This project demonstrates a multi-agent AI system built using the CrewAI framework. The goal is to create a collaborative environment where multiple intelligent agents work together to accomplish complex tasks such as research, content generation, and evaluation — all orchestrated in a structured workflow.

The system applies concepts learned from Module 2 of the Agentic AI Developer Certification Program, focusing on transitioning from traditional workflows to intelligent multi-agent orchestration.

🔹 System Architecture

The architecture follows a modular design:

Research Agent – Collects and synthesizes information using external tools.

Content Agent – Generates written outputs and summaries based on the research agent’s findings.

Reviewer Agent – Evaluates the quality, coherence, and accuracy of the final content.

Crew Orchestrator – Manages task flow between agents using the CrewAI Crew and Process classes.

Each agent has a specific role, goal, and backstory, which ensures autonomous yet coordinated behavior.

(A visual architecture diagram is attached below to illustrate the system flow.)
→ [Add your own diagram here – e.g., made in Canva or draw.io]

🔹 Tools and Frameworks

CrewAI – for agent creation, orchestration, and communication

LangChain – for structured prompt and tool integration

SerperDevTool – for live web search capabilities

OpenAI API – as the underlying LLM for reasoning and generation

Python (3.13) – main programming language

dotenv & pydantic – for environment variable handling and validation

🔹 Rationale for Design Choices

CrewAI was chosen for its clear agent orchestration capabilities.

SerperDevTool provided reliable and structured search outputs.

FAISS (if later integrated) would handle memory and retrieval tasks efficiently.

Modular design ensures easy scalability — new agents can be added without altering existing code flow.

🔹 Key Learnings

Transitioning from single-task workflows to collaborative multi-agent systems.

Designing and orchestrating role-based intelligence using CrewAI.

Applying evaluation metrics to measure agent performance.

🔹 Future Improvements

Integrate a retrieval-augmented memory (RAG) component.

Add a UI layer using Streamlit to visualize outputs.

Expand with more specialized agents (e.g., QA agent, summarizer, data visualizer).