Most AI agents do not fail because the model is weak. They fail because they start working before defining the result.
Paste this into your agent's system prompt:
Before executing any broad or high-stakes task:
1. Rewrite the request as one concrete result.
2. State why that result matters.
3. Identify the primary bottleneck.
4. Find a proven pattern worth adapting.
5. Select the 1–3 actions most likely to create 80% of the result.
6. List what should be ignored for now.
7. End with the next best move.
Rule: Do not brainstorm before diagnosing the bottleneck.
This turns long, generic answers into focused action(which also saves on tokens).
What type of task makes your agents lose focus most often?
Like and share with anyone building AI agents.
Link to the full skill is in the first comment.
Tired of messy agent output folders and never finding what you need?
Use this simple, repeatable structure:
- Filename: YYYY-MM-DD-semantic-slug.ext
- Folders: /outputs/YYYY-MM-DD/ or /outputs/[topic]/ for clear organization
- Add YAML frontmatter in .md files + update INDEX.md for instant search
Your agents (and you) can now easily find, reuse, and understand everything.
Leads to clean, self-documenting workflows in your harness.
What's your biggest output chaos right now? Reply below
Like and share with anyone who works with AI agents!
Link to the skill is in the first comment.
At OnBuzz, we are teaching agents how to work, not just what to do.
The stack helps them ask better questions, make decisions, and improve their output.
Link to the ready to copy skill stack in the first comment
Last year I had 8 agents running locally. They worked in isolation.
The moment we tried to chain them, everything broke. Context lost. Duplicated actions. Conflicting outputs.
That's the moment OnBuzz started.
Everyone's building AI agents. Almost no one is running them in production.
The gap between "demo" and "deployed" is where most projects die.
We learned this the hard way.
2023: AI is a chatbot
2024: AI is a copilot
2025: AI is a team
The founders who win won't use AI.
They'll run AI teams.
We're building the OS for that.
If you're tired of AI that forgets, try one that remembers.
If you're tired of managing tools, try running a team.
If you're tired of waiting, start today.
Pre-built AI teams. Ready to run.
→ Dev Team (code/test/review)
→ Sales Team (research/outreach/CRM)
→ Discord Team (mod/onboard/events)
→ Founders Ops (tasks/research/content)
Import. Customize. Execute.
10 minutes to a working AI team.
Before: Idea → 2 weeks → shipped
After: Idea → 2 days → shipped
The difference?
Not faster typing.
Parallel execution by a team that actually coordinates.
Founders don't need another tool.
They need a team that:
→ Prioritizes their tasks
→ Does research before they ask
→ Drafts content while they sleep
→ Never drops the ball
I built OnBuzz because I was drowning in my own company.
Now I have an AI ops team.
Running a Discord community?
You don't need more mods.
You need AI mods that:
→ Never sleep
→ Remember every user
→ Auto-onboard new members
→ Track engagement
→ Run events
One server owner cut admin time by 70%.
Dev team lead : "Fix the auth bug"
My AI team:
→ Reads the codebase
→ Writes tests
→ Fixes the code
→ Opens a PR
→ Notifies me
Dev team lead : reviews for 5 minutes, merges
This is not the future. This is today.
We open sourced the core.
Not a demo. Not a lite version.
The full multi-agent engine. The memory framework. The execution system.
Free. Forever.
Star it or fork it. Your call.