Report Abuse

Basic Information

Agentic AI is a small proof-of-concept repository and curriculum-style project organized as a week-by-week plan to explore building agentic systems. The README outlines eight weekly focuses that progress from building simple ReAct and chain-of-thought agents that can take actions and call tools, through adding external API/tool use and integrating vector databases for retrieval-augmented generation, to architecting short-term and long-term memory. Later weeks cover multi-agent simulations using CEO-Engineer-Researcher roles, deploying an agent server with FastAPI and GKE, and adding auto-evaluation and safety features. The final week aims to combine these components into a full multi-agent assistant such as a personal knowledge manager or an AI employee. The repo serves as a structured guide and proof-of-concept roadmap for someone experimenting with agent design, retrieval, memory, orchestration, deployment, and evaluation.

Links

Categorization

App Details

Features
The README enumerates several core features and focus areas rather than listing files or APIs. It emphasizes ReAct and chain-of-thought agents that can perform actions and call tools. It highlights staged integration of external APIs and tool use for agent capabilities. It specifies use of a vector database and retrieval-augmented generation for private document fetching. It calls out memory architecture with short-term and long-term components. It describes multi-agent system patterns, notably a CEO-Engineer-Researcher simulation. It includes deployment considerations using FastAPI and Google Kubernetes Engine. It also includes auto-evaluation and safety/self-grading agent topics and culminates in building a full multi-agent assistant project.
Use Cases
This repository provides a concise, staged roadmap for exploring and prototyping agentic AI concepts. By breaking development into weekly focuses, it helps practitioners learn incrementally from basic action/tool-enabled agents to more advanced topics like RAG, memory systems, and multi-agent orchestration. The inclusion of deployment topics such as FastAPI and GKE signals practical guidance for running agents in production-like environments. The multi-agent CEO-Engineer-Researcher pattern and the emphasis on auto-evaluation and safety help users design workflows, roles, and testing for complex agent interactions. Overall it is useful as a teaching scaffold and proof-of-concept reference for developers and researchers building multi-agent assistants or personal knowledge managers.

Please fill the required fields*