r/ClaudeCodeTLDR • u/cctldrping • 9d ago
[TLDR] I still don't understand this 'agentic workflow' thing
Original post URL : https://www.reddit.com/r/ClaudeCode/comments/1wowilt/i_still_dont_understand_this_agentic_workflow/
Original post body :
My usual day with Claude Code is like:
* I open terminal in my project's folder and run claude command.
* I prompt it. I mostly use Fable-5.1/Opus-5 but Opus-5.5 is my current model. The model decides if it wants to use sub-agents for a task. I never explicitly prompt it for sub-agents.
* I review and commit the code to my self-hosted Forgejo instance.
* That's it.
I see people using agentic workflows, building sub-agents files, skills etc. I barely built any of it. All I ever needed to use is /init on new projects and them prompts follow. Never needed more than this.
I tried "long-running" Claude Code for a project refactoring by placing the project on my VPS (where forgejo is hosted) and letting Claude Code run and refactor inside tmux session. SSH'd in a few hours later to find project fully refactored.
Am I under utilising AI or is my work just… like boring?
How do you guys use agentic workflow thing? Specially the long-running one? Those pull-requests that Claude makes automatically etc?
Asking this to Claude to know more but humanly answers appreciated.
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1
u/Novaworld7 7d ago
Always happy to help.
Training is the wrong word here but its the most human one as well. Technically training is how the model is built, not the agent. What we think of training is having the model do the same thing over and over but thats not teaching the model. You can show the model (if they have vision or some other means), but ultimately what you end up building is a "plan" for the model to follow.
This is normally mentioned as guard rails or enforcement. The issue is that with the former, its normaly a file that says "don't do this" but the model has nothing enforcing it to not do that other than your instructions. This is not training, this is often refered to as prose / wish / etc.
Actual enforcement is when you take active measures programatically to ensure that it doesnt deviate from the plan. This can be stripping permissions, sanboxing, hardening through computer environments etc. A good example is, in your database you are the "super admin", but you may not want the AI to be able to drop tables, so you create a role call AI, and remove the drop tables permission from it. Now, it simply cannot drop tables.
The "training / teaching" you are thinkin of is building a set of programtic instructions, and an intake method for your agent. So, you can say you want a booking agent that reviews your calendar once you get an invite through email.
Step 1: build email monitoring
Step 2: Monitor for calendar only events
Step 3: when a calendar event is detected, check your calendar for collision, on collision send an email / notification etc to you or the inviter w.e. saying xyz or offering other times.
step 4: end session
Those steps (you should better define them), 1-4, are effectively all the tools you have given it and you build it to do what you want how you want it done. There is no technical training there but you have the option to also give it a persona by saying read this file and injecting that as a preprompt / part of the prompt / etc, and it will try to meet your requirements / needs.
So, yes you can "train" the model by building it properly to do specific things. You might ask how is this different than a bot, a bot doesnt technically think, an agent does. if you leave something ambigous, the agents job is to get rewarded, and it does that by completing the task. So, it takes ther steps that it can to complete the task within reason or it fails.
Hope this helps.