An Open Foundation for Advanced Human‑AI Collaboration

Introduction

We are proud to introduce Common Ground, an open‑source application and framework purpose‑built to forge a true partnership between humans and AI. It is more than a powerful multi‑agent system; it is the next cornerstone of the Intelligent Internet, an ecosystem where technology sharpens human intellect instead of dulling it.

Unlike “answer engines” that work behind an opaque screen, Common Ground keeps reasoning in the open. You can inspect, steer, and refine every decision your AI team makes in real time and retrospectively. That control matters most when the stakes are highest: managing our health, educating our children, and coordinating our financial systems.

Why Common Ground?

From Cognitive Offloading to Intelligence Augmentation

Faster answers are not always smarter answers. As we offload more thinking to closed, synthetic voices, our own critical faculties risk atrophy. The cure is not another black‑box super‑agent; it is a Thinking Environment that makes AI reasoning legible, steerable, and ultimately symbiotic with human expertise.

Common Ground tackles that challenge head‑on, giving you a transparent workspace where human judgment stays firmly in the driver’s seat.

Key Features

• Human-AI partnership first: transparent multi‑agent workflows that augment, not replace your judgment.
• Three‑layer professional team architecture mirroring elite consulting models.
• Declarative agent personas via simple YAML profiles; swap roles without code changes.
• Multi‑view observability (Flow, Kanban, Timeline) keeps you fully in the loop.
• Persistent procedural memory stores every run locally for audit, learning, and reuse.

The Design of Common Ground

The framework is modular, extensible, and fully observable, simulating a high‑efficiency team of human experts capable of multi‑step reasoning, multi‑source information processing, and dynamic planning.

A Layered, Collaborative Architecture - The Professional Team Mode

1. Partner: The user‑facing strategist collects objectives, clarifies scope, and co‑authors a high‑level plan.
2. Principal: The execution hub breaks the plan into concrete Work Modules, assigns them, and monitors progress.
3. Associates: Specialist agents such as a Code Analysis Expert or Web Research Expert execute tasks independently and return structured deliverables.

Declarative Behavior: Designing AI Personas

• Design 'AI Personas': All behaviours live in simple, human‑readable YAML profiles. Edit system prompts, tool menus, decision logic, or self‑reflection rules without touching any code.
• Context Handover Protocol: Information moves from Partner → Principal → Associate via declaratively defined hand‑off rules, avoiding loss or drift.

Multi‑View Observability: A Shared Cognitive Context

The Flow view mirrors the AI’s reasoning graph, the Kanban view maps progress to familiar project boards, and the Timeline view shows execution chronologically. Together they align the AI’s internal model with yours.

Persistent Procedural Context

Every run is stored locally, capturing not just results but the full procedural context - the how and why behind every decision. Past work becomes reusable intelligence.

What’s Next

• API service & browser demo
• GUI improvements for faster in‑loop editing
• Expanded agent library: finance, education, biomedical
• Integration with II‑Commons indexes for richer grounding

Try It Now

• GitHub
• Community Discord

Fork it, extend it, and show us what you build.

Conclusion

Common Ground is not the destination; it is the launchpad for an open, collaborative, and trustworthy agentic future. Whether you are a researcher, engineer, educator, or policymaker, Common Ground offers a foundation you can build upon with confidence.

The next chapter of intelligent systems will not be written inside walled gardens. It will be open to all. Let us shape that future together.