AWS shows how to build an AI research assistant in roughly 30 lines with Strands Agents and Kiro

AWS demonstrated that developers can prototype a working AI research assistant with roughly 30 lines of Python by combining the open-source Strands Agents SDK, AWS services, and the Kiro IDE. The example reduces the usual overhead of orchestrating APIs, managing conversation state, and building agents that reason autonomously, making it easier to move from experiment to deployable prototype.

Strands Agents is presented as an open-source SDK that blends large language models with custom Python logic. The framework lets developers expose external functions and APIs via an @tool decorator, supports real-time streaming responses for interactive workflows, and is released under the Apache-2.0 license. AWS teams already run the same codepath in production in services such as Amazon Q and AWS Glue, demonstrating the SDK’s viability beyond experiments.

Kiro is an AI‑driven IDE that accelerates developer workflows by packaging reusable capabilities into Kiro Powers. A Strands power bundles SDK documentation search, getting‑started guides, correct API patterns and the supporting files needed to run examples (MCP servers, steering files, and hooks). AWS says there are more than 50 curated Powers from AWS, partners and the community that can be installed with a single click, shortening setup and reducing boilerplate.

The post lists concrete prerequisites and an initial setup flow for builders: an AWS account, a user configured in AWS IAM Identity Center or Builder ID, installing Kiro, and configuring AWS credentials to access Amazon Bedrock. It recommends running aws configure sso and aws sso login --profile research — assistant, and instructs attaching a scoped inline IAM policy that grants only the permissions required to invoke the tutorial’s example model (the Claude Sonnet model via Amazon Bedrock).

In AWS’s stack, Amazon Bedrock serves as the entry point to foundation models that can power agents, Kiro provides IDE scaffolding, and Strands simplifies orchestration and agent logic between those layers. The framework is model‑agnostic and can operate with providers such as Amazon Bedrock, Anthropic and OpenAI, enabling a prompt‑plus‑tools design where the LLM handles planning and tool usage rather than extensive hardcoding.

For builders the practical implications are concrete: Strands aims to lower the barrier from research idea to deployable app by supporting single agents, multi‑agent networks and hierarchical systems; integrating with AWS services like Lambda; and keeping the same codepath for local development and production. Together, Kiro Powers, model‑driven agent logic, streaming responses and community contributions are positioned to speed prototyping and create a clearer path to production‑ready agentic applications.