Xiaomi Releases HarnessX: A Composable, Adaptive, and Evolvable Runtime Framework for AI Agents

The Xiaomi Darwin Agent Team published a paper on arXiv in June 2026, proposing HarnessX—a composable, adaptive, and evolvable runtime framework (Harness) generation system for AI agents. HarnessX addresses the pain points of traditional agent frameworks such as manual construction, coupled architecture, and inability to co-optimize with models. It achieves automated framework optimization through three core modules:

• Componentized Composition Architecture: Abstracts the Harness into standardized processors and eight lifecycle hooks, dividing into nine optimization dimensions (model selection, context assembly, memory management, tool ecosystem, execution environment, evaluation and reward, control and security, observability, training bridging), enabling free plug-and-play and safe reuse of components via type constraints.
• AEGIS Adaptive Evolution Engine: Maps framework evolution to symbolic-space reinforcement learning, using a four-stage pipeline of parsing, planning, evolution, and evaluation to specifically address reward hacking, catastrophic forgetting, and insufficient exploration, with variant isolation strategies for heterogeneous task scenarios.
• Framework-Model Co-Evolution Mechanism: Builds a shared trajectory buffer and uses cross-framework GRPO algorithm to simultaneously optimize the framework and model, breaking through the capability ceiling of single optimization paths.

Experiments were conducted on five mainstream agent benchmarks (GAIA, ALFWorld, WebShop, τ³-Bench, SWE-bench Verified) with three large models of varying capabilities (Claude Sonnet 4.6, GPT-5.4, Qwen3.5-9B). Results show:

• HarnessX brings an average performance improvement of 14.5%, up to 44.0%, with weaker model baselines benefiting more significantly;
• Framework-model co-evolution yields an additional 4.7% gain;
• Variant isolation effectively prevents performance degradation and reduces token consumption by approximately 18%.

This research demonstrates that optimizing the runtime framework is an efficient path to enhance agent capabilities beyond model scaling, with the entire solution balancing automation, stability, and auditability. The paper will open-source the complete code, providing a new paradigm for self-evolving agent engineering.