Runtime controls for
AI Agents
Prevent autonomous agents from overspending, misusing tools, and taking unsafe actions in production.
AI agents can act autonomously
But most systems cannot control them safely.
Agents can now use tools, call APIs, provision infrastructure, and spend money on their own. But most teams still rely on manual approvals, hardcoded limits, and post-incident logging to control them. These approaches break once agents start running continuously in production.
Manual Approval Flows
Humans cannot review every action once agents operate at machine speed. Requiring approval for every tool call slows systems down and removes the benefit of autonomy.
Hardcoded System Limits
Budgets, permissions, and restrictions are often embedded directly into application logic. Updating policies becomes difficult, brittle, and hard to scale across workflows.
Post incident logging
Most observability systems only explain what happened after the damage is already done. They do not stop unsafe actions in real time.
Temporary Internal Guardrails
Many teams rely on lightweight wrappers and internal scripts to control agents. These solutions are difficult to maintain and break as agents become more autonomous and widely deployed.
A runtime control layer
for AI agents in production.
Hyperon sits between AI agents and external systems, evaluating every action before it executes. AI Agent → Hyperon Layer → APIs / Payments / Tools Instead of relying on hardcoded limits or post-incident monitoring, Hyperon applies real-time policies directly in the execution flow.
Approve Actions
Automatically allow safe actions that match defined policies and spending rules.
Block Unsafe Behavior
Stop unauthorized actions, destructive operations, and unsafe tool usage before execution.
Human approval workflows
Pause high-risk actions and route them for manual review when additional approval is needed.
Spending controls
Set budgets, transaction limits, and usage caps across agents, sessions, or workflows.
Detect unusual behavior
Identify loops, repeated failures, abnormal usage patterns, and unexpected agent behavior in real time.
Full audit trails
Record every action, decision, and policy event for visibility, debugging, and compliance.
Built for real world Agent
Environments.
Hyperon helps teams safely run autonomous agents in production by controlling how agents spend money, use tools, and execute actions in real time.
Prevent runaway spending
Stop loops, repeated retries, and expensive API calls before costs escalate. Set limits on transactions, model usage, and external tool execution.
Enforce financial policies
Apply spending rules across agents, workflows, vendors, and teams. Control budgets, approvals, and transaction limits from a central policy layer.
Secure tool usage
Control which tools and systems agents can access. Restrict sensitive actions, external API usage, and high-risk operations in real time.
Human-in-the-loop
Require manual approval for sensitive financial or operational actions before execution. Add oversight only when higher-risk decisions need it.
From assistants to operators.
The paradigm shift has occurred.
The first generation of AI systems mainly generated text, code, and summaries. Humans still reviewed every output and controlled every action manually. As agents move from passive assistants to autonomous operators, companies need real-time controls before they can safely trust them with money, infrastructure, and critical systems.
Read-Only Assistants
AI systems helped humans generate content and suggestions, but people still stayed in control of execution. The operational risk was low because every important action required human review.
Autonomous Operators
AI agents are beginning to operate independently across tools, APIs, cloud systems, and financial workflows. Without runtime controls, small failures can quickly turn into runaway spending, unsafe actions, or repeated execution loops.
The Shift
Companies are already deploying autonomous agents into production workflows. We believe every production AI agent will eventually require: runtime policy enforcement, spending controls, approval systems, and continuous monitoring. That is the infrastructure Hyperon is building.