To manage security risks, treat AI agents like new employees. Provide them with their own isolated environment—separate accounts, scoped API keys, and dedicated hardware. This prevents accidental or malicious access to your personal or sensitive company data.

The new Codex app is designed as an "agent command center" for managing multiple AI agents working in parallel. This interface-driven approach suggests OpenAI believes the developer's role is evolving from a hands-on coder into a high-level orchestrator, fundamentally changing the software development paradigm.

Avoid storing sensitive data like contracts directly within your custom-built agent. Instead, use "agent hopping": have the AI call APIs to a secure system of record, like Salesforce, to access data on-demand. This adds a crucial security layer and prevents data liability.

Trying to secure AI agents by restricting which tools are exposed in the Model Context Protocol (MCP) is the wrong approach. Security should be implemented at the API layer itself using robust, granular permissions like OAuth scopes. Treat the AI agent as any other third-party application accessing your API.

Claude Code's "AutoMode" uses one AI to check if another AI's proposed actions are safe, replacing constant user permission prompts. This is more secure than relying on users prone to "yes-fatigue" and simultaneously creates a better, more seamless user experience.

"Skills" are not just documentation; they are reusable, machine-readable instruction manuals. They teach the broader Codex ecosystem how to properly interact with your app's "safe actions." Neglecting to create skills prevents other agents from effectively and autonomously using the application you've built.

To ensure AI agents are trustworthy and can work together safely, Dreamer's architecture includes a central "Sidekick" that acts as a kernel. It manages permissions and communication between agents, preventing uncontrolled data access and ensuring actions align with user intent, much like a computer's operating system.

The three core concepts of Codex Sites work as an integrated system. 'Memory' (a database) stores the state, 'Safe Actions' provide the approved methods for changing that state, and 'Skills' teach other AI agents how to properly use those actions. All three are required to achieve a fully autonomous application.

A new, critical metric for evaluating software is how 'agent-friendly' its API is. This goes beyond traditional developer documentation and ease of use. It focuses on factors like rate limiting, security, and structure that are crucial for building reliable, autonomous AI agents on top of the platform.