AI agents can act. Who stops the action that should never happen?
Agents can use employee credentials, call APIs, send messages, change records, move money, deploy code and trigger machines. A bad answer is a software problem. A bad action can become a business, security or physical-world event.
They only need a valid-looking path to a tool, credential or system that lets them cause an effect nobody explicitly authorized.
START · email · client onboarding. If you already know what Hood product you need, you are routed to the commercial path. If you do not, onboarding routes you to Hood Evaluation.
Product access: Hood Console is a separate authenticated application. It is not served by this corporate website and is not claimed live from this Worker. HIOP maturity and production deployment scope are disclosed in the Trust Center.
Authentication tells you who signed in. It does not prove an agent was allowed to cause a particular effect, on a particular resource, under current conditions.
Who is acting?
What may it cause?
Permit or deny
What happened?
The model is one part. The system is what acts.
Modern AI applications combine models with retrieval, memory, planners, tools, APIs, people and operational systems. Hood sits at the consequential boundary so intelligence can stay flexible without turning every discovered route into authority.
A modern AI stack is more than a prompt and a model.
Hood is designed to work around the systems enterprises are already assembling instead of forcing them to replace their intelligence stack.
Models and planners
LLMs, multimodal models, planning loops and agent reasoning can propose actions while authority remains separate.
Retrieval and memory
RAG, vector search, enterprise knowledge and durable context inform the task without automatically expanding permissions.
MCP, tools and APIs
Tool ecosystems and connected applications create capability. Hood focuses on what those connections are allowed to cause.
Software and physical systems
Cloud, payments, enterprise systems, robotics, scientific workflows and other consequential targets can sit beyond the authority boundary.
The risk is no longer just hallucination. It is unauthorized execution.
As agents become connected to browsers, enterprise apps, cloud tools, payments, code, robots and infrastructure, one bad instruction can cross from “AI output” into an actual effect. The control question must be answered before the effect happens.
That is the first question Hood onboarding is designed to surface.
Start with your emailThe agent-security problem has moved from model output to enterprise action.
NIST, OWASP and Microsoft all describe agent-specific risks around autonomous action, excessive permissions, prompt injection and machine-speed workflows. The shared problem is simple: once AI is connected to tools, the action boundary matters.
External references are industry context only; they do not imply endorsement, partnership, certification or product validation.
A capable agent can become dangerous when authority is implicit.
Hood is built around the boundary between what intelligent systems can figure out and what they are actually allowed to cause.
Intelligence side
Models, agents, data, connectors, browsers, applications, robots and automation propose what could happen.
AUTHORITY
BOUNDARY
Consequence side
Money moves. Access changes. Records update. Code deploys. Messages send. Machines act. Mission state changes.
IDENTIFY · VERIFY · REASON · DECIDE · DETERMINE AUTHORITY · DENY/AUTHORIZE · EXECUTE · OBSERVE · PRODUCE EVIDENCE · REMEMBER / RECOVER
One unauthorized agent action can become an economic consequence.
If your agent can send, spend, deploy, delete, approve or actuate, implicit authority is already too much authority.
START identifies the client, the consequential workflow and whether the right Hood product is already known.
One platform. Many use cases.
HIOP's core authority pattern can be evaluated across these domains. Industry pages describe applications and research directions, not customer deployments or regulatory approval.
Connect your models, agents, applications, data and approved infrastructure.
You do not start over. HIOP is designed to connect to approved models, applications, agents, and infrastructure through scoped integrations.
Evaluate · Connect · Configure · Validate · License · Deploy · Expand
The foundation for governed intelligence.
HIOP evaluates a bounded question before a consequential action: is this actor currently authorized to cause this effect on this resource? It then preserves evidence of the decision and observed outcome.
Start with the client. Route the need.
START begins with email and onboarding. Known buyers continue toward the right product and commercial review. Unknown needs branch into Hood Evaluation for recommendation and scope.
Enter email.
Client + problem.
Known or unknown need.
Product / scope.
Prove the fit.
Written terms.
Approved environment.
When the work cannot be instant.
Government, live operations, industrial physical effects, unusual security. Required approvals. Same primitives — not a disconnected product line.
Bring governed intelligence closer to real-world systems.
Hood Node connects Hood with local and edge environments for organizations operating machines, devices, vehicles, facilities, and mission systems.
- AI cannot guarantee outcomes. HIOP does not prove an AI will always be safe.
- It records whether this effect was permitted, and what happened.
- Prevents unauthorized actions from executing without a grant.
- Provides receipts enterprises can replay.
- If authority cannot be proven, the action does not proceed
- Separated proposal and review roles in the evaluation design
- Deployment scope is determined during evaluation
- Hood does not claim certifications it does not hold
Put an authority boundary between AI and consequence.
Start with your email. Hood onboarding determines whether you already know the product you need or should enter a scoped evaluation.
Start an evaluationThe AI economy pays for models, cloud, data, agents and automation.
See how intelligence becomes a governed effect.
Use the new public-safe HIOP explainer to test illustrative ALLOW, APPROVAL REQUIRED and DENY paths across enterprise, cloud, robotics and scientific scenarios—without exposing proprietary control logic.