AI agents are increasingly used as autonomous systems that can call tools, access data, and take actions in real environments. As these systems gain more autonomy, ensuring their safe and predictable behavior becomes an engineering challenge rather than a prompting problem.

This talk examines how safety constraints can be explicitly enforced in agentic AI systems, instead of relying solely on natural language instructions or model alignment. We will discuss typical safety and security issues that arise in agent based architectures, including over permissioned tools, unintended action chains, goal drift, and unsafe retries.

Using practical Python examples, the talk introduces architectural patterns for constraining agent behavior, such as policy layers, capability based tool access, action budgets, and runtime validation of agent decisions before execution. We will also explore how human in the loop checkpoints and audit logging can be integrated into agent workflows to support safer operation in production environments.

The focus of this session is on practical design and implementation techniques that help developers build AI agents with clearly defined boundaries, making their behavior more controllable, observable, and secure

Through practical Python examples, we will demonstrate how to:

• Design constrained agent architectures
• Enforce tool level permissions and action budgets
• Validate and block unsafe agent actions at runtime
• Combine human-in-the-loop checkpoints with automated controls