Build a Research Agent

Welcome to the capstone project of the Agentic AI Workflows learning path! In this module, you'll integrate everything you've learned to build a sophisticated research agent capable of autonomous research, analysis, and reporting with appropriate human oversight. This project demonstrates how all the concepts—agent architectures, planning systems, tool orchestration, and human-in-the-loop design—work together in a real-world application.

By the end of this module, you will:

• Integrate agent architecture patterns, planning, tool orchestration, and human-in-the-loop design
• Build a complete research agent with autonomous research capabilities
• Implement sophisticated planning and goal management for research tasks
• Create robust tool orchestration for web scraping, data analysis, and report generation
• Design effective human oversight and quality control mechanisms
• Deploy a functional research assistant with explainable decision-making

Our research agent will be capable of:

1. Autonomous Research: Planning and executing comprehensive research on given topics
2. Multi-Source Information Gathering: Using web search, academic databases, and APIs
3. Intelligent Analysis: Processing and synthesizing information from multiple sources
4. Quality Control: Fact-checking and validating research findings
5. Human Collaboration: Involving humans at critical decision points
6. Report Generation: Creating structured, professional research deliverables
7. Explainable Process: Providing clear explanations of research methodology and findings

Our research agent follows a hybrid architecture combining reactive and deliberative approaches:

Test your understanding of building research agents:

Question 1

Which architecture pattern is most suitable for a research agent that needs both reactive responses and complex planning?

A) Purely reactive B) Purely deliberative C) Hybrid architecture D) Multi-agent system

Answer: C) Hybrid architecture

Research agents need reactive capabilities for immediate responses and deliberative planning for complex research strategies, making hybrid architecture ideal.

Question 2

What is the most critical intervention point for research quality?

A) Planning phase approval B) Information source credibility validation C) Report formatting review D) Timeline management

Answer: B) Information source credibility validation

Source credibility directly impacts research quality and reliability, making it the most critical intervention point for maintaining research integrity.

Question 3

Which quality metric is most important for research deliverables?

A) Completeness of information coverage B) Speed of research completion C) Number of sources consulted D) Length of final report

Answer: A) Completeness of information coverage

Completeness ensures that research adequately addresses the topic scope and requirements, providing comprehensive and reliable insights.

Time: 60 minutes

Build a simplified research agent with:

1. Basic planning capabilities for research tasks
2. Simple web search and information gathering
3. Basic quality assessment of sources
4. Simple report generation

Exercise 2: Advanced Research Features

Time: 90 minutes

Enhance your research agent with:

1. Multi-source information orchestration
2. Conflict detection and resolution
3. Human intervention points
4. Explainable research methodology
5. Quality control mechanisms

Exercise 3: Complete Research System Deployment

Time: 120 minutes

Deploy a production-ready research agent featuring:

1. Full workflow orchestration
2. Comprehensive quality control
3. Multiple deliverable formats
4. Human review integration
5. Performance monitoring and analytics
6. User interface for research requests

Building a research agent integrates all the concepts from this learning path:

• Agent Architecture: Hybrid design combining reactive and deliberative approaches
• Planning Systems: Specialized research planning with goal management
• Tool Orchestration: Coordinating multiple information gathering and analysis tools
• Human-in-the-Loop: Strategic intervention points for quality control and oversight
• Quality Assurance: Comprehensive validation and fact-checking mechanisms
• Explainable AI: Transparent research methodology and decision-making

This capstone project demonstrates how sophisticated agentic systems can augment human capabilities in complex domains like research, providing both automation and appropriate human oversight.

You have successfully completed the Agentic AI Workflows learning path! You now have the knowledge and skills to:

• Design and implement sophisticated AI agent architectures
• Build planning systems for complex, multi-step tasks
• Orchestrate multiple tools and services effectively
• Integrate human expertise and oversight appropriately
• Create explainable and trustworthy AI systems

These skills form the foundation for building production-ready agentic AI systems that can handle real-world complexity while maintaining reliability, transparency, and human control.

Continue your AI engineering journey with the Production AI Systems learning path, where you'll learn to deploy, monitor, scale, and secure AI systems in production environments.

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• Research Methodology Best Practices
• Information Quality Assessment
• Automated Research Tools
• AI Agent Development Patterns
• Research Ethics and AI