A support team wants Claude to process every inbound email the same way: classify the request type, look up the customer account, and draft a reply…

According to Anthropic's architectural guidelines for LLMs, when a task consists of well-defined, predictable, and unvarying steps, a fixed workflow (such as a prompt chain) is the most appropriate pattern. Workflows offer higher reliability, predictability, and easier debugging compared to autonomous agents. Agents are better suited for tasks requiring dynamic decision-making and flexibility. Since the email processing steps are identical for every email and never vary, orchestrating discrete, sequenced LLM calls ensures consistent execution without the overhead, latency, or unpredictability of an autonomous agent planning its own steps.

A. Autonomous agents are designed for dynamic tasks requiring runtime planning, which introduces unnecessary unpredictability and latency for a fixed, unvarying process.

B. A multi-agent system adds unnecessary architectural complexity and token overhead for a simple, linear sequence of identical steps.

D. Allowing runtime decision-making violates the strict requirement that the steps are identical and never vary, risking inconsistent execution.

1. Anthropic, "Building effective agents" (2024), Section: "Workflows vs. Agents" and "Workflow: Prompt Chaining".

2. Anthropic Documentation, "Architectural patterns for AI products", Section: Predictability vs. Flexibility.