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🏛️ AgencyGrid: Open AI Societal Infrastructure

In a society of agents or the Internet of Agents, billions of autonomous entities interact, collaborate, and compete across open, unbounded environments. Success here depends not only on what individual agents can do, but on how they are organized, coordinated, and governed. Without clear agency, the structures of roles, relationships, authority, and interaction rules - large-scale cooperation breaks down, trust erodes, and resilience falters.

AgencyGrid provides the formal mechanism for these open systems:
- Defining roles and verifying capabilities.
- Aligning incentives and structuring negotiations.
- Handling escalation and conflict resolution.
- Establishing adaptable governance.

It bridges the gap between self-organizing autonomy and structured collaboration, enabling agents to form stable societies, negotiate shared goals, and evolve collective intelligence. By embedding agency as a first-class construct, AgencyGrid transforms the Internet of Agents from a loose network of autonomous nodes into a coherent, adaptive civilization of machines.

🌐 Beyond the Individual Agent

Agency extends beyond the capabilities and decision-making of individual agents to include the societal dimension of coordination and control.

This broader view recognizes that effective MAS design requires frameworks where:
- Coordination mechanisms, global requirements, and system-wide behaviors are not solely embedded within individual agents,
- but can also be defined and managed at the societal, organizational, or infrastructural level.

Systems that rely on a predefined agent type or class risk becoming closed and inflexible, as they exclude agents with different coordination models or behavioral patterns.

True agency therefore embraces:
- Heterogeneity – supporting diverse kinds of agents.
- Interoperability – enabling communication and collaboration across boundaries.
- Shared Governance – ensuring order without sacrificing autonomy.

✨ This allows diverse agents to operate and collaborate within a common societal framework.

Effective multi-agent design requires frameworks where:

  • Coordination, global requirements, and system behaviors are not hard-coded inside agents.
  • Institutions, protocols, and organizational structures manage interaction at the societal or infrastructural level.
  • Heterogeneity and interoperability are embraced, allowing diverse agents to collaborate within a common framework.

An Agency can be understood as an entity distinct from the agents within it. It has its own goals, processes, and structure (roles, responsibilities, and activities), but it cannot act directly. Instead, agents occupy these roles and carry out its functions. This creates a mutual dependency: the agency provides the framework for coordinated action, while the agents provide the operational capacity to realize its aims.

⚙️ Agencies as Active Systems

Agencies operate within multi-agent or massive multi-agent environments. Like organizations in systems thinking, their success depends not just on the parts but on how those parts work together. Agencies:
- Take in resources and use them efficiently to produce results.
- Intentionally structure interactions to achieve shared global goals.
- Define desired outcomes externally, independent of individual agents’ inner design or goals.

This ensures global outcomes without reducing the autonomy of agents.

🏙️ Inspiration from Human Societies

Multi-agent agencies draw inspiration from human institutions by:
- Defining roles, responsibilities, and permissions.
- Structuring both coordination mechanisms (plans, workflows, goals) and institutional mechanisms (rules, norms, protocols).
- Balancing agent-centric approaches (where coordination is implicit inside each agent) with agency-centric approaches (where the societal framework is explicit and referenceable).

✨ AgencyGrid turns agency into programmable societal infrastructure, enabling billions of agents to interact through structured coordination, trust, and adaptability.

A unified backend for orchestrating roles, workflows, policies, tasks, and infrastructure for AI-driven organizations. Modular, policy-aware, and designed for secure, scalable, and autonomous operations in modern cloud-native environments.

🚧 Project Status: Alpha
Not production-ready. See Project Status for details.

📚 Contents

🏗 Architecture Diagrams

🌟 Highlights

🧩 Modular Organization Lifecycle

  • 🏢 Define and provision organizations via declarative metadata
  • 🔁 Automate creation of org-specific resources and service deployments
  • 📇 Register, search, and manage orgs via GraphQL and REST APIs
  • ⚙️ Enable dynamic org-level autoscaling and resource enforcement

🔐 Secure Role and Access Management

  • 🧑‍🤝‍🧑 Associate users or agents with roles, job spaces, and function access
  • ✅ Enforce access control using constraint-based DSLs & Policies
  • 🔄 Dynamically resolve permissions during request lifecycle
  • 📜 Track all associations, constraints, and contracts in DB

🛠 Workflow-Driven Execution Engine

  • 🧠 Execute modular workflows using tools, functions, and DAGs
  • 🔄 Route jobs to internal agents based on org-specific logic
  • 🪢 Delegate node-level execution via APIs or local executors
  • 📦 Queue-based execution and WebSocket updates

🔍 Comprehensive Governance & Observability

  • 🛡 Enforce constraint checks before tool/function invocation
  • 📈 Monitor org usage, apply quotas, and trigger autoscaling
  • 📬 Message validation and execution via NATS or Redis queues
  • 📘 Fully observable with logs, metrics, and live execution traces

📦 Use Cases

Use Case What It Solves
Multi-Org Deployment Management Automates provisioning, scaling, and isolation of organizations
Role-Based Access & Policy Control Enforces contract-driven access and validation across systems
Workflow Orchestration DAG-based execution across tools, agents, and microservices
Internal Function & Tool Execution Local or remote invocation of approved code with audit logging
Constraint-Driven Job Assignment Matches tasks to agents based on pre-evaluation logic

🧩 Integrations

Component Purpose
MongoDB Registry storage for roles, functions, contracts, jobs, workflows
Redis Event queue, execution buffer, and constraint caching
NATS Message transport for real-time job delegation
Flask + GraphQL REST and query interface for APIs and frontend orchestration
Kubernetes Dynamic org provisioning and autoscaler management

🧠 Subsystems and Roles

Subsystem Description
org_registry Manages org metadata, tags, structure (MongoDB + GraphQL)
org_deployer Provisions new orgs and deploys scoped services via Kubernetes
org_autoscaler Monitors usage and triggers quota enforcement / scaling decisions
org_resources Governs org quotas, replica counts, and resource constraints
roles_system Assigns subjects to roles/groups with fine-grained policy enforcement
constraints_checker Validates request constraints using dynamic DSLs before execution
contracts-generator Generates and maps job-specific contracts and policy rules
tools_executor Manages registration and runtime execution of tools (binary/Python)
dsl_proxy Executes DSL-based workflows and resolves dynamic node-based execution
assignment-system Assigns tasks/jobs to appropriate agents or workers
job-internal-processor Handles job lifecycle, agent resolution, and contract enforcement
task_system Orchestrates full task pipelines and external events
tasks_db Stores persistent state of tasks and sub-task executions
task-internal-assigner Resolves assignments and dispatches to correct executors
gateway API gateway for policy enforcement and routing validation

💡 Why Use This?

Problem Our Solution
🔹 Fragmented org, role, and policy systems Unified control layer for access, validation, and execution
🔹 Manual org provisioning and scaling Automated provisioning + dynamic autoscaler per org
🔹 Workflow sprawl and uncontrolled function use DSL-governed workflows with role-based execution rights
🔹 Inefficient job allocation across agents Constraint-evaluated assignment engine with queue fallback
🔹 Lack of observability into function/tool execution WebSocket and Redis tracking for every execution lifecycle

Project Status 🚧

⚠️ Development Status
The project is nearing full completion of version 1.0.0, with minor updates & optimization still being delivered.

⚠️ Alpha Release
Early access version. Use for testing only. Breaking changes may occur.

🧪 Testing Phase
Features are under active validation. Expect occasional issues and ongoing refinements.

⛔ Not Production-Ready
We do not recommend using this in production (or relying on it) right now.

🔄 Compatibility
APIs, schemas, and configuration may change without notice.

💬 Feedback Welcome
Early feedback helps us stabilize future releases.

📢 Communications

  1. 📧 Email: community@opencyberspace.org
  2. 💬 Discord: OpenCyberspace
  3. 🐦 X (Twitter): @opencyberspace

🤝 Join Us!

This project is community-driven. Theory, Protocol, implementations - All contributions are welcome.

Get Involved