From Prompts to Production: A Playbook for Agentic Development

"Large language models (LLMs) generate text from a learned probability distribution. On their own, they do not have the ability to take real-world actions. Agentic AI is the layer that wraps an LLM in an iterative process of improvement involving reflection, tool use, planning, and multiagent collaboration. It is the bridge between the core technology of LLMs (such as GPT-5, Claude Opus 4.1, etc.) and real-world business value."

"Agentic system development need not be ad-hoc. Reusable patterns exist that address common agentic challenges, such as Supervisor Pattern, ReAct Agents, and Human-in-the-Loop. We need a way to integrate these in the regular development process without restricting developer creativity. Prompts, tool manifests, policy configurations, memory schemas, and evaluation datasets require versioning and systematic Infrastructure-as-Code treatment. We need a way to reduce prompt-related production failures and to use version control, semantic diffing, and formal change approval processes."

Large language models generate text from learned probability distributions and lack innate ability to take real-world actions. Agentic AI wraps LLMs in iterative processes of reflection, tool use, planning, and multiagent collaboration to enable real-world value. Enterprises must learn how to develop, ship, secure, and operate agentic applications in production. An agentic software development life cycle (ASDLC) is necessary to specify both required behaviors and explicit prohibitions. Reusable patterns—Supervisor Pattern, ReAct Agents, and Human-in-the-Loop—can standardize development without stifling creativity. Prompts, tool manifests, policy configurations, memory schemas, and evaluation datasets require versioning and Infrastructure-as-Code treatment. Behavioral quality assurance and vendor-neutral standards like the Model Context Protocol improve integration and maintainability.

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