What it is
An AI agent proxy is a network layer that sits between an agent process and the LLM APIs it calls. Instead of the agent connecting directly to Claude, OpenAI, or other LLM endpoints, it connects to the proxy. The proxy forwards requests to the LLM, reads every tool call in the response, inspects it against policy rules, and decides whether to return it to the agent or block it.
The proxy is out-of-process enforcement: it lives outside the agent's trust boundary and the agent cannot disable, bypass, or modify it. This is fundamentally different from in-process SDK approaches, where the governance library runs inside the agent's process and the agent could theoretically ignore or circumvent it.
Why it matters
Proxy architecture offers tamper resistance. A compromised agent, a malicious team member with code access, or an agent that has escalated privileges cannot disable the proxy to bypass policy enforcement. The proxy enforces governance at the network boundary, making it architecturally impossible to bypass.
Proxies are also framework-agnostic. They don't care whether you're running Claude, GPT-4, Llama, or a custom model — they only understand the LLM response format. A single proxy deployment can govern agents across multiple frameworks, languages, and architectures.
How it works
The agent is configured to send API requests to the proxy endpoint instead of the LLM provider's endpoint. The proxy receives the request, forwards it to the actual LLM API, and waits for the response. When the response arrives (containing tool calls), the proxy parses it, evaluates each tool call against the deny list and policy rules, and decides whether each call should be forwarded to the agent.
If a tool call is allowed, it's returned as-is. If denied, the proxy returns an error response indicating that the action was blocked. If escalation is needed, the proxy pauses the response and notifies the human-in-the-loop system. The agent receives a structured response and continues normally.
How Intercis implements it
Intercis is deployed as an out-of-process proxy that your agents point at instead of the Anthropic or OpenAI API. No code changes to the agent, no SDK imports, no framework-specific integration. The agent's LLM client configuration changes from api.anthropic.com to your Intercis endpoint, and governance is active.
We maintain an immutable audit trail of every interception event. We evaluate tool calls against 78 regex patterns across 16 threat categories. We support severity-based escalation, per-agent rate limiting, and per-agent tool allowlists. All policy decisions are logged and exportable as CSV for compliance workflows.
Related terms
- Tool Call Interception — The process of inspecting tool calls at the proxy.
- AI Agent Governance — The broader discipline the proxy enables.