u/Even-Outcome-9801

The video starts by showing an extremely comprehensive, well-structured, production-ready Deep Research Report delivered directly via email. It breaks down an automated evaluation of a company (AgentMinds), covering Executive Summaries, Competitor Landscapes, full SWOT Analysis, Growth Signals, and clear, prioritized Recommended Next Steps—all generated with a 55/100 confidence score breakdown.

It shifts to the n8n canvas displaying a robust visual workflow triggered via webhooks/forms. The logic branches out to sub-agents working concurrently: a core Research Agent, a Fact Extractor Agent, and an Audit Agent. Their payloads merge into structured outputs, route through an OpenRouter Chat Model parser, format dynamically, generate an HTML/Markdown report via a Report Builder Agent, and finally shoot it out through Gmail.

Marketing & Sales OS
The concept:

Instead of one general AI assistant that does everything, I built 84 specialised agents,each with a single job. Blog Writer only writes blogs. Cold Call Script Writer only writes cold call scripts. The theory is that specialisation produces better outputs because the context, instructions, and examples are all scoped to one task.

What I'm actually unsure about and want feedback on:

1. Is specialisation even the right approach? Or are people better off just writing good prompts into a general model? I have a strong opinion here but want to be challenged on it.
2. 84 agents — is that a feature or a UX problem? I added search and category filters but I genuinely don't know if the number feels powerful or just overwhelming when you first land on it.
3. Brand Voice as a core feature — users upload their guidelines once and every agent writes in their tone automatically. Is this actually solving a real pain point or is it something people think they want but don't use?
4. Human-in-the-Loop by design — I deliberately didn't build auto-publish. Every output needs manual approval before it goes anywhere. Was this the right call or am I just adding friction?

What I've heard so far from early users:

• The specialisation clicks immediately for some people and confuses others
• People love Brand Voice in theory but haven't tested it enough yet
• The pricing model (run-based rather than seat-based) is getting mixed reactions

Would genuinely appreciate anyone who's built something similar or worked in this space poking holes in the approach. What am I missing? What would make you not use this?
And any suggestions and recommendations and improvements.

I wanted to share what I just launched and get real opinions.

AgentMinds OS, 84 specialised AI agents for marketing and sales. The idea is simple: one agent, one job. You don't ask a generalist to do everything. You pick the specialist you need.

Categories: Content, Ads, Email, Social, Sales, Strategy, Research, Reports, E-commerce.

What makes it different from just using Claude or ChatGPT directly:

• Brand Voice is trained once and applied automatically to every agent
• Agents remember your previous runs (History tab)
• You can schedule agent runs in advance
• Output actions: Copy, export to PDF, mark as Good/Needs Work so the agent learns

Pricing starts at ₹900/month (~$9). Free tier exists.

Genuine questions I'd love feedback on:

1. Is 84 agents overwhelming or is the search + category filter enough?
2. Would you trust an AI agent for sales scripts or does that feel like a step too far?
3. What's missing that you'd actually pay for?

Got into a conversation with a marketing director last week. Her team of 4 was spending 60% of their time on tasks that didn't require creative thought: scheduling posts, pulling reports, writing first-draft email copy, building audience segments, and running competitor research.

None of that work needed a human brain. It just needed a human to approve it before it went live.

So we built something different. Not a black box that spits out marketing output you can't control. Instead, 89+ AI agents that do the repetitive work, but your team stays in the loop on everything that gets published or sent.

The result? One client built what used to take 3 people to do. No new hires. No outsourcing headaches. Just approval workflows that kept human judgment in place.

Here's what we're seeing work:

• Content research and first drafts (before your team writes)
• Email sequences (templated but still reviewed)
• Social media captions (batch approved, not auto-posted)
• Audience segmentation (built the rules, but you decide)
• Competitor reports (raw data pulled, formatted, human sign-off)
• Ad copy variants (AI writes 10, you pick the best 2 to run)

The catch: this isn't "set it and forget it" automation. It's "let AI handle the grunt work, you handle the strategy" automation.

For the builders here: we're using Claude for the reasoning layer, and we built this specifically for marketing and sales workflows. Not a generic AI tool. Not a no-code platform pretending to be AI.

Question for you: what's the one task your team does manually every week that you'd automate if you could do it without losing control?

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Trying to identify the biggest operational bottlenecks in modern marketing teams.

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The automation now has broken and stopped working when I built it on their system.

Have you faced this issue before and if so how did you solve it?