Day 5 of Becoming an AI Engineer | Building a Deep Research Agent with Multi-Agent AI 🚀
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.
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🔧 Tech Stack Used
OpenAI Agents SDK
Pydantic
Gradio
SendGrid
asyncio
python-dotenv
GPT-4o-mini
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🧠 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
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⚡ 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.