Workflows vs. Agents: Choosing the Right Architecture for Agentic Systems
When building with LLMs, the choice between a predefined workflow and an autonomous agent depends on your requirement for predictability versus flexibility. Learn when to use each approach and how to mitigate the risks of agentic systems.
Workflows (Orchestrated Paths)
In a workflow, the system follows code-defined logic. This is ideal for production environments where reliability and latency are priorities. Common patterns include Prompt Chaining, Routing, Parallelization, Orchestrator-Worker, and Evaluator-Optimizer.
Agents (Dynamic Autonomy)
Agents are used when the steps cannot be hardcoded. They utilize feedback loops to observe results and adjust their actions dynamically. While highly flexible and capable of solving open-ended problems, they require robust monitoring and sandboxing.
Risks and Mitigation
Autonomy introduces variables that are difficult to manage at scale: unpredictable paths/outputs and compounding costs. Mitigation strategies include implementing visibility tools (Monitor) and strict boundaries (Guardrails).
Key Takeaway: Agents are powerful for open-ended, complex problems. But in many cases, a well-designed workflow is more reliable and more cost-efficient.