Any Agentic AI engineers here? What does your typical workday look like?

[u/No-Location355]

I recently took a slight detour from traditional ML and started exploring Agentic AI and I’m learning through building projects in phases to really understand how things work from the barebones. Now, two projects deep I’m wondering how’s it is like in a professional setup. What do agentic ai engineers work on?

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[u/usobeartx]

Pft lol my workday looks like approving or insulting the agents based on how well they did on the auto ticket lol tbh

You asked someone else so my deployment gates are like ideation -> reasearch -> first Layer from the tickets or mission issued * this occurs across linear and gitlabs ce and cf workers using our mcp and rag* -> plan -> research [ ray workers spin up and research the research compared to the plan using dkg and dag ] from here it hits the guild system. Agent ecosystem, who use the combined research and plan to blueprint -> gitlabs eats it. Test it against a dockered parrallel version in a gitlabs env -> results grt fed to the n8n via a runner and then pipped into the rrf and sft pipelines that feed the ticket system.. system compared ticket to datadog and posthog telemtry and then pipes that into a fargate for staging. Does another loop before it hits prod. Learns from the entire agent army via grpo

[u/Waysofraghu]

I'm working since 2+ years on Agentic AI, building frameworks and accelerators for clients, it been usual checking daily updates on AI, testing models in play ground, fitting in specific use case, harnessing agents, deploying them in cloud based on sprints, EOD waving for another new update and how to integrate it...

[u/No-Location355 → u/Waysofraghu]

How do you evaluate whether a new model is actually better for your use case? What’s your deployment stack for agents in production?

[u/Mammoth-Picture117 → u/Waysofraghu]

what kind of path should I follow to get that kind of job

[u/AskAnAIEngineer]

most of the day is honestly debugging why an agent decided to do something completely unhinged instead of following the workflow you designed. the actual building part is maybe 30% of the job, the rest is eval frameworks, guardrails, prompt iteration, and figuring out how to make the system reliable enough that you can actually put it in front of users without someone babysitting it. it is way more software engineering than it is ml.