Usman Salis
⬤ OpenAI and Anthropic have pulled back the curtain on how they architect production-ready enterprise AI agents, with safety and controlled execution at the core. The playbook breaks down how large-scale AI systems screen user input before it ever reaches language models or triggers real-world actions. The framework uses a multi-layered workflow specifically designed to stop unsafe or malicious requests in their tracks.
⬤ Here's how it works: user input gets filtered through several protection layers first—moderation APIs, rules-based safeguards like character limits, blacklists, and pattern matching. These mechanisms decide whether a request is safe before sending it to the main language model. If something fails these checks, the system either throws an error message or shuts down completely, making sure problematic instructions never move forward.
This separation allows systems to assess relevance and safety independently from response generation.
⬤ The framework also uses specialized language models dedicated solely to safety classification, running separately from the primary reasoning models. When requests get the green light, the agent either responds directly to the user or moves forward with a controlled function call. For sensitive actions like processing refunds, the system hands things off to specialized agents for extra oversight.
⬤ This playbook shows how production systems are being built with clear guardrails and decision checkpoints baked in. By structuring agent workflows around safety validation and controlled execution, OpenAI and Anthropic are setting the standard for reliability and risk management as AI agents roll out across real-world business operations.
Usman Salis
