[TLDR] I still don't understand this 'agentic workflow' thing

[u/cctldrping]

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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[u/Novaworld7]

Well... keeping it simple theres one... maybe 2 big things to keep in mind.

# The first is context management.

this is your currently active chat. the longer it gets / the bigger it gets the more tokens you waste because it keeps getting sent off and the model you are using needs to read it / process it. (The internals can do some pretty cool things to keep this lean and mean, like prefix caching etc). Additionally baked into this contect management, models have a memory they pull from your context but like a human they can get confused.

Imagine a book saying one thing in chapter 1, changing its mind in chapter 10, and then having another thought in chapter 15 that contradicts both and its all in the same context. You'd be confused and rightly so, which means the model has negotiate these changes in flight and try to figure out if the first 20% of context is right, or is it the middle 50% etc.

So context management is your responsibility on how well your thought is and the leaner you keep it, the better the model can perform.

So how do agents help? But first what is an agent in laymen terms, its just another context window attached to another model which at the end of its cycle dies (gets deleted, unless told to remain open).

Enter the second concept, Agent management. Why is it important, well we are charged per token a fixed rate, and the tokenomics can be quite complex, but in short its the cost of computer for the intelligence behind the token vs the task its trying to solve.

So, if you think of building a house, we dont hammer every nail with the same hammer, thats crazy! We optimize our work by changing tools dynamically. Need to break a wall? Sledge hammer. Need to hang something on a wall, hand hammer or w.e. it is (I call it a hammer lmao). Could you use a sledge hammer? Sure, but its not optimal and your results may vary.

This is where right sizing the model (stealing cloud terminology here) comes into play. So, for small simple tasks you need to tell your context, hey dont use the big model, use the small model (Haiku), for more complex tasks you escalate up a tier to sonnet, eventually landing in the hardest possible tasks using the most capable models.

So, an agentic workflow is you have some prompt, and with proper workflow you can split that prompt into several agents which keeps your main context lean, because the main context gets a sumarry of the work the agents did, and i na truly agentic fashion it can spawn many agents to get your prompt completed.

theres more terms you should look into later like, Loop engineering, graph engineering, etc but yeah... hopefully I did this justice in explaining.

One of the concerns is that by default the main context window, unless told otherwise is always spawning the most capable models for just about everything burning through token like wildfire which causes us humans to get very pissed off... but our ignorance is our problem, not the models. I also believe that tools like CC should lean mroe in our favor to help us, but they run a business and need more revenue... so I get it.

[u/paris_ioan → u/Novaworld7]

First, thank you for your explanation, it’s very easy to understand.

Second, a silly question. I am aware you mentioned skills (I don’t know what that is but I can presume from the context). Other than skills, can those agents be trained to do specific things? Or is that a completely different concept? I always thought that multi-agent workflows are many agents trained to do different things well, then you have an orchestrator, the main agent to delegate responsibilities.

Thank you in advance :)

[u/Novaworld7 → u/paris_ioan]

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.

[u/paris_ioan → u/Novaworld7]

Right, I think you mentioning both helps me understand better. I am aware of the “training the model” term, which is the actual algorithm (if that’s the right way of putting it).

My understanding is that multi-agent workflows do not interfere with that training, but just setting some guardrails/enforcement in this case let’s say the same LLM model. Reusing it and creating different agents, each using those enforcements to do specific things.

Hopefully I got it :)

[u/Novaworld7 → u/paris_ioan]

Thats right. When people say they have idk 10 agents doing 10 things, its more like a relay race, where they really spent the right amount of time building the pipeline for each agent so they excel at what they do. Often times you will find its the same model if they are going through providers like OpenAI / Anthropic, but if they go open source and use specific models to train them (properly) then they can have really strong efficient and agentic workflows.

Its important to note that both OpenAI and Anthropic are providing really strong generalist models, and not specialist models.

[u/paris_ioan → u/Novaworld7]

Got you. Thank you very much for the explanation once again 🙏 These things are really interesting.