Authority Ledger for Operating Systems
An AI agent shipped something last quarter and nobody can say who approved it. That's the gap between a written AI policy and something your engineers actually run against.
Built by TEKIMAX: integrated into the mission-critical software we deliver.
Your rules, applied as the work runs.
Approve the models, packages and tools a project may use. A model off the list is refused, anything permanent waits for a named person, and the parts list exports in the standard your security tools already read.
AI agents reach no further than their owner
AI agents work strictly within human-set boundaries. An agent's access never exceeds its owner's permissions, and every action is dual-stamped with both the machine and human identity.
Works with the tools the work already lives in
Promoting a model is one decision, made once in Studio. Every call after it, from a terminal or from an agent, is counted against it.
An agent runs on somebody’s authority and never reaches further than they do. That person stays accountable for what it produces, and both names go on the record beside the work.
An AI agent past its limits is refused on the call. What cannot be undone stops before it runs, with the reason beside it.
How it works
Four steps, one record at the end
Work moves through a unified, 4-step governed pipeline that enforces real-time policy checks at every stage.
Set up the rules
Define team roles, AI permissions, and compliance guardrails up front. Impossible or non-compliant workflows are flagged and blocked immediately during setup.
Approve models and integrations
Maintain centralized tool control. Only authorized team members can approve AI models, skills, and packages across your enterprise network.
Assign agent ownership
Every AI agent maps directly to a team member. Reversible tasks run automatically, while permanent actions queue for explicit human sign-off.
Hand off the work and the record
Deliver complete projects with a cryptographically hashed record of the work. Clients gain access to an exportable, audit-ready delivery record detailing the components, controls, and approvals behind it.
Feature highlights
Built into every action
Three controls run on every action an AI agent takes, so accountability, safety, and speed hold at once.
- CollaborationTwo names on every action
Clear accountability on every line. Every system event logs both the executing AI agent and its responsible human supervisor in real time, eliminating retroactive guesswork.
GovernanceNothing irreversible runs aloneAgent output is treated as unverified until validated. Reversible operations process instantly, while critical decisions (deployments, publications, client sends) pause for mandatory human authorization.
AugmentationMachine speed, human directionScale team capacity without diluting quality. AI agents handle high-volume execution, while human experts retain total strategic oversight and decision-making control.
ALOS brings the work together: humans, agents and workflows in one workspace.
humans
agents
workflows
What you can point at
Every AI agent has an owner
Every AI agent gets a login of its own and belongs to somebody. It acts for them, never as them, and that person stays accountable for the security, functionality and integrity of what it produces.
- Never a shared key, never a borrowed login, and it never sees a password
- It can only do what its owner may do, and what they approved it to do
- Take somebody off a project and their AI agent loses it too
Anything irreversible stops
Each step’s kind is decided in advance, from a written list. Reversible work runs. Work that cannot be undone stops: most of it goes to a named human, and the rest only a signed-in person can run.
- Deploying, publishing and sending to a client all wait
- The approval sits beside the work it approved
- A refusal is recorded as carefully as a yes
Everything reversible in this run already finished. This is the only step that waited.
One record, written as the work happens
The record is the product, not a report written afterward. It is written as the work happens and sealed as it lands, so when somebody asks who did what six months later, you export it rather than reconstruct it.
- Who changed what, when, and under whose authority
- A later edit to the record would show
- The parts list and the controls export in formats your auditors already read; the delivery record is signed and hashed
Every line is assigned to whoever acted, and names the human whose authority they used.
Where to start
One platform, two ways in
The mechanism is the same on both sides. What changes is the work it is pointed at, and who signs for it.
StudioWhere your people and AI agents work.Software companies, and the forward-deployed engineers and contractors who deliver for them.Agent runsAgent securityApproved modelsScans and approved packagesCommand lineOpen
Customer portalYour client watches the work itself.The client paying for the work, and the people they answer to.Client portalApprovalsAudit trail and evidenceOpen
Earn while you learn
Train the people who approve the work
Every approval on the record is a person's judgment. If that person is not trained, the stamp is rubber and the record shows oversight that did not happen. AI Solutions Specialist is a Registered Apprenticeship: TEKIMAX trains the people who sign for what your agents produce.
- They learn on real client work, beside a mentor, from the first week
- They move on by showing what they can do, not by serving out the months
- TEKIMAX sponsors the programme, or teaches the related instruction for yours
AI Solutions Specialist
U.S. Department of Labor registered · Competency-based
From the newsroom
What we published lately
Security · standardsThe OWASP LLM Top 10 for 2026.For the first time the list is checked against real incidents, not only expert opinion. The order moved, one risk was renamed, and the guidance underneath is the thing we build for: assume the model gets fooled, and make sure nothing important breaks when it does.Read it
WorkforceBuilding the next generation of AI talent.TEKIMAX runs a Department of Labor Registered Apprenticeship: paid career pathways in AI and cybersecurity, and no four-year degree required.Read it
Governance · standardsThe NIST AI RMF, at any size.The common language of AI risk in the United States, and the reason your customers are suddenly asking about it. Why a ten-person company needs it as much as a global enterprise, and what it will not do for you.Read it
Talk to us
Tell us what you are building
Say what you are building and who asks you about it: a client, an auditor, a programme office. A person reads it and writes back with the record your work would leave behind. No demo you have to sit through before anybody answers a question.
Would rather write your own email? [email protected]
Come with real work
Bring a repo, a workflow, a client deliverable. We'll show you the record it would leave behind.
