Envpod - Governance for AI agents
Diff, approve, and rollback every AI agent side effect
What it does
AI agents run with full system access and zero accountability. Docker isolates but doesn't govern. envpod wraps every agent in a copy-on-write overlay - your host is never touched until you review and commit. Encrypted credential vault, per-pod DNS filtering, action queue for dangerous ops, append-only audit trail. Single 13MB static binary. No daemon, no dependencies. 32ms warm start. Tested on 9 Linux distros. 41 agent configs included. Open source.
Does the same job
all alternatives →- AVAgent Vault – Open-source credential proxy and vault for agentsApr 2026 · github.com · ▲156
Hey HN! Today we're launching Agent Vault - an open source HTTP credential proxy and vault for AI agents. Repo is at https://github.com/Infisical/agent-vault, and there's an in-depth description at https://infisical.com/blog/agent-vault-the-open-source-crede.... We built Agent Vault in response to a question that been plaguing the industry: How do we give agents secure access to services without them reading any secrets? Most teams building agents have run into this exact problem: They build an agent or agentic system and come to realize at some point…
- RARunning AI agents across environments needs a proper solutionMar 2026 · github.com · ▲8
Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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