2025-11-17 · Authensor

AI Agent Action Interception Explained: How SafeClaw Sits Between Agent and System

An AI agent decides to write a file. Between that decision and the file appearing on disk, something needs to happen: the action needs to be evaluated against a security policy. If it passes, the write proceeds. If not, it is blocked. The agent receives a denial response instead of a success confirmation.

This is action interception -- the ability to sit between an AI agent and the system it operates on, inspecting and gating every action before execution. It is the fundamental mechanism that makes AI agent security possible. Without it, you are trusting the agent to police itself, which, as the Clawdbot incident demonstrated (1.5 million API keys leaked in under a month), is not a viable strategy.

This article explains how action interception works, the architectural patterns that enable it, and how SafeClaw implements it across Claude, OpenAI, and LangChain agents without requiring modifications to the agents themselves.

The Interception Problem

AI agents interact with systems through tool calls. When a Claude agent wants to read a file, it emits a tool call describing the action. The runtime environment executes the tool call and returns the result. The agent processes the result and decides its next action.

The interception point is between the tool call emission and the tool call execution. This is where a security layer can inspect the action, evaluate it against a policy, and decide whether to allow it.

The challenge is that different agent frameworks have different architectures for tool execution:

Claude (Anthropic API): Tool calls are returned as part of the model response. The application code is responsible for executing them.
OpenAI (function calling): Similar model -- the API returns function call descriptions, and the application executes them.
LangChain: Tools are registered with the agent, and LangChain's runtime handles invocation through its executor pipeline.

A viable interception mechanism must work across all of these architectures. It cannot require modifying the agent's prompts, the model's behavior, or the framework's internal code.

Architectural Patterns for Interception

There are several architectural patterns for intercepting agent actions. Each has tradeoffs.

Pattern 1: Proxy Layer

A proxy sits between the agent runtime and the system. All tool calls are routed through the proxy, which inspects each call before forwarding it.

Agent Runtime  -->  Proxy (SafeClaw)  -->  System (filesystem, shell, network)

The proxy receives the tool call description, evaluates it against the policy engine, and either forwards the call to the system or returns a denial response to the agent runtime.

Advantages:

Agent is unaware of the proxy. No modifications required.

Works with any agent framework that supports tool execution.

Clear separation of concerns.

Disadvantages:

Requires routing tool calls through the proxy.

Must handle the tool call interface for each action type.

Pattern 2: Wrapper Functions

Each tool function is wrapped with a security check. The wrapper evaluates the action before calling the original function.

Original tool:    writeFile(path, content) -> result
Wrapped tool:     secureWriteFile(path, content) -> evaluate(policy) -> writeFile(path, content) | deny

Advantages:

Fine-grained control over each tool.

Can access full action context including parameters.

Disadvantages:

Requires wrapping every tool function.

Tightly coupled to the specific tool implementations.

Pattern 3: Middleware Pipeline

The interception layer is inserted into the agent's execution pipeline as middleware, similar to HTTP middleware in web frameworks.

Agent Decision  -->  Middleware (SafeClaw)  -->  Tool Execution  -->  Result

Advantages:

Clean integration with frameworks that support middleware (like LangChain).

Single integration point for all tools.

Disadvantages:

Not all frameworks have middleware concepts.

Middleware ordering can be complex.

How SafeClaw Implements Interception

SafeClaw uses a combination of these patterns, adapted to each supported framework. The key design principle is that the agent itself is never modified. SafeClaw operates at the tool execution layer, not at the agent decision layer.

The Interception Flow

Here is the complete flow when an agent attempts an action:

1. Agent decides to perform an action (e.g., write a file)

Agent emits a tool call with action type and parameters
SafeClaw intercepts the tool call before execution
SafeClaw constructs an action description:
   {
     type: "file_write",
     path: "/workspace/output.txt",
     content: "...",
     agent: "claude-agent-01",
     timestamp: "2026-02-13T10:00:00Z"
   }

Policy engine evaluates the action against the ruleset
Decision is recorded in the cryptographic audit trail
7a. If ALLOW: tool call is forwarded to the system, result returned to agent
7b. If DENY: tool call is blocked, denial response returned to agent

Steps 4 through 6 happen in sub-millisecond time. The agent experiences negligible latency overhead.

Framework-Specific Integration

Claude (Anthropic API):
When using Claude's tool use API, the application code receives tool call descriptions in the model response. SafeClaw integrates at the point where these tool calls are executed. The application's tool execution handler passes each tool call through SafeClaw before performing the actual operation.

OpenAI (function calling):
OpenAI's function calling follows the same pattern. The API returns function call descriptions, and the application code handles execution. SafeClaw intercepts at the execution handler level.

LangChain:
LangChain provides a more structured agent execution pipeline. SafeClaw integrates as a component in the tool execution chain. When LangChain's executor invokes a tool, SafeClaw evaluates the action before the tool function runs.

In all three cases, the integration point is the same: the moment between "agent decides to act" and "system executes the action." SafeClaw does not modify the agent's decision-making process, its prompts, or its model configuration. It only controls what happens when the agent's decisions reach the system.

What Gets Intercepted

SafeClaw intercepts three categories of actions:

File operations (file_write):
Any operation that creates, modifies, or deletes files. The intercepted data includes the target path, the operation type (create, modify, delete), and the content being written. The policy engine can gate based on path patterns, file size, content patterns, and more.

Shell execution (shell_exec):
Any operation that executes a command in a system shell. The intercepted data includes the full command string. The policy engine can gate based on command patterns, allowing specific commands (e.g., git commit) while blocking dangerous ones (e.g., rm -rf).

Network requests (network):
Any outbound network request. The intercepted data includes the URL, HTTP method, and headers. The policy engine can gate based on URL patterns, domains, ports, and protocols. This prevents data exfiltration and unauthorized API calls.

The Non-Modification Principle

A critical design constraint of SafeClaw is that it does not modify the agent. This means:

No prompt injection. SafeClaw does not add security instructions to the agent's system prompt or user messages. Prompt-based security is brittle and can be circumvented through prompt injection attacks.
No model fine-tuning. SafeClaw does not require a specially trained model that "knows" about security policies. Policies are enforced externally, not internally.
No framework patches. SafeClaw does not monkey-patch framework internals. It integrates through supported extension points and execution handlers.

This principle exists because agent-side security is fundamentally unreliable. An agent's behavior is determined by its model weights, its prompt, and its context. All of these can be manipulated. External enforcement -- gating actions at the system boundary -- is the only mechanism that provides reliable security guarantees.

Handling Denial Responses

When SafeClaw denies an action, the agent receives a structured denial response instead of the expected tool result. This response indicates:

That the action was blocked by the security policy.
Which rule triggered the denial.
The action type and parameters that were evaluated.

Well-designed agents handle denial gracefully. They may attempt an alternative approach, request different permissions, or report the denial to the user. Poorly designed agents may retry the same action repeatedly. SafeClaw's audit trail records all attempts, including repeated denials, providing visibility into agent behavior patterns.

The Control Plane Connection

SafeClaw's interception mechanism operates locally -- the policy engine runs in the same process, evaluation happens in memory, and there are no network round trips for action gating. However, SafeClaw does maintain a connection to the Authensor control plane for policy updates, key management, and audit trail synchronization.

If the control plane is unreachable, SafeClaw does not fail open. The deny-by-default architecture ensures that if the control plane connection is lost and the local policy cannot be verified, all actions are denied. This prevents an attacker from bypassing security by disrupting the control plane connection.

Getting Started with Action Interception

Setting up SafeClaw's action interception takes minutes:

npx @authensor/safeclaw

The setup wizard walks you through configuring policies for file_write, shell_exec, and network actions. The browser dashboard at safeclaw.onrender.com provides real-time visibility into intercepted actions, policy evaluations, and audit trail entries.

SafeClaw offers a free tier with 7-day renewable keys, so you can evaluate the interception mechanism in your own environment before committing. The client is 100% open source, built with zero runtime dependencies, and tested with 446 tests under TypeScript strict mode.

For more on the Authensor framework, visit authensor.com.

Try SafeClaw

Action-level gating for AI agents. Set it up in your browser in 60 seconds.

$ npx @authensor/safeclaw