AI Agent Harnesses

README excerpt

The performance optimization system for AI agent harnesses.

Not just configs. A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.

Works across Claude Code, Codex, Cursor, OpenCode, Gemini, and other AI agent harnesses.

v2.0.0-rc.1 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the rc.1 release notes and cross-harness architecture.

**Language:** English 
# ai-agent-harnesses

## The Guides

This repo is the raw code only. The guides explain everything.

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| Topic | What You'll Learn |
|-------|-------------------|
| Token Optimization | Model selection, system prompt slimming, background processes |
| Memory Persistence | Hooks that save/load context across sessions automatically |
| Continuous Learning | Auto-extract patterns from sessions into reusable skills |
| Verification Loops | Checkpoint vs continuous evals, grader types, pass@k metrics |
| Parallelization | Git worktrees, cascade method, when to scale instances |
| Subagent Orchestration | The context problem, iterative retrieval pattern |

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## What's New

### v2.0.0-rc.1 — Surface Refresh, Operator Workflows, and ECC 2.0 Alpha (Apr 2026)

- **Dashboard GUI** — New Tkinter-based desktop app