Blocking Agent [OFFICIAL]

: Use a "before_agent" method to intercept user requests or an "after_agent" method to scan model responses before they are delivered.

Developing a "blocking agent"—more commonly known as a or middleware agent —is the process of building a specialized AI component designed to monitor, filter, and intervene in the interactions of a primary AI agent. Its core purpose is to prevent "hallucinations," enforce safety policies, and block unauthorized actions (like leaking credentials) before they reach the user or the external environment. Core Architecture for a Blocking Agent blocking agent

: This is the "brain" that analyzes incoming data against your rules. In production systems, this often involves a smaller, faster model (like GPT-4o-mini or Claude Haiku) optimized specifically for classification and risk detection. : Use a "before_agent" method to intercept user

: When a block occurs, the system must handle it gracefully—such as providing a standardized "I cannot fulfill this request" response—rather than just crashing or failing silently. Key Patterns in Modern Agentic Systems How to Build Reliable AI Agents (without the hype) Core Architecture for a Blocking Agent : This

: Explicitly list what the agent is not allowed to do. This might include blocking the output of API keys, preventing the execution of destructive commands (like rm -rf ), or filtering toxic language.

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: The blocking logic should be decoupled from the primary agent. This allows you to update security policies or "constitutions" without having to retrain or reconfigure the main task-oriented agent. Step-by-Step Development Process