Enterprise teams are often told that agents are the next step after retrieval. That shortcut creates expensive systems that are difficult to test, govern, and operate. This session offers a practical decision framework for choosing the simplest architecture that can safely deliver the outcome.

We will compare three patterns through the same enterprise workflow: retrieval when the task is primarily about finding trustworthy context; deterministic workflows when rules, approvals, and integrations should control the path; and tool-using agents when the work genuinely requires judgment across changing context. For each pattern, we will examine failure modes, operational signals, evaluation, human review, and the point at which multi-agent coordination creates more cost and risk than value.

The goal is not to make every workflow agentic. It is to make architecture choices explicit, observable, and reversible. Attendees will leave with a decision framework they can use in design reviews, plus practical questions for data boundaries, tool permissions, evaluation, observability, retries, and escalation before an AI system reaches production.