What I can do is help you:

Short-term memory tracks the state of the current mission. It records which sub-tasks have been completed, what data was retrieved from an API five minutes ago, and what obstacles the agent encountered during its current run. Long-Term Memory

The "Agentic AI Bible" represents a shift from AI as a to AI as a coworker . It argues that the future of AI isn't just bigger models, but smarter ways to let models plan, use tools, and critique themselves. This workflow allows smaller, cheaper models (like GPT-3.5 or open-source Llama models) to outperform "smarter" models (like GPT-4) simply by allowing them to think longer and retry harder.

Ideal for setting up role-based multi-agent teams that collaborate like human departments.

that power modern agentic systems, enabling readers to design agents with reasoning, memory, and planning from the ground up.

We are moving from the era of Chatbots (which talk) to the era of Agents (which act).

Implement a structured ReAct loop where the agent assesses its state, checks for errors, and decides whether to query a tool or finish the task.

Dynamic scrapers (e.g., Playwright) that allow agents to search the live web, navigate authentication walls, and extract real-time data.

If you secure the extra quality PDF, here is the intellectual treasure map you will uncover: