What it is
Agent Optimizer is a Salesforce agent that builds and refines your other Agentforce agents. In Agentforce Builder, you describe what an agent should do in plain language and Optimizer creates subagents and stubs out actions, testing as it builds. In Agentforce Observability, it reads production sessions, finds recurring failure patterns and ranks them by impact.
Why it matters
Agents rarely fail in one obvious place. Grouping repeated failures and ranking them by impact tells a team what to fix first, and lets them build, test and stage the change in one conversation.
Key components
- Plain-language agent building in Agentforce Builder
- Failure-pattern analysis in Agentforce Observability
- Impact ranking
- Build, test and stage changes conversationally
How it connects
It connects agent building with agent observability, so production behavior feeds directly back into the next version of the agent.
Good to know
Announced at Dreamforce 2026 with general availability in October 2026. Human review of staged changes still matters before they go live.
Frequently asked questions
What does Agentforce Agent Optimizer do?
It helps build new Agentforce agents from a plain-language description, and analyzes production sessions to find recurring failures, rank them by impact, and stage fixes.
Related terms
Agent Evaluations
Structured testing that scores an AI agent's answers for accuracy, tone, and safety before and after it goes live.
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.
Agentforce
Salesforce's AI agent platform that enables businesses to build, customize, and deploy autonomous AI agents across sales, service, marketing, and commerce.
Agentforce Testing Center
Salesforce's built-in tool for testing Agentforce agents at scale against generated and real test cases before they go live.
AgentOps
Shorthand for the practice of running AI agents in production — borrowed from "DevOps" and "MLOps" — encompassing observability, cost attribution, evaluation, and the operational discipline of managing agents at scale. Often used interchangeably with "agent operations."
Agent Optimizer in practice
Articles from the Funnelists Team that put this term to work.
