Zero Trust for autonomous AI
Every AI action needs authorization.
AgentWall makes deterministic allow-or-deny decisions between AI agents and the MCP tools, APIs, SaaS, or cloud resources they can use.
Policy decisions happen outside the model—where they can be explicit, reviewed, and enforced.
Policy decision
Delete user request
Rule violation
DELETE > 10 USERS = BLOCK
The enforcement layer
A clear decision at the point of action.
AgentWall sits in the trust boundary between autonomous reasoning and enterprise execution.
Identify the actor and the action.
Capture the agent identity, requested tool, scope, environment, and policy-relevant context before execution.
Evaluate explicit policy.
Authorize the exact action—not a prompt, prediction, or best-effort instruction.
Allow, deny, or stop.
Block unsafe requests, enforce limits, and use a kill switch when a workflow needs to stop.
Retain the decision trail.
Give teams a readable record of requests, decisions, and the policy context behind them.
Free attack surface scanner
Start with the agent trust boundary you have.
Paste an MCP configuration, agent configuration, or tool manifest to map declared actions and begin the conversation with your security team.
Run the scannerAgent security score
High risk
Private pilot
Bring a real workflow. Leave with a clearer control plane.
The AgentWall private pilot is a focused engagement for teams evaluating deterministic authorization around an existing AI agent or MCP-connected workflow.
01
Map
Review the agents, tools, and actions that define the current exposure.
02
Model
Translate intended controls into a small set of understandable policies.
03
Decide
Validate where deterministic authorization belongs in the execution path.
Private-pilot availability
Scope, deployment model, and commercial terms are defined with your team.
Questions