Your AI Agents Are Ungoverned.
Until Now.
Deploy, manage, and secure any AI agent — from any framework, any model — with built-in OS-level execution protection. Works alongside NVIDIA NemoClaw, OpenShell, and leading agentic platforms.
AI Agent Threats Are Real — And Accelerating.
AI Agents Are Proliferating.
The Control Gap Is Growing.
By end of 2026, 40% of enterprise apps will embed autonomous AI agents. These agents deploy across teams, spawn sub-agents, act on production infrastructure, and run up costs — with no operational layer to see, govern, or stop them at scale. The result is an expanding attack surface that traditional security tools were never designed to address, and an operational blind spot that grows with every new agent deployment.
You have no operational control over your agent fleet.
- No central registry of deployed agents across teams and clouds
- No visibility into which agents are running, where, or at what cost
- Agent deployment is ad hoc — no lifecycle management or version control
- A single runaway job can exhaust an LLM budget in hours with no alerting
- No mechanism to rapidly suspend agents across hybrid environments
Your agents are operating outside defined boundaries.
- Agents deviate from intended behavior over time — Execution Drift, where autonomous agents stray from intended operational, security, or cost boundaries, is a Tier-1 risk
- No enforcement layer between agent intent and OS-level action
- Over-permissioned agents can access credentials, filesystems, and external networks
- Audit trails may not support auditability for SOC 2 and emerging EU AI Act governance needs
- Security posture of sandboxed agents is invisible to the SOC
Every Agent Action Evaluated
Before It Reaches the OS.
JetPatch works with any execution enforcement engine — but you don't need one to get started. Execwall, our proprietary OS-level enforcement engine, ships with the platform. It intercepts shell commands, network calls, and tool invocations before they reach infrastructure — evaluating every argument, flag, path, and value against policy in real time.
How Execwall Enforcement Works
When an AI agent issues a command — whether it is a shell execution, a network request, a file system operation, or a tool invocation through an MCP protocol — Execwall intercepts it before the operating system processes it. The engine parses the full command structure: the binary being called, every flag and argument, the target paths, environment variables being accessed, destination hosts and ports, and the payload size. Each element is evaluated against the policy rules configured for that agent's role and risk tier.
If the action falls within policy boundaries, it passes through instantly with zero perceptible latency. The execution is logged with the agent identity, the full command, the policy evaluation result, and a tamper-proof timestamp — creating the audit trail that SOC 2 Type II and EU AI Act governance require. If the action violates any policy rule — a forbidden flag, a restricted path, an unauthorized network destination, an injection pattern, or an environment variable access attempt — the command is blocked before it reaches the OS, a SOC alert is triggered, and the incident is logged with full forensic detail.
This approach is fundamentally different from prompt-layer security, which evaluates what an agent says it will do. Execwall evaluates what the agent actually attempts to execute. A jailbroken agent that has bypassed all prompt-level guardrails still cannot execute a forbidden command — because Execwall operates below the prompt layer, at the point where intent becomes action. This is why JetPatch positions Execwall as the last line of defense between autonomous AI agents and your production infrastructure. No other platform provides this level of OS-level enforcement for AI agent operations.
Deploy Execwall as your Execution Firewall. Arg-level OS enforcement from day one — no additional tooling required.
JetPatch sits above it as the AI Agent Cockpit — adding fleet management, fleet-wide kill switch, budget governance, and SOC reporting without replacing your existing layer.
Run OpenShell and Execwall together in the same environment. Multiple enforcement layers, unified visibility and policy management from the cockpit.
>_ Execution Examples
rm file.tmp ✓ rm -rf / ✗cp /workspace/* ✓ cp ~/.ssh/* ✗Block | ; && $() ` ✗AWS_SECRET / *_TOKEN ✗Network Examples
github.com/repos/* ✓ github.com/user ✗Block POST >10KB external ✗Block 10.0.0.0/8 ✗HTTPS only ✓ FTP/SSH ✗Get a tailored walkthrough showing how JetPatch secures AI agents across your specific infrastructure — in under 30 minutes.
Deploy. Control. Patch. Kill.
From One Console.
Truly framework and model agnostic — no vendor lock-in. JetPatch works with every agent framework and every LLM your enterprise runs today or deploys tomorrow.
Set the Budget Once.
The Cockpit Enforces It Automatically.
As spend thresholds are hit, JetPatch auto-downgrades to lighter models — with zero markup via OpenRouter pass-through pricing. CIOs define the limits. The AI Agent Cockpit makes sure they hold.
Autonomous CPU Management
for Local Model Workloads.
When agents run on local models, JetPatch dynamically manages CPU utilization — scaling throughput up or down to maintain peak performance without overloading your infrastructure.
Trusted by Enterprise IT and Security Teams
"This kind of solution can reduce so much complexity in managing the lifecycle of your applications."
"JetPatch provided real WOW factor as it innovated our entire vulnerability and patch remediation process. We met required compliance and significantly reduced our risk exposure."
"Using the JetPatch platform, we were able to quickly and efficiently extend modern management services to our large customer base with smart automation."
AI Agent Security — Technical Details
What is an AI Agent Execution Firewall?
An execution firewall intercepts and evaluates every action an autonomous AI agent attempts before it reaches the operating system. Unlike prompt-layer filters that only see intent, an execution firewall inspects actual shell commands, network calls, file operations, and tool invocations — blocking dangerous actions in real time while allowing legitimate operations to pass through with a tamper-proof audit record.
What AI agent frameworks does JetPatch support?
JetPatch is framework and model agnostic. It works with NVIDIA NemoClaw, OpenShell, Claude-based agents, CrewAI, Devin, and any other agentic platform. The Execwall enforcement engine operates at the OS level, so it secures agents regardless of which framework or LLM they use.
What is the difference between Execwall and prompt-layer security?
Prompt-layer security evaluates what an agent intends to do based on its text output. Execwall evaluates what the agent actually does at the OS execution layer — inspecting every argument, flag, path, and value of shell commands and network calls. This catches threats that prompt-layer filters miss, such as command injection, credential exfiltration, and privilege escalation.
Can JetPatch control LLM costs across an AI agent fleet?
Yes. JetPatch includes LLM budget governance that automatically downgrades agents to lighter models as spend thresholds are reached — from premium frontier models to standard, economy, and local models. CIOs set the budget limits and the AI Agent Cockpit enforces them automatically with zero markup via pass-through pricing.
Does JetPatch support SOC 2 and EU AI Act compliance for AI agents?
JetPatch provides tamper-proof audit trails for every agent action, including allowed and denied executions. This supports auditability requirements for SOC 2 Type II and emerging EU AI Act governance needs. Every action is logged with the agent identity, the command attempted, the policy evaluation result, and a timestamp.
Take Control of Your AI Agent Fleet.
Talk to our team — get a tailored walkthrough of the AI Agent Cockpit in your environment in under 30 minutes.