Your Agent Is Live. Now What?

Why It Matters

Without an operational framework, AI agents can silently degrade, driving up support costs and eroding customer trust. Implementing the ADLC and dedicated roles turns a risky prototype into a sustainable, revenue‑protecting asset.

Key Takeaways

• AI agents drift; operational oversight prevents costly errors.
• Assign an Agent Operations Engineer like an SRE for real‑time health.
• Implement weekly quality reviews using conversation logs to catch drift.
• Define data freshness SLOs; stale data leads to incorrect responses.
• Set hard SLOs: >70% self‑service, <15% escalation, token cost limits.

Pulse Analysis

The excitement of deploying an AI‑driven agent often eclipses the reality that these systems are probabilistic, not deterministic. Traditional software behaves predictably, but agents make decisions based on model reasoning and data context, creating a "stochastic contract" that can shift over time. When that contract drifts—such as referencing outdated policy documents—the agent may appear functional while delivering misleading outcomes. This inherent uncertainty forces organizations to adopt a continuous development mindset, embodied in Salesforce’s Agent Development Lifecycle (ADLC), which blends ongoing testing, observability, and rapid iteration.

Operationalizing agents demands new roles and processes. An Agent Operations Engineer, akin to a Site Reliability Engineer, monitors health metrics, escalations, and token usage in real time. Weekly quality reviews of sampled conversation logs surface hidden reasoning shifts before customers notice. Data freshness SLOs ensure the knowledge base reflects the latest information, while conditional logic in the Agentforce Builder provides granular off‑switches for problematic actions without full redeploys. Robust testing centers and sandbox environments act as regression harnesses for prompt changes, safeguarding against unintended behavior.

For enterprises, the stakes are high. Undetected drift can inflate support tickets, breach compliance, and waste compute resources through token‑heavy loops. By defining concrete SLOs—self‑service resolution above 70%, escalation below 15%, and strict token budgets—companies translate AI performance into measurable business outcomes. The ADLC transforms a flashy launch into a disciplined, revenue‑protecting capability, positioning AI agents as reliable front‑line assistants rather than experimental curiosities.