Agentic Workflow Patterns: Plan-Execute-Observe Loops, ReAct, and Task Decomposition

Agentic Workflow Patterns#

An agent without a workflow pattern is a chatbot. What separates an agent from a single-turn LLM call is the loop: observe the environment, reason about what to do, act, observe the result, and decide whether to continue. The loop structure determines everything – how the agent plans, how it recovers from errors, when it stops, and whether it can handle tasks that take minutes or hours.

Prompt Engineering for Infrastructure Operations: Templates, Safety, and Structured Reasoning

Prompt Engineering for Infrastructure Operations#

Infrastructure prompts differ from general-purpose prompts in one critical way: the output often drives real actions on real systems. A hallucinated filename in a creative writing task is harmless. A hallucinated resource name in a Kubernetes delete command causes an outage. Every prompt pattern here is designed with that asymmetry in mind – prioritizing correctness and safety over cleverness.

Structured Output for Infrastructure Data#

Infrastructure operations produce structured data: IP addresses, resource names, status codes, configuration values. Free-form text responses create parsing fragility. Force structured output from the start.