deepseek-ai/deepseek-harness (dsh) uses a Cordis plugin architecture to make models, tools, sandboxes, and memory all swappable components, hitting nearly 200k stars a week after its developer preview launch; PrimeIntellect-ai/prime-agent runs long-lived research coding tasks on a Recursive Language Model architecture, surviving terminal disconnects via a persistent IPython session; liqiwa/mcp-radar automates this very kind of digest by scanning GitHub daily for newly ranked MCP servers. On the framework side, Mastra 1.61.0 adds a crash-resilient background task queue, and ComposioHQ/composio 0.17.0 extends SSRF protection to tool-execution downloads and S3 uploads.
Slack Code moves AI coding agents from individual terminals into shared Slack channels where teams can see diffs, previews, and plans in real time. But it solves management's visibility anxiety, not engineers' productivity bottleneck — the real battle is over who becomes the agent control plane.
DeepSeek's open-source agent harness 'dsh' crossed 20K stars within an hour of its 8/13 launch and has since accumulated ~158K stars, with 2000+ plugin proposals flooding in within two days. Its core is a Cordis-powered 'everything is a plugin' architecture that can even call Claude Code and Codex as sub-agents. RightNow-AI reimagines agents at the OS level with Rust (openfang), NetEase Youdao ships a desktop Agent built on OpenClaw (LobsterAI), and PrimeIntellect's prime-agent features a self-improving reasoning loop. CrewAI 1.15.16 adds execution context tracking and flow error logging.
Harness-IF reveals Coding Agent instruction following is overestimated by 3.6-7.4 pp because things the model would do anyway are counted as compliance; SHE decomposes the harness into four safety components and auto-evolves from trajectory failures, cutting ASR by 3.1x while improving correctness; SBCO uses a decomposed verifier bank with text gradients for harness self-improvement, matching Gödel Machine at 4-5.5x lower compute on planning tasks
Evo-Bench benchmarks nine models on self-improving harnesses — GPT-5.6 Sol tops at +16.6 but Office tasks barely move; MEGA uses a three-layer Wisdom Graph to make agent optimization infrastructure self-evolving, merging knowledge accumulation with optimization; SHE decomposes harnesses into four evolvable components that learn safety boundaries from failure trajectories, cutting ASR by 3.1x with cross-model transferability
OneDayAgent's decompose-remember-verify harness hits 0.821 new SOTA on AgentIF-OneDay and works unchanged across five backends; The Horizon Gap surveys 1,547 papers to find that six categories of long-horizon failure share a single structural pattern — outcome-only signals degrade as step count grows, driving the field toward denser process signals; Evo-Bench is the first benchmark for harness self-evolution — GPT-5.6 Sol peaks at +16.6 absolute gain, but Office tasks still need hand-crafted workflows
OpenAI wrapped the Codex harness as a JSON-RPC over stdio App Server, enabling VS Code, JetBrains, Web, and desktop apps to share a single agent loop. Three core primitives: Item, Turn, and Thread.
Agent memory isn't a plugin — it's part of the harness itself. Pick the right memory type, estimate data volume, then decide on the technology. And finally, figure out whether you actually own that memory.
An open-source Agent Harness framework from HKUDS (HKU Data Science Lab) that implements tool calling, skill loading, memory, permissions, and multi-agent collaboration as complete infrastructure, supporting Anthropic / OpenAI / GitHub Copilot API formats.
The model is the CPU, the harness is the operating system, and the agent is the application. No matter how powerful a model is, without a good harness it's just a demo. Phil Schmid argues that harness is the most critical infrastructure in AI engineering for 2026.