Research Agent
Highly skilled Data Test Automation professional with over 10 years of experience in data quality assurance and software testing. Proven ability to design, execute, and automate testing across the entire SDLC (Software Development Life Cycle) utilizing Agile and Waterfall methodologies. Expertise in End-to-End DWBI project testing and experience working in GCP, AWS, and Azure cloud environments. Proficient in SQL and Python scripting for data test automation.
When you have independent tasks, you can run them all at the same time using a ParallelAgent. This agent executes all of its sub-agents concurrently, dramatically speeding up the workflow. Once all parallel tasks are complete, you can then pass their combined results to a final 'aggregator' step.
Use Parallel when: Tasks are independent, speed matters, and you can execute concurrently.
Architecture:
Project Structure
Github repository contains the following key files:
File | Purpose |
| Core sequential agent logic |
| Environment variables (API keys, etc.) |
The heart of the project is agent.py, where the agent is defined and configured.
Code Walkthrough:
1. Define three specialized research agents
The code creates TechResearcher, HealthResearcher, and FinanceResearcher.
Each agent:
Uses the Gemini model.
Has a focused instruction (AI trends, medical breakthroughs, fintech trends).
Uses the
google_searchtool to gather information.Stores its output in a unique session key (
tech_research,health_research,finance_research).
These agents act like three domain‑experts.
2. Create an aggregator agent
A fourth agent, AggregatorAgent, is defined.
Its job is to:
Pull the three stored outputs using placeholders
{tech_research},{health_research},{finance_research}.Combine them into a single 200‑word executive summary.
Store the final result under
executive_summary.
This agent acts like the “editor‑in‑chief.”
3. Group the three researchers into a ParallelAgent
ParallelResearchTeam runs the three research agents simultaneously.
This speeds up the workflow because all three reports are generated at the same time rather than one after another.
4. Define a SequentialAgent to control the workflow
root_agent is a SequentialAgent that runs:
The parallel research team
The aggregator agent
This ensures the summary is created after all three research reports exist.
5. Establish the full research pipeline
The combination of:
Parallel research
Followed by aggregation
creates a complete multi‑agent research system that behaves like a coordinated team.
Execution Output:
GitHub Repo:
https://github.com/puthanvipin/parallel-research-agent/tree/main/parallel_research_agent
