Conclusion

LLMs provide the cognitive core of modern software agents, but raw model invocation is not enough to achieve purposeful, robust, and controllable intelligence. To move from output generation to structured reasoning and goal-aligned behavior, LLMs must be embedded in intentional workflow patterns that define how models process inputs, manage contexts, and coordinate actions.

LLM workflows introduce foundations to build an agent's cognitive module:

• Prompt chaining breaks down complex reasoning into modular, auditable steps.

• Routing enables intelligent task classification and targeted delegation.

• Parallelization accelerates throughput and promotes diverse reasoning.

• Agent orchestration structures multi-agent collaboration through task decomposition and role-based execution.

• Evaluator (reflect-refine loop) enables self-improvement, quality control, and alignment checking.

Each workflow represents a composable pattern that can be adapted to the agent's needs, the complexity of the task, and a user's expectations. These workflows are not mutually exclusive. They are building blocks that are often combined into hybrid architectures that support dynamic reasoning, multi-agent coordination, and enterprise-grade reliability.

As you transition to the next chapter on agentic workflow patterns, these LLM workflows will reappear as embedded structures within larger systems, supporting goal delegation, tool orchestration, decision loops, and lifecycle autonomy. Mastering these LLM workflows is essential to designing software agents that don't just predict text but reason, adapt, and act purposefully.