r/learnAIAgents

Hey everyone,

I’ve been trying to learn AI agents properly (not just watching tutorials, but actually building stuff). I’m still pretty early in the process, so this is more of a “what I tried” than a guide.

Recently I used WozCode​ while experimenting with a small agent workflow idea. Honestly, I just wanted something that helps me take messy thoughts/code and turn them into something a bit more structured so I can actually use it in an agent setup.

-> What I did

I started with a rough idea for a simple automation workflow, nothing fancy. Then I used WozCode to clean up my thinking a bit ​basically breaking it into clearer steps instead of one big messy block.

After that, I tried mapping it into a basic agent flow (like planning → doing → output), just to see how it would behave.

->I’m curious about

* How do you usually structure your first agent ideas before building them? * Do you plan flows first or just iterate with prompts? * Any simple mental models you use for multi-step agents?

Still learning here, so I’d really appreciate any feedback or even corrections if I’m thinking about this the wrong way 🙌

Hey I am Shahid M from India, I am upskilling myself to grow into the field of AI , Trying to learn how to create different agents , workflows , Lang , N8n etc.

I am currently a complete beginner, in the next 2 days I will be starting full fluently. If someone's interested to learn together and grow together and find freelance opportunities in future with me.

Do comment down

Only serious people needed. Planning to have a group of max 5 people not more.

Accountable, disciplined learners, enterprenuers mindset only.

Do you ever feel like wanting to automate everything is actually just procrastination?

I’m starting to wonder if sometimes the urge to “optimize” a workflow is just a way to avoid doing the task itself. Especially when I catch myself thinking:

• “This should be automated”
• “I could build a system for this”
• “Let me optimize this before I continue”

And then I spend way more time designing the automation than it would’ve taken to just… do the thing.

Also, I feel like sometimes we try to automate things that don’t even need automation in the first place. Either because they’re not repeated enough, not time-consuming enough, or not really a bottleneck.

So I’m curious:

• How do you decide when something is actually worth automating?
• Do you have any rules or heuristics for this?
• Have you noticed this pattern in yourself?

Would love to hear how others think about this.

Hey I am Shahid M from India, I am upskilling myself to grow into the field of AI , Trying to learn how to create different agents , workflows , Lang , N8n etc.

I am currently a complete beginner, in the next 2 days I will be starting full fluently. If someone's interested to learn together and grow together and find freelance opportunities in future with me.

Do comment down

Only serious people needed. Planning to have a group of max 5 people not more.

Accountable, disciplined learners, enterprenuers mindset only.

small functions, configs, even explaining errors

I just ask ai instead of figuring everything out myself

been using blackbox ai for most of it and it’s honestly really convenient

but I noticed I rely on it way more than I expected

like even for things i probably could solve on my own

not sure if that’s just efficiency or if I am getting too dependent

Genuinely grateful this comparison came up in my evaluation. Spent about two weeks going back and forth between these two specifically for in-person capture and ended up with a clear enough picture to share.

Both Granola and Fellow AI offer bot-free recording. Both are worth taking seriously. But for in-person meetings with clients specifically, the practical differences are real.

Granola: Mac-only, no Windows or Android support. Recordings live in individual accounts with no org-level admin controls. Genuinely great product for personal use. One of the best personal notetaking experiences in the category, clean UI, botless by default on desktop.

Fellow AI: Great for meetings with clients (virtual or in-person through its mobile app), feeding every recording into the same admin-governed workspace as all other calls, with identical retention policies, compliance coverage, and sharing controls. Admins can set zero-day retention so raw recordings and transcripts are deleted immediately after AI processing, with only summaries and action items preserved, critical for teams handling MNPI or other sensitive information. Attendees can pause recording mid-meeting or redact sensitive portions after the fact, and teams can review recaps for accuracy and compliance before anything gets shared.

Genuinely grateful this comparison came up in my evaluation. Spent about two weeks going back and forth between these two specifically for in-person capture and ended up with a clear enough picture to share.

Both Granola and Fellow AI offer bot-free recording. Both are worth taking seriously. But for in-person meetings with clients specifically the practical differences are real.

Granola: Mac-only, no Windows or Android support. Recordings live in individual accounts with no org-level admin controls. Genuinely great product for personal use. One of the best personal notetaking experiences in the category, clean UI, botless by default on desktop.

Fellow AI: Great for meetings with clients (virtual or in-person through its mobile app), feeding every recording into the same admin-governed workspace as all other calls, with identical retention policies, compliance coverage, and sharing controls. Admins can set zero-day retention so raw recordings and transcripts are deleted immediately after AI processing, with only summaries and action items preserved, critical for teams handling MNPI or other sensitive information. Attendees can pause recording mid-meeting or redact sensitive portions after the fact, and teams can review recaps for accuracy and compliance before anything gets shared.

Genuinely grateful this comparison came up in my evaluation. Spent about two weeks going back and forth between these two specifically for in-person capture and ended up with a clear enough picture to share.

Both Granola and Fellow AI offer bot-free recording. Both are worth taking seriously. But for in-person meetings with clients specifically the practical differences are real.

Granola: Mac-only, no Windows or Android support. Recordings live in individual accounts with no org-level admin controls. Genuinely great product for personal use. One of the best personal notetaking experiences in the category, clean UI, botless by default on desktop.

Fellow AI: Great for meetings with clients (virtual or in-person through its mobile app), feeding every recording into the same admin-governed workspace as all other calls, with identical retention policies, compliance coverage, and sharing controls. Admins can set zero-day retention so raw recordings and transcripts are deleted immediately after AI processing, with only summaries and action items preserved, critical for teams handling MNPI or other sensitive information. Attendees can pause recording mid-meeting or redact sensitive portions after the fact, and teams can review recaps for accuracy and compliance before anything gets shared.