u/PublicTension720

Gerade bauen immer mehr Leute KI-Agenten.

n8n-Agenten. Cursor-Agenten. Claude-Code-Agenten. MCP-Setups. LangChain-Skripte. Persönliche Assistenten. Kundenautomationen.

Aber kaum jemand hat einen einfachen Ort, um die wichtigsten Fragen zu beantworten:

Welche Agenten habe ich überhaupt?
Was machen sie genau?
Auf welche Tools und Daten greifen sie zu?
Welche davon sind riskant?
Welche sind getestet?
Welche verbrennen unnötig Geld?
Welche kann ich sicher teilen, verkaufen oder deployen?

Genau da sehe ich die Lücke.

Aktuell liegen Agenten überall verstreut: in n8n-Workflows, GitHub-Repos, Cursor-Projekten, Claude-Chats, Notion-Dokumenten und irgendwelchen Screenshots.

Das ist unübersichtlich. Und je mächtiger Agenten werden, desto riskanter wird dieses Chaos.

Ich habe die Idee auch schon in amerikanischen Subreddits gepostet, und dort wurde ziemlich klar: Dieses Problem ist real. Viele bauen Agenten, aber verlieren schnell den Überblick über Tools, Datenzugriffe, Kosten, Tests und Risiken.

Deshalb arbeite ich gerade an einem Agent Control Center.

Die Idee: Ein Dashboard, in dem jeder KI-Agent einen eigenen Agent Passport bekommt.

Darin sieht man zum Beispiel:
Zweck
Tools
Datenzugriff
Berechtigungen
Risiken
Test-Checkliste
Kostenschätzung
Version / Status
teilbare Dokumentation

Für Entwickler ist es ein sauberer Überblick über alle eigenen Agenten.

Für Creator ist es ein Trust-Layer für Templates.

Für Agenturen ist es ein kundenfertiger Übergabebericht.

Für Teams ist es ein Inventar: Welche Agenten existieren überhaupt und was dürfen sie?

Kurz gesagt: Du baust Agenten überall. Du kontrollierst sie an einem Ort.

Hättet ihr auch Lust das zu nutzen? Also in den amerikanischen Subreddits kam dieses Projekt echt gut an.

Gerade bauen immer mehr Leute KI-Agenten.

n8n-Agenten. Cursor-Agenten. Claude-Code-Agenten. MCP-Setups. LangChain-Skripte. Persönliche Assistenten. Kundenautomationen.

Aber kaum jemand hat einen einfachen Ort, um die wichtigsten Fragen zu beantworten:

Welche Agenten habe ich überhaupt?
Was machen sie genau?
Auf welche Tools und Daten greifen sie zu?
Welche davon sind riskant?
Welche sind getestet?
Welche verbrennen unnötig Geld?
Welche kann ich sicher teilen, verkaufen oder deployen?

Genau da sehe ich die Lücke.

Aktuell liegen Agenten überall verstreut: in n8n-Workflows, GitHub-Repos, Cursor-Projekten, Claude-Chats, Notion-Dokumenten und irgendwelchen Screenshots.

Das ist unübersichtlich. Und je mächtiger Agenten werden, desto riskanter wird dieses Chaos.

Ich habe die Idee auch schon in amerikanischen Subreddits gepostet, und dort wurde ziemlich klar: Dieses Problem ist real. Viele bauen Agenten, aber verlieren schnell den Überblick über Tools, Datenzugriffe, Kosten, Tests und Risiken.

Deshalb arbeite ich gerade an einem Agent Control Center.

Die Idee: Ein Dashboard, in dem jeder KI-Agent einen eigenen Agent Passport bekommt.

Darin sieht man zum Beispiel:
Zweck
Tools
Datenzugriff
Berechtigungen
Risiken
Test-Checkliste
Kostenschätzung
Version / Status
teilbare Dokumentation

Für Entwickler ist es ein sauberer Überblick über alle eigenen Agenten.

Für Creator ist es ein Trust-Layer für Templates.

Für Agenturen ist es ein kundenfertiger Übergabebericht.

Für Teams ist es ein Inventar: Welche Agenten existieren überhaupt und was dürfen sie?

Kurz gesagt: Du baust Agenten überall. Du kontrollierst sie an einem Ort.

Hättet ihr auch Lust das zu nutzen? Also in den amerikanischen Subreddits kam dieses Projekt echt gut an.

Everyone is building AI agents now.

n8n agents. Cursor agents. Claude Code agents. MCP setups. LangChain scripts. Personal assistants. Client automations.

But nobody has a clean place to answer the basic questions:

What agents do I have?
What do they do?
What tools and data can they access?
Which ones are dangerous?
Which ones are tested?
Which ones are wasting money?
Which ones are ready to share, sell or deploy?

That’s the gap.

Right now agents live across n8n workflows, GitHub repos, Cursor projects, Claude chats, Notion docs and random screenshots.

It’s messy. And as agents get more powerful, messy becomes risky.

So I’m working on an Agent Control Center.

One dashboard where every AI agent gets an Agent Passport:

purpose
tools
data access
permissions
risks
test checklist
cost estimate
version/status
shareable docs

For hobby builders, it’s a clean overview of all your agents.

For creators, it’s a trust layer for templates.

For agencies, it’s a client-ready handover report.

For teams, it’s an inventory of what agents exist and what they’re allowed to do.

You build agents anywhere. You control them in one place.

Would you use this? And what would make this a must-have for you?

Everyone is building AI agents now.

n8n agents. Cursor agents. Claude Code agents. MCP setups. LangChain scripts. Personal assistants. Client automations.

But nobody has a clean place to answer the basic questions:

What agents do I have?
What do they do?
What tools and data can they access?
Which ones are dangerous?
Which ones are tested?
Which ones are wasting money?
Which ones are ready to share, sell or deploy?

That’s the gap.

Right now agents live across n8n workflows, GitHub repos, Cursor projects, Claude chats, Notion docs and random screenshots.

It’s messy. And as agents get more powerful, messy becomes risky.

So I’m working on an Agent Control Center.

One dashboard where every AI agent gets an Agent Passport:

purpose
tools
data access
permissions
risks
test checklist
cost estimate
version/status
shareable docs

For developers, it’s a clean overview of all your agents.

For creators, it’s a trust layer for templates.

For agencies, it’s a client-ready handover report.

For teams, it’s an inventory of what agents exist and what they’re allowed to do.

You build agents anywhere. You control them in one place.

Would you use this? And what would make this a must-have for you?

Everyone is building AI agents now.

n8n agents. Cursor agents. Claude Code agents. MCP setups. LangChain scripts. Personal assistants. Client automations.

But nobody has a clean place to answer the basic questions:

What agents do I have?
What do they do?
What tools and data can they access?
Which ones are dangerous?
Which ones are tested?
Which ones are wasting money?
Which ones are ready to share, sell or deploy?

That’s the gap.

Right now agents live across n8n workflows, GitHub repos, Cursor projects, Claude chats, Notion docs and random screenshots.

It’s messy. And as agents get more powerful, messy becomes risky.

So I’m working on an Agent Control Center.

One dashboard where every AI agent gets an Agent Passport:

purpose
tools
data access
permissions
risks
test checklist
cost estimate
version/status
shareable docs

For hobby builders, it’s a clean overview of all your agents.

For creators, it’s a trust layer for templates.

For agencies, it’s a client-ready handover report.

For teams, it’s an inventory of what agents exist and what they’re allowed to do.

You build agents anywhere. You control them in one place.

Would you use this? And what would make this a must-have for you?

AI agents are moving from “chatting” to actually doing work: reading company data, sending emails, updating CRMs, reviewing invoices, drafting contracts, triggering workflows.

That creates one big problem: Loss of control.

A single prompt injection, hallucinated fact, or runaway loop can cause data leaks, wrong decisions, compliance issues, or thousands in API costs.

So I’m building a guardrail platform for AI agents.

The idea is simple: Put a control layer between the agent, the model, company data, and external tools.

It checks:
▪️malicious prompts and prompt injections
▪️hallucinated or unsupported claims
▪️risky tool calls
▪️sensitive data exposure
▪️runaway loops and API cost spikes
▪️actions that should require human approval

So instead of blindly trusting an agent, companies can define exactly what it is allowed to do, what must be blocked, and what needs approval.

Think of it as a safety switchboard for AI agents.
Not another chatbot wrapper. A control plane for making autonomous AI usable in real businesses.

🔈If you think this needs to exist, an upvote would help a lot.

🔈And if you’re interested in trying it when it goes live, comment below and I’ll send you an invite.

AI agents are moving from “chatting” to actually doing work: reading company data, sending emails, updating CRMs, reviewing invoices, drafting contracts, triggering workflows.

That creates one big problem: Loss of control.

A single prompt injection, hallucinated fact, or runaway loop can cause data leaks, wrong decisions, compliance issues, or thousands in API costs.

So I’m building a guardrail platform for AI agents.

The idea is simple: Put a control layer between the agent, the model, company data, and external tools.

It checks:
▪️malicious prompts and prompt injections
▪️hallucinated or unsupported claims
▪️risky tool calls
▪️sensitive data exposure
▪️runaway loops and API cost spikes
▪️actions that should require human approval

So instead of blindly trusting an agent, companies can define exactly what it is allowed to do, what must be blocked, and what needs approval.

Think of it as a safety switchboard for AI agents.
Not another chatbot wrapper. A control plane for making autonomous AI usable in real businesses.

🔈If you think this needs to exist, an upvote would help a lot.

🔈And if you’re interested in trying it when it goes live, comment below and I’ll send you an invite.

Al agents are moving from "chatting" to actually doing work: reading company data, sending emails, updating CRMs, reviewing invoices, drafting contracts, triggering workflows.

That creates one big problem: Loss of control.

A single prompt injection, hallucinated fact, or runaway loop can cause data leaks, wrong decisions, compliance issues, or thousands in API costs.

So I'm building a guardrail platform for Al agents.
The idea is simple: Put a control layer between the agent, the model, company data, and external tools.

It checks:
• malicious prompts and prompt injections
• hallucinated or unsupported claims risky tool calls
• sensitive data exposure
• runaway loops and API cost spikes
• actions that should require human approval

So instead of blindly trusting an agent, companies can define exactly what it is allowed to do, what must be blocked, and what needs approval.

Think of it as a safety switchboard for Al agents.
Not another chatbot wrapper. A control plane for making autonomous Al usable in real businesses.

If you think this needs to exist, an upvote would help a lot.

And if you're interested in trying it when it goes live, comment below and I'll send you an invite.

AI agents are moving from “chatting” to actually doing work: reading company data, sending emails, updating CRMs, reviewing invoices, drafting contracts, triggering workflows.

That creates one big problem: Loss of control.

A single prompt injection, hallucinated fact, or runaway loop can cause data leaks, wrong decisions, compliance issues, or thousands in API costs.

So I’m building a guardrail platform for AI agents.

The idea is simple: Put a control layer between the agent, the model, company data, and external tools.

It checks:
▪️malicious prompts and prompt injections
▪️hallucinated or unsupported claims
▪️risky tool calls
▪️sensitive data exposure
▪️runaway loops and API cost spikes
▪️actions that should require human approval

So instead of blindly trusting an agent, companies can define exactly what it is allowed to do, what must be blocked, and what needs approval.

Think of it as a safety switchboard for AI agents.
Not another chatbot wrapper. A control plane for making autonomous AI usable in real businesses.

🔈If you think this needs to exist, an upvote would help a lot.

🔈And if you’re interested in trying it when it goes live, comment below and I’ll send you an invite.

I’m building a small developer tool for teams using AI agents in production.

The problem I’m seeing: AI agents can now call tools, access systems, create PRs, send messages, update tickets, touch databases, etc. but teams often don’t have a simple way to control, approve, block and prove what those agents actually did.

So I’m working on a lightweight gateway/control layer for AI agent actions:
- log every tool call
- block risky actions
- require human approval for sensitive actions
- show which agent did what
- create an audit trail for later review

The goal is simple: make AI agents safer to use in real workflows, without stopping teams from moving fast.

I’m still building the tool.

If you’re building with AI agents, MCP, Cursor, Claude, LangChain, CrewAI or similar tools, I’d love to hear:

Would this be useful for you or your team? If you‘re interested, comment below.

I’m building a small developer tool for teams using AI agents in production.

The problem I’m seeing: AI agents can now call tools, access systems, create PRs, send messages, update tickets, touch databases, etc. but teams often don’t have a simple way to control, approve, block and prove what those agents actually did.

So I’m working on a lightweight gateway/control layer for AI agent actions:
- log every tool call
- block risky actions
- require human approval for sensitive actions
- show which agent did what
- create an audit trail for later review

The goal is simple: make AI agents safer to use in real workflows, without stopping teams from moving fast.

I’m still building the product.

If you’re building with AI agents, MCP, Cursor, Claude, LangChain, CrewAI or similar tools, I’d love to hear:

Would this be useful for you or your team? If you‘re Interested, comment below.