AI Agents

Understand what AI agents are, how they differ from chatbots, and when agentic behavior is appropriate.

Core Concepts

• Goals, observations, actions, and stopping conditions
• Agents versus assistants and deterministic workflows
• Levels of autonomy and human oversight

Architecture and Workflow

A reliable agent separates model reasoning from deterministic application controls. The application validates inputs, authorizes tools, records state, limits execution, observes results, and decides whether to continue, stop, retry, or request human help.

Production Best Practices

• Start with the smallest useful degree of autonomy
• Define success and failure before selecting a framework
• Keep consequential actions behind explicit controls

Key Takeaways

• AI Agents should solve a clearly defined user or business problem.
• Model output is a proposal and must not bypass authentication, authorization, validation, or approval.
• Evaluate task success, safety, latency, reliability, and cost before increasing autonomy.
• Logs, traces, budgets, timeouts, recovery paths, and human escalation are part of the architecture.