Day 5 of Becoming an AI Engineer | Building a Deep Research Agent with Multi-Agent AI 🚀

Description

Today was the biggest build so far. I created a **Deep Research Agent** — a multi-agent AI system that can: → Take a research query → Plan web searches autonomously → Execute searches in parallel → Synthesize findings into a detailed report → Email the final report automatically Built entirely using the OpenAI Agents SDK + structured outputs with Pydantic. --- ## 🔧 Tech Stack Used * OpenAI Agents SDK * Pydantic * Gradio * SendGrid * asyncio * python-dotenv * GPT-4o-mini --- ## 🧠 How the System Works The workflow consists of multiple agents collaborating together: 1. Planner Agent Creates 5 targeted search queries from the user prompt. 2. Search Agent Runs web searches in parallel using `asyncio` and summarizes findings. 3. Writer Agent Synthesizes all search results into a detailed markdown research report. 4. Email Agent Converts markdown → HTML and sends the report via SendGrid --- ## ⚡ Key Concepts Learned * Multi-agent orchestration * Parallel execution with `asyncio` * Structured outputs using Pydantic * Function tools in OpenAI Agents SDK * Why typed outputs matter in agentic systems * Tool calling and enforced workflows * Streaming outputs to a Gradio UI * Tracing multi-agent systems for debugging One of the most interesting parts was seeing how Pydantic acts as a **contract layer** between LLM outputs and Python code — making agent systems much more reliable. GitHub Repository - In comments What’s Next The goal is no longer just calling APIs. Now it’s about building systems where multiple agents collaborate, use tools, validate outputs, and complete workflows autonomously. Day 6 next.