Hey IH,
After several months of solo building, I'm selling Custodyn — a runtime security and trust infrastructure platform for AI agents.
What it does:
AI agents autonomously execute code, send emails, make payments with no oversight. Custodyn sits inside agent code via SDK — blocks risky actions, routes medium-risk to human approval queue, logs everything in a tamper-evident audit trail.
Three ways to integrate:
SDK — pip install or npm install, one line of code
Reverse proxy gateway — zero code changes, route traffic through Custodyn
Sentinel CLI — system-wide monitoring (in development)
What's included:
Live platform at custodyn.app
Python + JS SDKs on PyPI and npm (MIT licensed)
LangChain, LangGraph, CrewAI, AutoGen, MCP, Claude Code, OpenAI Agents SDK integrations
Human approval queues, policy engine, multi-tenant dashboard
Role-based access control — Owner, Admin, Operator, Viewer roles
Multi-tenant architecture — serves multiple organizations from one platform
Full security pentest done
Open-core model ($49–$2000/mo tiers)
60 day handover support
Pre-revenue. Full acquisition.
Listed on IndieMaker:
👉 indiemaker.com/listings/custodyn-ai-agent-security-platform
DM me or email founder@custodyn.app
Atul
Exactly. I think the interesting part is that these models may actually be complementary rather than competing.
If Custodyn blocks before dispatch at an enforcement boundary it controls, then “prevented dispatch” is a strong, defensible claim. There’s no reason to weaken that to UNRESOLVED.
The uncertainty only begins once the action crosses a boundary where the enforcing system can no longer establish consequence truth.
That’s why I’ve found it useful to keep two questions separate:
What did the control provably enforce?
and
What consequence can the available evidence provably establish?
Sometimes those line up perfectly. Sometimes they don’t.
A runtime authorization layer that can strongly prove the first, combined with an evidence model that refuses to overclaim the second, feels like a much stronger security story than asking either layer to prove something outside its observation boundary.
Really interesting architecture. I’d be interested to see how Custodyn handles that boundary as you develop it.