SwarmHarness: Decentralized Skill-Based Task Routing Protocol for AI Agent Networks
SwarmHarness is a proposed decentralized protocol enabling AI compute sharing and task routing across heterogeneous nodes (workstations, inference servers, edge devices) without a central coordinator. It combines a DHT-based registry for peer discovery, a utility-function router dispatching tasks by capability/load/latency/trust, and a Shapley-value-based credit mechanism to align incentives among participating nodes. The system is designed as a foundational primitive for autonomous multi-agent networks where agents can hire compute, route subtasks, and settle credits without human intermediation. It positions itself against existing approaches like Golem, BrokerChain, BOINC, and Petals by integrating decentralization with a native incentive layer.
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Awesome Harness Engineering: Curated List for AI Agent Infrastructure
A GitHub repository aggregating resources on AI agent harness engineering, covering tools, patterns, evaluations, memory systems, MCP (Model Context Protocol), permissions, observability, and orchestration. The list has accumulated 1,318 stars with 39 added today, indicating moderate community traction. It serves as a reference index rather than original research or tooling.
From Model Scaling to System Scaling: Scaling the Harness in Agentic AI
This paper argues that the next major bottleneck in agentic AI is system-level design—what the authors call 'scaling the harness'—rather than continued model scaling alone. The agent harness encompasses memory substrates, context constructors, skill-routing layers, orchestration loops, and verification/governance components that together translate model capability into long-horizon behavior. The authors identify three core bottlenecks (context governance, trustworthy memory, dynamic skill routing) and propose harness-level benchmarks measuring trajectory quality, memory hygiene, and verification cost. They introduce CheetahClaws, a Python-native reference harness, and compare it against Claude Code and OpenClaw.
Code as Agent Harness: A Survey of Code as Operational Substrate for Agentic AI Systems
This survey paper introduces the concept of 'code as agent harness,' framing code not merely as output but as the operational infrastructure for LLM-based agents—covering reasoning, action, environment modeling, and execution-based verification. The authors organize the analysis across three layers: harness interface, harness mechanisms (planning, memory, tool use, feedback control), and scaling to multi-agent systems. Applications span coding assistants, GUI/OS automation, embodied agents, scientific discovery, and enterprise workflows. Open challenges include evaluation beyond task success, verification under incomplete feedback, and human oversight for safety-critical actions.
SIA: Self-Improving AI via Joint Harness and Weight Updates
SIA proposes a self-improving loop in which a Feedback-Agent simultaneously updates both the scaffold (harness) and model weights of a task-specific agent, unifying two previously disjoint research lines: meta-agent scaffold rewriting and test-time training. The system is evaluated on three diverse benchmarks—Chinese legal charge classification, GPU kernel optimization, and single-cell RNA denoising—achieving gains of 56.6%, 91.9% runtime reduction, and 502% respectively over baselines. The paper argues that harness updates shape agentic behavior while weight updates instill domain intuition that prompting alone cannot provide, and that combining both levers consistently outperforms either alone.
ruvnet/ruflo: Agent Meta-Harness for Claude with Multi-Agent Swarm Coordination
Ruflo is an open-source TypeScript framework positioning itself as a meta-harness for Claude-based multi-agent systems. It features adaptive memory, swarm intelligence coordination, RAG integration, and native Claude Code/Codex integration. The project has accumulated 57,231 stars with 354 added today, indicating significant community traction.
Hermes Agent Challenges OpenClaw on Token Usage Leaderboard; Agent Self-Improvement Highlighted
Hermes Agent, an open-source AI agent from Nous Research launched in February 2026, has surpassed OpenClaw on OpenRouter's daily token consumption leaderboard. Hermes Agent differentiates itself through a memory architecture and automatic skill-building capability using the SKILL.md format, enabling self-improvement as a core agentic feature. It supports local and cloud deployment, integrates with ~20 messaging services, and works with a wide variety of LLMs via the Agent Communication Protocol. The piece also covers Andrew Ng's commentary on Harvard's grade-capping policy, which is tangential to AI/ML.
PolyGnosis 2.0: Multi-Agent Architecture for Prediction Market Intelligence via Harness Engineering
PolyGnosis 2.0 introduces a multi-agent system that synthesizes Polymarket prediction market signals with GDELT OSINT streams to identify 'Perspective Mismatches' as trading signals. The paper rigorously evaluates agentic harness engineering techniques—reflection loops, tool-calling, divide-and-conquer partitioning, and chain-of-thought—in high-noise financial domains. Key empirical findings include that structural partitioning is necessary for multi-dimensional alignment, but unconstrained terminal reflection induces logical drift, and a pervasive consensus bias emerges across agent configurations. The authors identify a Pareto-optimal configuration achieving professional-grade analytical precision with minimized latency and token overhead.
Andrew Ng introduces OpenCoworker, an open-source desktop AI agent harness
Andrew Ng and collaborators Rohit Prasad and Devika Verma have released OpenCoworker, a free open-source desktop agent built by extending the aisuite library to support agent harnesses. The tool allows users to connect frontier LLMs (OpenAI, Anthropic, Google) or local models via Ollama to desktop tasks including file access, messaging, and workflow automation, with privacy as a design priority. Ng frames this as a response to data-retention concerns with commercial desktop agents, citing Anthropic's Fable release as a recent example of policy opacity. The post also provides a concise overview of the current desktop agent landscape and the shift toward LLM-driven agentic loops.