r/Agentic_Marketing

Ok, so first, it's not what it sounds like.

I've lately been diving into certain conspiracy theories, i.e. ice wall, world hierarchy, religion, secret societies, the type of shit that'll get you called crazy at the family reunion.

And I use chat GPT for questions bc I can't talk to Google live a human, but I'm tired of the guidelines on like what they can say and shit.

If anyone knows a really good, completely unregulated ai GPT that would literally admit to me that ai is going to be the downfall of society and all that without guidelines stopping it. And it doesn't need to have image generation, as I'm not trying to generate pornographic images, I can just tell chat GPT I have a class assignment, generate a chart with these points or something like that.

Now I'm not in the best financial stance, or to be honest, a financial stance whatsoever, so I'm looking for preferably free, but I understand something like this would probably cost, so even still, I'm taking suggestions.

Whether it's research, coding, content creation, customer support, data analysis, or something entirely different, which AI powered workflow has had the biggest impact on your productivity?

What changed after automating it, and would you ever switch back to the manual process?

Interested in hearing real world examples from the community.

Hey everyone,
Just saw this meme and it hit way too close to home (check the pic in the reply).

───────────────────────────────   
Let's be real: with AI doing the heavy lifting, coding has become the easy part. The barrier to entry for building an app or a full-on Agent business is practically zero now.
But this brings us to the actual million-dollar questions that are keeping me up at night:

1. IDEA (What to build): When anyone can build anything in a weekend, how do you find a problem that’s actually worth solving? How do you avoid building just another generic wrapper?
2. USAGE (How to sell): The market is flooded. Distribution is the new bottleneck. How the heck are you guys getting eyeballs and actually getting people to use (and pay for) your products?

For those of you trying to bootstrap a business or build cool stuff in this new "Agent Era," what’s your game plan? Are you hyper-focusing on niche industries, relying on personal branding, or something else entirely?

Curious how people here deal with this: when you’ve got several agents working together (a researcher, a writer, a critic, whatever the setup), how do you manage their shared token spend? In my experience most setups just give each agent a flat cap, which means an important agent can run dry mid-task while a less important one right next to it has budget sitting unused.
I ended up building a small open-source library to deal with this on my own projects — Token Budget Contracts (TBC). Each agent gets a priority and a budget; when one runs low, spare tokens automatically flow in from lower-priority or idle agents, never the reverse, and never below a protected reserve. It also stops an agent from spending further once it’s already confident in its result.

from tbcontracts import BudgetManager

manager = BudgetManager()
manager.register_agent("researcher", priority=3, max_tokens=4000)
manager.register_agent("critic", priority=1, max_tokens=2000)

@manager.govern("researcher")
def call_researcher(prompt):
return my_llm_call(prompt)

pip install token-budget-contracts — MIT licensed, 23 tests, no dependencies. Works with any Python-based agent setup (LangGraph, CrewAI, AutoGen, or raw API calls).

Mostly want to know: is this a real pain point for others, or is everyone already solving it some other way (rate limits, observability tools, just eating the cost)? Genuinely curious what people are doing here.

I keep feeling like “on-brand AI content” is still kinda broken.

Some models go way too hard and everything turns into a caricature of the brand voice.
Others play it so safe that it’s technically fine, but completely forgettable.

Feels like we don’t have a good middle ground yet.

I’ve been spending a lot of time on this (we’re building in this space), and a couple things have helped a bit:

• Chaining models together Instead of relying on one model, we’ll start with a solid draft, pass it through something more creative, then have another model critique it, and finally clean it up. It’s messy, but it works better than any single model.
• Forcing it to use real brand stuff Big difference when you give it actual past campaigns, brand guidelines, assets, etc. vs asking it to “sound like a brand.” Otherwise it just makes things up.

Even then… it still feels inconsistent.

Curious if anyone here has cracked this (or at least gotten closer).

How are you keeping things on-brand without everything sounding generic or overprocessed?

Hey everyone,

I'm working on a project called KitAI. The goal is to build an AI assistant completely from scratch instead of fine-tuning an existing model.

Before anything else: this is not a job posting, and I'm not hiring. This is just a personal project I'm building for fun, learning, and experimentation. I'm looking for people who enjoy AI and might want to share ideas, advice, or contribute because they find the project interesting.

I'm not trying to compete with Claude or GPT overnight. I know that's unrealistic for a small project. My plan is to start tiny, learn as I go, and gradually improve it over time.

Right now I'm looking for people interested in:

• Machine learning
• Transformers and LLMs
• Training small models
• Datasets and tokenizers
• Python and PyTorch
• AI infrastructure

Even if you're a beginner, I'd love to hear your ideas or suggestions.

A few things about the project:

• The name is KitAI (inspired by cats 🐱)
• The first version will be a very small model
• The goal is to learn and build something cool from the ground up
• I'm interested in experimenting with custom training, tokenizers, memory systems, and other AI components

If you'd like to discuss ideas, contribute, share resources, or just follow the project's progress, feel free to comment or send me a message.

Thanks for reading!

A quick confession. I usually stay away from technical implementation detail, because when it comes to actual engineering I am about as useful as a chocolate soldering iron. This post is different. Sub-processors and data flows are one of the very few areas where I genuinely know what I am talking about. So for once, consider me briefly competent.

At some point in the last year, someone on your team added an SDK. Then another one. A webhook. An error monitoring agent. An AI API where you pipe user input and get a response back. Each of those felt like a tool decision. Each of them is also a data governance decision. The two things rarely happened in the same conversation.

Under GDPR, every third party handling your users' personal data on your behalf is a data processor. You are legally responsible for them. If something goes wrong, regulators will ask you to account for the full chain, not just your own code. Most teams cannot do that, not because they are careless, but because the integrations accumulated faster than the documentation did.

Start with your network traffic, not a spreadsheet. Pull every outbound destination your application connects to. Then walk the codebase: every SDK, every API key, every webhook endpoint, every external call with a payload that could be linked back to a person.

Common places builders miss: error monitoring tools (stack traces contain more PII than you think), AI model APIs (their DPA terms have evolved: the version you accepted at signup may not reflect current terms), staging environments (often connected to the same third-party tools as production, sometimes with production data), and libraries with embedded analytics that phone home by default. "We did not know the library was doing that" is not a sentence that lands well in front of a regulator.

For each integration, three questions. Does it receive personal data? Do you have a signed DPA with this vendor? Where is the data processed? And if outside the EU, is there a valid transfer mechanism documented)?

Two things worth actually reading in any DPA before you accept it: whether the vendor can use your data to train their models, and what happens to your data when you stop paying. Both are frequently worse than the marketing copy implies. A vendor that refuses to sign a DPA entirely is a red flag, not a negotiating position.

Triage what you find, fix the highest-risk gaps first, and write it down. GDPR requires a Record of Processing Activities and what you just built is the foundation. Add a processor review to your integration checklist so you are never doing this retrospectively under deadline pressure.

Regulators are not auditing most startups. The real reason to do this is simpler: a breach involving data you did not know was being processed by a vendor you had not mapped is a different category of problem than one you can fully account for. One is an incident. The other is evidence that your data governance does not exist.

Do the audit. A few days of uncomfortable discovery, and then you know what you actually have.

Full step-by-step breakdown here: https://kolsetu.com/blog/your-processor-list-is-longer-than-you-think

Fancy to read more? Take a look at our blogs: https://kolsetu.com/blog

If you are building a social media AI agent, you probably do not want to start by maintaining separate integrations for Instagram, TikTok, Facebook, LinkedIn, YouTube and X just to answer basic performance questions.

Sociality MCP gives the agent one MCP layer for social media data. It can work with account stats, published posts and stories, competitor posts, channel performance, available metrics and workspace context.

For example, a user could ask:

"Check our Instagram and LinkedIn performance from last week, compare it with competitors and suggest what we should post next."

The agent can then check the active workspace, see which accounts and metrics are available, pull account stats and published posts for that date range, pull competitor posts and stats for the same period, compare what worked across owned and competitor content, and return a short report with performance changes, top posts, competitor patterns, and content ideas.

If the user says "also track this brand", the agent can add it as a competitor through MCP too.

So instead of the builder spending the first part of the project on API/data plumbing, they can focus more on the actual agent workflow.

Anything public-facing like publishing posts or replying to customers still feels like it should need more control.

If you were building a social media agent, what would you want the MCP layer to handle first?