About
LLMs are useful.
Production isn't a playground.
We're building the boring, defensible layer that lets AI agents help operate real infrastructure without becoming the next production incident.
Why this exists
Giving an LLM shell access is reckless. Giving it a catalog is sensible.
Give an LLM a terminal on a laptop and it works beautifully. Point it at the cluster that runs your customers' payments and you've handed root to a process that's fast and capable but wrong often enough to matter. There is no gate before the command and no record after. That is an access problem, not a model problem.
Ops teams already solved the access problem for humans and CI with a finite, versioned catalog of operations instead of a blank shell. emisar is that catalog for AI agents. The model can only call the actions you declared, and policy decides what runs. A human approves the risky ones, and every call lands in an audit log your security team will accept.
What we believe
Our priors.
Least privilege, always
The LLM is the least-trusted participant. The policy engine is the source of truth, not the model.
Auditability is non-negotiable
If a thing happens in production, it needs a searchable record in the cloud and a tamper-evident journal on the host.
Boring is a feature
We pick dull, defensible technology: Postgres, YAML, JSONL, and outbound TLS. There are no clever distributed systems for your oncall to learn.
A note from the founder
I spent a decade as a CTO, full-stack engineer, SRE and DevOps. I've been paged at 3 a.m. because someone — sometimes me — ran the wrong command on the wrong cluster. I've also watched LLMs solve, in seconds, problems that used to take an oncall an hour of greps and tails.
Both of those things are true. The job of emisar is to keep them true at the same time: let the runner help, but never let it become the cause of the next outage.
If that resonates — try it free, or email me with your war story. I read every message.
— Andrii Dryga, founder
Free forever for 3 runners.
Install on one host, point your LLM at it, and see exactly what it tried to do before you decide whether to keep it.