AI control and infrastructure software
AI agents can build software.
Humans should still be in control.
Keais is developing AI Supervisor, a local, provider-independent control and execution layer for software-development agents. It turns human intent into planned, tested, reviewable software while keeping the agents themselves from becoming the final authority over their work.
plans · mediates authority · coordinates work · evaluates evidence
Why it exists
AI coding agents can write code, change files, run tools, and review their own output. The harder problem is deciding what they are allowed to do, whether the result is actually acceptable, and when a person needs to decide.
AI Supervisor
A supervisor for the agents doing the work.
AI Supervisor sits between a human-approved goal and the software-development agents carrying it out. It coordinates planning, implementation, testing, review, and version control while preserving explicit human ownership of the product.
The aim is not to make every step manual. It is to let routine engineering proceed under approved rules while keeping consequential authority, unresolved disagreements, and final acceptance with the human.
Requirements, assumptions, risks, implementation steps, and acceptance criteria are made explicit before implementation begins.
The human approves the plan, grants elevated permissions, resolves unresolved disagreements, and decides when the product is acceptable.
Builds, tests, runtime behavior, reviews, and other evidence can support or contradict an agent's claim that its work succeeded.
Version control and checkpoints preserve approved and working states so experimentation can remain reversible.
The difference
Autonomy is useful.
Authority is different.
Receive a task → act → report success
Clarify → approve → execute within authority → gather evidence → review → accept or revise
How it works
Automation in the middle. Human authority around it.
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1
Clarify
Understand what the person actually wants.
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2
Plan
Define requirements, risks, implementation steps, and acceptance criteria.
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3
Approve
The human approves the plan before implementation begins.
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4
Execute
Agents perform approved engineering work within the authority they were given.
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5
Test + review
Evidence is gathered and work that falls short returns for correction.
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6
Preserve
Working states, decisions, and useful evidence remain recoverable.
Design principles
Built to survive changing models and vendors.
AI Supervisor is intentionally provider-independent. The engineering process should remain understandable and recoverable even as the agents behind it change.
The human defines goals, approves plans, establishes priorities, grants elevated permissions, and decides when the product is acceptable.
A project should not become dependent on one AI provider merely because that provider happened to perform the original work.
Worker output remains evidence until it is independently verified. Final acceptance remains an authoritative Supervisor decision under human-approved policy.
Agents can experiment while recoverable versions and checkpoints preserve approved and working project states.
Keais
Control infrastructure for AI that can act.
Keais is developing AI control and infrastructure software. AI Supervisor is the first project: a practical system for making software-development agents more useful without treating autonomy as a substitute for authority, evidence, or human judgment.
AI Supervisor is in private prototype development. Public details are intentionally limited, and the current prototype is not presented as a production-grade security sandbox.
Contact
Interested in supervised AI development?
Keais is interested in conversations with prospective enterprise users, technical partners, and investors who are thinking seriously about autonomous software-development systems.