From idea to AI app: Creating intelligent research assistants with Strands

2026-05-26 · Source: Artificial Intelligence · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

This article details building an intelligent AI research assistant using Strands Agents and AWS services like Amazon Bedrock and Kiro. Strands Agents, an Apache-2.0 licensed open-source framework, simplifies AI application development by enabling large language models (LLMs) to handle autonomous reasoning and planning with minimal code, supporting various LLM providers including Amazon Bedrock, Anthropic, and OpenAI. The author demonstrates creating a functional research assistant in just 30 lines of Python code, integrating with Streamlit for a web interface. Kiro, an AI-powered IDE, further accelerates development by generating the complete Streamlit application code from natural language prompts, including specific requirements for the research report structure and content. The process involves configuring AWS IAM permissions for Bedrock and installing necessary SDKs.

Key takeaway

For AI Engineers or Software Engineers building agentic AI applications on AWS, Strands Agents significantly reduces development complexity. You can create sophisticated research assistants or similar agents with minimal Python code, leveraging LLMs for autonomous reasoning and Kiro for AI-assisted code generation. Prioritize configuring AWS IAM permissions and Bedrock Guardrails, and implement cost caps and input validation for production readiness.

Key insights

Strands Agents and Kiro simplify AI agent development, enabling complex applications with minimal code and LLM-driven reasoning.

Principles

• LLMs can manage agent logic and tool usage.
• Model-driven AI development reduces hardcoding complexity.
• Open-source frameworks enhance AWS AI integration.

Method

Initialize a Strands Agent, define behavior via prompt engineering, add tools for autonomous research, and process responses for clean output, often with AI-assisted code generation.

In practice

• Build AI research assistants with 30 lines of code.
• Use Kiro to generate Streamlit AI app code.
• Integrate external functions via the "@tool" decorator.

Topics

• Strands Agents
• AI Agents
• Amazon Bedrock
• Kiro IDE
• LLM Development
• Streamlit Applications

Code references

• kirodotdev/powers

Best for: AI Engineer, Software Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.