Aegisora
Zero-latency runtime security proxy for AI agents
Hey IndieHackers,
Everyone is building autonomous AI agents, but nobody is talking about the authorization nightmare in production. If you give an LLM an API key or direct database access, traditional firewalls are completely useless.
I realized that if you use a standard 'allow/block' firewall for an AI agent, you fall into the "Binary Trap": You either block too much and break the agent's deterministic workflow, or you permit too much and risk massive data exfiltration or prompt-injection attacks.
I couldn't sleep thinking about this security gap, so I spent the last few months building a solution. Today, I am officially open-sourcing it.
Meet Aegisora — a zero-latency runtime governance and security layer for autonomous AI agents.
How it works (The Architecture): Instead of a binary allow/block decision, I built a third state: Asynchronous Human Escalation.
Aegisora sits exactly between your LangChain/LangGraph agents and your internal APIs. Safe requests pass through via a fast-path in < 10ms. But if an agent tries a high-risk, ambiguous action (like DROP TABLE users or sending bulk emails), Aegisora automatically pauses the tool call and routes it to a 'Human Review Queue'. A human manager can review the reasoning trace and approve or reject it, while the agent safely waits without timing out or crashing.
It acts as a drop-in middleware, so you don't have to rewrite your entire AI stack.
I need your brutal feedback! The core engine is now live and open-source on GitHub. You can get it running locally in under a minute.
If you are building autonomous workflows, I would love for you to fork the repository, look at the code, try to break it, and tell me what you think of the architecture.
Check it out here and let me know your thoughts (and a GitHub ⭐️ would mean the world to a solo founder!): https://github.com/aegisora-ai/aegisora.ai
How are you currently handling tool-call security for your LLMs in production?
2 Comments
The human-escalation layer is the part that makes this materially different from a conventional allow/block model. It also makes the operational side of the product much more interesting than the middleware itself.
Hey, congrats on building Aegisora — the zero-trust sidecar proxy angle is genuinely one of the more thoughtful approaches I've seen to the AI agent security problem.
I'm the founder of Zarek (https://zarek.tech/), a launch copilot for indie founders. I ran Aegisora through it to get a sense of what a real launch roadmap looks like for your product — thought you might find the output useful.
Here's what Zarek produced:
SEO / Blog — Draft a technical post like "How we block PII leakage in AI agent pipelines" targeting searches from platform engineers evaluating agent observability tools.
Hacker News (Show HN) — Lead with the fail-closed sidecar angle: "Show HN: Aegisora — a zero-trust proxy that enforces least-privilege tool calls for AI agents, runs inside your own VPC." That framing hits the self-hosting crowd hard.
Reddit (r/MachineLearning / r/devops) — Post a short breakdown of a real prompt injection it caught in testing, with the audit log output. Concrete examples convert lurkers into signups.
Product Hunt — Tagline draft: "See what your agents actually do — and stop what they shouldn't." Gallery includes a side-by-side of a blocked PII call vs. an allowed one.
Cold outreach (platform teams) — Short one-liner for LinkedIn/email: "If your agents can call any API with any payload, you don't have a security posture — Aegisora fixes that in one sidecar."
Each of these has channel-specific copy, timing, and a checklist baked in.
If you want the full roadmap generated for Aegisora specifically, happy to walk you through it — or just reach out directly: wangc2016217@gmail.com.