Author(s): Raj kumar

Originally published on Towards AI.

Most AI agent projects are using one workflow pattern for problems that need a different one. Here is how to fix that.

A few months ago, a team shared a demo with me. They had built a multi-agent system with an orchestrator, three specialized sub-agents, and a memory layer. It ran their customer support ticket classification.

The article explains that agentic AI often gets over-engineered by applying the most complex workflow pattern when a simpler one would work better. After clarifying what “agentic AI workflow” means (LLMs involved in structured/semi-structured processes across multiple steps), it walks through eight core workflow patterns—single-shot, chaining, routing, orchestrator, evaluator, tool use, parallelism, and autonomous—and for each one provides when it fits, a concrete example, and common mistakes to avoid. It then gives a practical decision guide for choosing patterns based on the shape of the task, emphasizing that patterns are composable in real production systems (e.g., routing into chains, parallel steps inside chains, tool calls along the way, and evaluation before final output). The takeaway is to start with the simplest pattern that truly solves the problem, add complexity only when required, and avoid defaulting to autonomous agents or elaborate orchestrations for tasks where reliability, cost, latency, or correctness demands can be met with simpler architectures.

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Published via Towards AI