1 The rise of AI agents

By themselves, LLM-based apps, like simple chatbots, can generate responses and answering questions. But these days we want them to make plans and also carry out those plans: we want them to book a flight, not just provide a list of flights, or update a project tracker, not just list the changes that need to be added. To equip AI apps with active abilities, we add AI agents to the system. The agent is not a new concept in machine learning and artificial intelligence, and the term can be a bit ambiguous. But when we talk about intelligent or AI agents, we generally mean software that perceives its environment, decides what to do, and takes action to achieve a goal, using the resources provided by an LLM.

1.1 Defining agents and agentic thinking

1.1.1 Defining agents and agentic thinking

1.1.2 Understanding agent/assistant and LLM patterns

1.1.3 Thinking like agents: sense, plan, act, learn

1.1.4 Agents act with tools

1.2 Introducing the Model Context Protocol (MCP)

1.3 Understanding the five functional layers of an agent

1.3.1 The Agent Persona

1.3.2 Agent Actions & Tools

1.3.3 Agent Reasoning & Planning

1.3.4 Agent Knowledge & Memory

1.3.5 Agent Evaluation & Feedback

1.4 Advancing onto multi-agent systems

1.4.1 The agent-flow assembly line

1.4.2 Agent orchestrations (hub-and-spoke)

1.4.3 Agent collaboration (teams of agents)

1.5 Next steps

1.6 Summary