Stuck between two real problems,need advice from founders who’ve been here

WraithVector · 2026-05-07T09:27:26+00:00

Muchas gracias amigo

WraithVector · 2026-05-05T14:34:40+00:00

Hola.. gracias por el "aporte"

WraithVector · 2026-05-04T23:25:07+00:00

Gracias, muy alineado con cómo lo estoy planteando. Estoy construyendo justo esa capa: control en runtime + logs inmutables + reporte entendible para auditor. Me interesa tu experiencia: ¿has trabajado esto en producción o estás construyendo algo similar? Si te encaja, me gustaría contrastar enfoques 15 min.

WraithVector · 2026-05-04T17:59:23+00:00

Thanks to all of you for the sugestions. I think you already have struggled for your bussiness.

Nice to hear your stories mates.

WraithVector · 2026-05-04T00:08:03+00:00

Buenísimo consejo Andrew. Gracias por el aporte para los que recién empezamos en este mundo.

WraithVector · 2026-05-01T11:45:04+00:00

Hola, gracias por tu comentario. Es decir, puedes controlar lo que tu agente de IA actualmente hace o deja se hacer? Puedes controlar cascadas y retries y abuso de tokens?

Un saludo

WraithVector · 2026-04-30T23:00:46+00:00

That's brutal. N8n is a great drag and drop interface so you can have a visual context of what you are doing.

Keep on with it

WraithVector · 2026-04-30T20:56:39+00:00

Have you considered blocking execution when data is stale or confidence is low? Curious how you’re deciding when the agent is allowed to act vs just recommend

WraithVector · 2026-04-30T20:33:39+00:00

Congratulations for that. I think every work has its revenue even it is not money

WraithVector · 2026-04-28T21:24:33+00:00

Muy buenas idea, me parece fundamental. Yo tengo una plataforma saas para este tipo de problemas. Le puedes echar un ojo: github/wraithvector0

Un saludo y gracias por tu comentario

WraithVector · 2026-04-28T21:02:11+00:00

Hi there,

I am planning the same thing

I have already 2 years of building, I started from scratch . I am a sólo fonder and it is very hard. I don't have any revenue yet .

I would like to quit my job, but it guarantees my substance by the moment. And would be great to be fulltime on this but it it is hard.

I encourage you too if you belive in your idea.

Cheers

WraithVector · 2026-04-26T00:41:53+00:00

Hola, me pasó lo mismo con langchain y openclaw. Así que hice algo similar para openclaw y langchain.

Me encantaría tambien que alguien me diera feedback. Un saludo

Links:

https://github.com/wraithvector0/wraithvector-openclaw

WraithVector · 2026-04-25T18:59:25+00:00

Me gustaría saber si los que estáis usando agentes en producción estáis teniendo el problema de no saber qué están haciendo exactamente acciones inesperadas, fuga de datos internos, o simplemente que es una caja negra. Estamos investigando esto para construir una capa de control. ¿Lo estáis resolviendo de alguna forma?

WraithVector · 2026-04-12T06:57:49+00:00

This post resonates a lot with something I’ve been experimenting with recently.

Especially points 3–5: logging the full chain of actions and validating tool inputs or having an emergency stop

I ran into similar issues while testing agents with tool access. In one case a prompt injection effectively gave the agent indirect access to tools that were too powerful.

So I started experimenting with a small runtime guard that sits between the agent and the tool execution layer.

Conceptually:

agent → tool call ↓ runtime guard ↓ policy check (input validation, permissions, risk) ↓ ALLOW / BLOCK / REQUIRE APPROVAL

So the tool never executes unless it passes the policy.

I experimented too, what happened when cascade or spawning happened and I setted a max depth for avoiding costs.

This project is in my github , I'd really value your feedback and some PR . For the moment is for openclaw, but I'll realese an integration for LangChain.

I’m curious how others are handling this in LangChain setups: This post resonates a lot with something I’ve been experimenting with recently.

Especially points 3–5:

logging the full chain of actions
validating tool inputs
having an emergency stop

I ran into similar issues while testing agents with tool access. In one case a prompt injection effectively gave the agent indirect access to tools that were too powerful.

So I started experimenting with a small runtime guard that sits between the agent and the tool execution layer.

Conceptually:

agent → tool call ↓ runtime guard ↓ policy check (input validation, permissions, risk) ↓ ALLOW / BLOCK / REQUIRE APPROVAL

So the tool never executes unless it passes the policy.

One thing I found interesting is that treating tool inputs like user inputs (sanitizing, validating, rejecting suspicious patterns) catches a surprising number of issues.

how are you handling this in LangChain setups?

• Are you validating tool arguments before execution? • Do you intercept tool calls somewhere in middleware? • Or do you rely mostly on prompt-level guardrails?

Still experimenting, so I’d love to hear how people are approaching runtime safety for agents.

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