AI Agents and Agentic AI Architecture in Python

Vanderbilt extends an agent workflow into architectural choices. Self-prompting and safety precede multi-agent systems and tool dependency injection, followed by reasoning approaches. Choose this continuation to examine how parts of a system interact rather than only build an initial agent.

What you will learn

• Explain the role of self-prompting in an agent loop.
• Separate tool access from an agent’s implementation.
• Compare reasoning and coordination choices in an agent system.

Who this suits

Learners interested in agent architecture. The provider lists beginner level and no required background.

Before you enroll

The architecture material is easier to assess with some agent and Python practice. Inspect examples involving repeated model calls because latency and usage costs can accumulate.

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Put it into practice

Replace one tool implementation behind a fixed interface. Test whether the agent still follows the same task constraints and record any unexpected additional calls.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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