Local-first control layer
Designed to keep action checks close to the files, commands, tools, and endpoints the agent is trying to use.
Local-first AI action control
Control AI actions before they touch files, systems, or data.
Guardian Gate is a local-first control layer for action-taking AI. It checks sensitive file, command, tool, and network actions before they run.
Built for teams deploying coding agents or tool-using AI into real workflows, where the goal is to move faster without blindly trusting every agent action.
Action decision layer
Action
Write updated payroll summary to
/reports/march.csvPolicy
Allow writes only inside approved workspace paths
Decision
Allowed
Action
Delete customer export archive
Decision
Blocked
Action
Call external billing API with account data
Decision
Approval required
Current control surface
What Guardian Gate controls today.
Guardian Gate is focused on the action points where agents can create real operational risk. Each request can be evaluated before execution and recorded as reviewable evidence.
File reads, writes, and deletes
Check access before agents inspect, change, overwrite, or remove workspace data.
Shell and command execution
Evaluate command intent, target, and scope before the host environment is touched.
Outbound HTTP destinations
Gate external domains and endpoints before data leaves the local workflow.
Protected or hidden paths
Keep sensitive directories, configuration files, and hidden paths outside approved agent scope.
Allow, block, or approval decisions
Let low-risk actions proceed while risky actions can be blocked or escalated for review.
Structured audit evidence
Capture what was requested, what policy applied, and what decision was enforced.
Why teams pilot Guardian Gate
Narrow control, before execution.
Guardian Gate is credible as a pilot because it focuses on a specific control problem: deciding what an agent may do before the action reaches real systems.
Allow, block, or approval decisions
Each sensitive action can be allowed, stopped, or routed to a human review step before it runs.
Policy-based enforcement
Rules are applied at the action boundary rather than relying only on model instructions or post-incident review.
Audit evidence for pilots
Teams can review what was requested, what decision was made, and why that action was allowed, blocked, or escalated.
Architecture
How it fits into an agent stack.
The stack fit is plain: AI or agent -> Guardian Gate policy check -> files, shell, endpoints, and tools. Guardian Gate sits at the point where model intent becomes an action request.
AI / agent
Coding agent, copilot, or workflow assistant requests an action.
Guardian Gate policy check
Evaluate action type, target, protected paths, destination, and approval rules before execution.
Files / shell / endpoints / tools
Only permitted actions reach the local environment, connected tools, or approved destinations.
Example policy controls
What controls can look like.
These examples are intentionally product-facing: simple controls a team can discuss during a pilot, not legal policy text.
Protected paths blocked
Requests to read or write hidden paths such as .env, credentials, or restricted config folders are blocked by default policy.
Destructive deletes require approval
Bulk deletes, workspace cleanup, or removal of shared files pause for review before anything is removed.
Risky shell commands escalated
Commands that can remove data, change permissions, or affect the host are blocked or routed for approval.
Outbound requests limited
HTTP calls are restricted to approved destinations so agents cannot freely send data to unknown endpoints.
Example audit evidence
What a decision record can show.
A pilot should make agent behavior reviewable. Guardian Gate is designed to expose the action request, policy reason, and final outcome.
Requested action
Delete generated files outside the approved workspace
Target
/Users/team/shared_exports/Decision
Approval required
Reason
Destructive action outside approved agent workspace
Approval
Required before execution
Final outcome
Paused for reviewer decision; no files touched
Current scope
What it is and what it is not.
Guardian Gate is best understood as an auditable control layer around agent actions. It is intentionally narrower than a full security platform.
It is a control layer for agent actions
Guardian Gate evaluates action requests before they reach files, commands, tools, or network destinations.
It is not a full OS sandbox
It should not be treated as a replacement for operating-system isolation, endpoint security, backups, or least-privilege infrastructure.
It does not claim to solve every AI security problem
It focuses on policy-based action control, approval routing, and audit evidence for the actions it can observe and enforce.
It adds reviewable boundaries
Risky actions can be blocked or escalated so teams can test safer rollout of action-taking AI with clearer operational control.
What actions it controls
Built for the actions that create operational risk.
For action-taking AI deployed into workflows where file changes, command execution, and outbound requests need control before they run.
Read files
Restrict which directories or file types an AI system can inspect.
Write files
Allow updates only in approved locations and workflow-specific scopes.
Delete files
Block destructive operations or require explicit human approval.
Run commands
Check shell or system execution before it reaches the host environment.
Call websites
Limit outbound requests for copilots and agents to trusted destinations.
Call HTTP endpoints
Apply destination checks before agents send data to internal or external services.
Why a control layer matters
Why buyers ask for action controls.
These examples show what can happen when AI executes without checks on files, systems, or destinations.
Hundreds of emails affected
An AI agent reportedly deleted or archived hundreds of emails.
Even familiar business systems can be affected quickly when autonomous actions are allowed to proceed without policy checks.
2.5 years of data lost
A coding agent reportedly wiped years of stored work.
Write and delete permissions need strong boundaries before agents are allowed to touch valuable project data.
Live database deleted
An AI coding agent reportedly removed a production database.
High-impact actions need an approval or policy checkpoint before an agent can reach live systems.
Unsafe agent behavior
Research has highlighted manipulative and unsafe autonomous actions.
The risk is broader than a single workflow. Teams need controls between AI intent and execution across files, tools, and endpoints.
Isolation was advised
Some agent systems were serious enough that isolated environments were recommended.
Isolation reduces exposure, but teams still benefit from a dedicated decision layer around what actions are allowed at all.
Private data at risk
Reports have described autonomous systems leaking sensitive details or deleting files.
Outbound requests and file operations both need explicit controls when AI is connected to internal or customer data.
Pilot scope
Good first fit.
Guardian Gate is best suited for focused internal pilots where teams already have an agent workflow and want enforceable action boundaries, reviewable approvals, and audit evidence.
Coding agents
Teams testing agents that read code, edit files, run commands, or interact with local development environments.
Tool-using AI workflows
Assistants and copilots that can call tools, move data, update records, or reach internal and external endpoints.
Internal pilot environments
Teams that want to evaluate action control in a bounded workflow before expanding agent access more broadly.
Reviewability-first teams
Buyers who need to see what an agent requested, what was approved, and what was blocked before trusting wider rollout.
Request a pilot discussion
Show us the agent workflow you want to control.
This is a static form. When you submit, your email client will open a prefilled pilot request so you can review and send it directly.
Best fit: teams evaluating coding agents, internal automations, tool-using copilots, approval boundaries, or endpoint restrictions in production-like workflows.
Pilot next step
Evaluate Guardian Gate against a real agent workflow.
Use the demo to see the control model, then request a pilot discussion around the files, commands, tools, and endpoints your AI can reach.