The way we build &amp; deploy software is changing with AI agents &amp; workflows, the Agentic Engineering way. Developers who understand how to use AI have an added advantage today. Well, already many of you might already be using many AI tools, AI agents and workflows to automate your SDLC. That’s a good move.
What I have noticed is, many developers use AI tools to code but you need to go beyond that. See how you can automate the whole SDLC and cut off the manual hands-offs between different SDLC phases. That’s what the goal should be.
One more thing, many of we ignore context when it comes to AI SDLC (Using AI to automate SDLC), but that will definitely backfire you big time. Without context, the AI tools &amp; Agents act hallucinated and make huge mistakes (like deleting a prod database, pinging the wrong team on slack, etc).
Make sure you have a proper Context Layer in place when approaching AI driven SDLC.
I found this article really interesting as it talks about the components of a coding agent: <a href="https://magazine.sebastianraschka.com/p/components-of-a-coding-agent" target="_blank" rel="noopener nofollow">https://magazine.sebastianraschka.com/p/components-of-a-coding-agent</a>

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Pavan Belagatti

AI agents and workflows are reshaping the software development lifecycle (SDLC). Developers should move beyond using AI just for coding and look to automate entire SDLC phases, eliminating manual handoffs. A critical but often overlooked element is maintaining a proper Context Layer — without it, AI agents can hallucinate and cause serious errors like deleting production databases or notifying wrong teams. Understanding coding agent architecture is essential for developers embracing agentic engineering.

Coding Agent Architecture Every Developer Should Know!

This is the part people miss: automation without context can make mistakes faster.

I’d put prompt-cache hit rate on the agent dashboard beside context size.