PERASYS LABS

Control AI systems that can act.

AI can reason, plan, and act. Logos controls what those systems are allowed to do, who authorized it, and how every action can be verified later.

Become a design partner Review the architecture

The problem

AI can now take actions on behalf of your organization.

AI systems no longer just answer. They send, purchase, file, route, approve, and execute.

When an agent acts, most organizations cannot answer four questions: who approved it, why it was allowed, what evidence supported it, and what changed afterward.

That gap is why agent deployments stall in pilot. It is the gap Perasys Labs closes.

The product

Logos is the control layer for AI agents.

Human authority

The named person who authorized an action and accepted responsibility for it.

AI activity

What the agent proposed, generated, inferred, or attempted to execute.

Delegation

The scoped, revocable policy under which an action was allowed to run without a new approval.

Evidence

The inputs, references, and decision path behind the action.

Outcome

What happened, recorded in a tamper-evident log that can be verified later by anyone.

How it works

A control path for agents that take consequential action.

Agent proposes

The system generates a recommendation, draft, action, or execution request.

Human authorizes

A named person approves, edits, rejects, or delegates through a scoped policy they can revoke.

Logos records

The runtime captures human authority, AI activity, delegation, evidence, and outcome.

Anyone verifies

The record can be checked later, offline, to prove what was authorized and whether it changed.

Use cases

For teams putting AI agents into production.

AI agent platforms

Add authorization and an audit record to agent actions without replacing your model or tool stack.

Compliance and risk

Produce verifiable records for AI-driven decisions that must survive audit, review, and dispute.

Regulated workflows

Mark and prove AI-assisted output to meet disclosure and provenance requirements such as the EU AI Act and SB 942.

Enterprise automation

Separate machine capability from organizational authority before agent actions become operational.

The principle

Capability is not authority.

A system's ability to perform an action is not permission to perform it. Machines may act. Humans remain accountable. Logos exists so increasing capability does not bypass human authority.

Working papers

The product is simple. The work underneath it is rigorous.

The Control ProblemWhy enterprise agent deployments stall, and what actually closes the gap. Logos Technical OverviewRuntime architecture, guarantees, invariants, and the record model. Capability Is Not AuthorityThe principle beneath the control layer, and why it is load-bearing for agents. Building Accountable AI AgentsA practical pattern for agents whose every action is authorized, attributed, and verifiable.

Design partners

Deploying agents into workflows where authorization and proof matter?

We are working with product, compliance, and operations teams that need AI agent actions to be authorized, accountable, and verifiable in production.

Start a conversation