"Agentic AI has become a hot topic amongst software developers in recent months. As usage of LLMs has become increasingly popular, many developers are switching to agentic AI services to build projects. One point of contention with agentic AI is that productivity is always limited without proper context. Solid context is crucial in streamlining agentic AI tools, as it guards against things like hallucinations and inefficiencies with software development."
"Having a working specification that you use with agentic AI helps even more, as it provides a structured approach to building your project. With agentic AI tools, you can both generate a specification and then incrementally work through that specification on projects. For the purposes of this post, I'll be referring to this process of first creating and then using a specification as a "spec-first" workflow."
Agentic AI adoption has increased among software developers as LLM usage grows. Productivity with agentic AI is limited when proper context is missing. Solid context helps prevent hallucinations and inefficiencies in software development. A working specification supplies structure and improves the effectiveness of agentic AI tools. A spec-first workflow involves creating a specification first and then incrementally using it to guide agentic AI through project tasks. AWS Kiro and GitHub Spec Kit provide tools that support building and applying specifications. Claude Code, Copilot, and similar tools can implement the spec-first approach.
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