Salesforce’s Agentforce platform now powers AI agents for more than 20,000 enterprise customers, handling over three million support conversations. The system combines a four‑layer architecture—engagement, agent, system of work, and context—plus a cross‑cutting trust layer that supports multiple LLM providers. By exposing a machine‑readable auth.md file, developers let agents authenticate to OAuth‑protected services without custom code in production today easily.

John Kucera, CPO of Agentforce, stresses that 90 % of work occurs after launch. Traditional software front‑loads development, but AI agents require continuous monitoring, transcript analysis, and prompt engineering to tame nondeterministic outputs. Teams that treat deployment as a starting line invest in pre‑launch scaffolding, define narrow high‑value use cases, and allocate resources for ongoing iteration across their entire organization now.

Agentforce responds with features like Agent Script and Hybrid Reasoning, which embed deterministic workflows beneath probabilistic LLM reasoning. The platform measures output through Agentic Work Units, giving product teams a quantifiable signal of completed tasks rather than raw interaction counts. Salesforce’s experience shows that disciplined post‑launch processes, not fancy models, deliver reliable enterprise AI for large customers today globally today.