrete way to justify continued investment or pull an agent back for retraining.
Key components
- accuracy threshold
- latency target
- escalation rate
- review cadence
How it connects
Agentforce Testing Center and agent telemetry data are commonly used to measure and report against Agent SLA targets.
Good to know
Set SLAs before launch, not after — retrofitting benchmarks onto a live agent is much harder to enforce.
Related terms
Agent Observability
The practice of inspecting, debugging, and understanding AI agent behavior at runtime by consuming agent telemetry — traces, metrics, logs, and events — through dashboards, alerts, and evaluation tools.
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.
Agentforce Testing Center
Salesforce's built-in tool for testing Agentforce agents at scale against generated and real test cases before they go live.
Cost Per Conversation
The board-friendly ROI metric: total AI spend—tokens, compute, tool calls—divided by resolved conversations, so you know if Agentforce is actually paying off.
