This course offers a practical, non-technical guide designed specifically for product managers, tech leads, and strategy heads who want to turn AI buzzwords into real impact.
Agentic AI is more than just chatbots and copilots. It represents a major shift in how software operates—from reactive prompts to autonomous, goal-seeking agents that can plan, decide, and act across tools and systems.
This course provides a strategic roadmap to help you understand, evaluate, and implement Agentic AI in your products, platforms, and workflows—without needing to write a single line of code.
In this six-week structured guide, you’ll gain clarity on:
- What Agentic AI is and how it differs from traditional AI and LLM-based copilots
- Key agent components: tools, memory, goals, reflection
- Design patterns: single-shot, reactive, planning-based, multi-agent systems
- The agent ecosystem: LangGraph, BeeAI, CrewAI, Autogen
- Tool execution with plugins, APIs, and MCPs
- Understanding GPT-4o, Claude, and open-source or local LLM options
- No-code and low-code platforms for agent building
- Internal agents for research, data synthesis, and roadmapping
- Customer-facing agents for onboarding, demos, and support
- Agents for DevOps, data monitoring, and documentation
- Frameworks like the Use Case Canvas to identify agent opportunities
- Single vs multi-agent architectures and when to use them
- Orchestration models: parallel, sequential, hierarchical
- Agent roles: researcher, coder, planner, critic
- Integrations with tools like Slack, Notion, CRMs, GitHub
- Planning pilot projects with measurable KPIs
- Defining team roles and readiness
- Legal, ethical, and governance frameworks for agentic systems
- Scaling your agentic stack responsibly
You’ll walk away with a reusable playbook and templates to start building agent strategies for your team or product.
