AI curriculum
Course outline for Agentic AI Foundations
Not every AI task needs an autonomous agent. Compare agents with fixed workflows and design a bounded agent loop with clear human oversight.
About Agentic AI Foundations
Not every AI task needs an autonomous agent. Compare agents with fixed workflows and design a bounded agent loop with clear human oversight.
Agentic AI Foundations Course Objectives
- Distinguish agents from deterministic workflows.
- Describe planning, memory, and tool use in an agent loop.
- Define stop conditions and human escalation rules.
Pre-requisites
- Familiarity with generative AI.
- Basic understanding of tasks and process steps; coding is optional.
Lab Setup
- Computer with a browser and a diagram or text editor.
- Use supplied tool-call examples or a local agent simulator with synthetic inputs.
Detailed Course Outline
Proposed modulesModule 1: Choosing an execution model
- What an agent is
- Agents vs workflows
- Tool-using agents
Practical outcome: Decide whether a sample task needs an agent.
Module 2: Managing agent state
- Planning
- Memory
- Reflection
Practical outcome: Sketch a plan and memory policy for a bounded task.
Module 3: Keeping execution controlled
- Agent loops
- Human oversight
- Failure modes
Practical outcome: Define stop and escalation conditions.
Practical exercise
- Simulate an agent loop and document when a person must intervene.
How we train
Contact us for full course details, including duration, delivery options and lab requirements.
Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.
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