📖 AI-Native Infrastructure: Architecture evolution guide from cloud-native to AI-native

Aden Hive

A production-ready framework and runtime for building self-evolving AI agents.

Aden · Since 2026-01-12
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Detailed Introduction

Aden Hive is a production-focused agent development framework that generates agent graphs and connection code from natural-language goals. The project provides a runtime, observability, and human-in-the-loop nodes so agents can capture failure data, evolve via a coding agent, and redeploy automatically—forming a continuous self-improvement loop.

Main Features

  • Goal-driven development: describe objectives in natural language and let the coding agent build the execution graph and test cases.
  • Self-evolution: built-in failure capture and evolution workflows let the system improve agent structure based on real execution feedback.
  • Human-in-the-loop: configurable intervention nodes let teams insert manual judgment at critical decision points.
  • Observability & cost control: real-time streaming, metrics, and budget controls make production operation and cost management practical.

Use Cases

Suitable for long-running, iterating, and reliability-critical agent systems such as automated business workflows, enterprise assistants, and self-hosted multi-agent orchestration. Aden helps teams move experimental agents to production with integrated development and operational tooling.

Technical Characteristics

Aden Hive provides a modular runtime and SDK-wrapped nodes, supports multiple LLM providers and local models via LiteLLM, and integrates MCP-style tools for tool calling and state management. It is designed for observability, fault tolerance, and CI/CD integration to run at scale on platforms like Kubernetes.

Aden Hive
Score Breakdown
🤖 Agent Framework 🦾 Agents ⚙️ Automation 🔭 Observability