Tag

Runs AI-generated code in secure, isolated cloud sandboxes you control via Python or JavaScript SDKs; supports self-hosting (Terraform) and AWS/GCP, enabling agents and code-interpreting workflows to execute real-world tools safely.

Terminal-native agentic CLI to chat with Amazon Q Developer, run and explain shell commands, and operate on local code via natural language. Integrates local repo/shell context, supports agent actions and multiple model backends; repo later marked as maintenance-only and points to the closed-source Kiro CLI.

Provides a set of MCP servers that expose AWS documentation, APIs, and tools to LLM-based applications via the Model Context Protocol—enabling up-to-date context retrieval, RAG, and controlled AWS operations from AI assistants.

Provides adaptive workflow steering rules for AI coding agents to guide development across Inception, Construction, and Operations phases. Includes opt-in extensions (security, testing), IDE/agent integrations (Cursor, Kiro, Amazon Q, Copilot, Claude), and human-in-the-loop approval points.

Exposes models available in Kiro IDE/CLI (e.g., Claude and other provider models) as local OpenAI- and Anthropic-compatible proxy endpoints so tools and SDKs can use them. Key features: multi-account failover, streaming, tool-calling, VPN/proxy support, and Docker/Python deployments.

Provides a framework to build, evaluate, and run AI SRE agents that investigate and remediate production incidents on your infrastructure. Includes a CLI, synthetic + end-to-end benchmark suites, and 40+ connectors for observability, infra, and LLM providers so teams can train agents and run investigations locally or in cloud.

Equips AI coding agents with reusable AWS skills (deployment, serverless, Amplify, SageMaker) by packaging agent skills, MCP servers, hooks, and references so agents invoke vetted workflows instead of bloating prompts.