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SuperAI SuperBlueprint

SuperAI SuperBlueprint

Proposal: SuperAI SuperBlueprint

Linux Foundation | LF Edge + Agentic AI Foundation (AAIF)

1) Executive Summary

This proposal establishes SuperAI SuperBlueprint as a Linux Foundation-aligned initiative to accelerate agentic AI adoption in edge environments via open standards and open governance.

The blueprint aims to:

  • Define a reference agentic AI + edge architecture

  • Deliver tested, reusable “blueprint” implementations aligned to LF Edge blueprints and edge interoperability goals

  • Use AAIF artifacts (e.g., MCP, AGENTS.md, goose) to ensure consistent, interoperable agent behavior and tool connections

2) Background (Why LF Edge + AAIF)

LF Edge is an umbrella organization launched by the Linux Foundation to create an open, interoperable framework for edge computing, independent of hardware/silicon/cloud/OS. LF Edge projects such as Akraino provide sets of open infrastructures and application blueprints across use cases including 5G and AI, demonstrating a proven model for “blueprint-based” adoption.

AAIF was formed under the Linux Foundation to provide a neutral, open foundation for agentic AI, with initial contributions including MCP, goose, and AGENTS.md. It operates as a directed fund under the Linux Foundation, offering neutral stewardship for open, interoperable agentic AI infrastructure.

For future AI use cases, such as physical AI, real-time capabilities will be critical, and collaboration between the edge and the cloud will be necessary. Furthermore, given the current challenges with power and cooling in centralized data centers, distributed data centers—including those at the edge—are likely to emerge as a solution.


To address these challenges, LF Edge and the Agentic AI Foundation (AAIF) define reference architecture for "agentic AI + edge" and publish it as open-source to help drive solutions to the issues outlined above.

3) Problem Statement

Enterprises deploying AI at the edge face fragmentation:

  • inconsistent protocols for connecting agents to tools and data

  • siloed edge stacks and deployment patterns

  • weak reproducibility and governance for automation behavior at scale

  • expanded potential attack surface due to bi-directional communication

  • Inconsistent of agent runtime environment

  • Various environment to environment communication protocol. 

  • Lack a unified protocol for agent orchestration and scaling.

SuperAI SuperBlueprint addresses this by unifying agentic standards with edge blueprints into a practical, deployable reference.

4) Goals & Scope

Goals

  • A common blueprint for agentic AI running on/against edge infrastructure

  • Standardized interface layer for agents to access tools/data via MCP-based integrations

  • Reusable patterns for Guardrails, HITL, auditability, and incident response

  • Reference implementations that can be adopted “as-is” or customized

Scope (Initial Domain Candidates)

  • Industrial/OT edge (monitoring + action pipelines)

    • a multimodal AI assistant for operators and technicians to diagnose board failures, guide debugging, and reduced scrap and rework cost.

  • Telco edge

    • automation of network ops workflows

  • Retail edge

    • loss prevention, inventory optimisation, federated learning environments for optimized store ops, security, and analytics)

  • Physical AI
    Physical AI involves intelligent systems with physical bodies that interact with the real world. Unlike software-only AI, it integrates perception, reasoning, and action in robots or autonomous vehicles. Real-time performance is critical for physics AI, which requires the optimal utilization of both edge and cloud resources. In other words, it necessitates resource control across a broadly defined distributed data center. Super AI SuperBlueprint will provide reference architecture for physical AI from computing resources layer on distributed data center including cloud and edge to agentic AI application layer.

    • Smart city + mobility edge

      • Adaptive traffic control using cameras and edge AI to optimize signal timing in real time.

    • Healthcare edge  

      • bedside monitoring, local clinical inference, or Federated Learning for low latency and data privacy use cases)

    • Public safety

      • using autonomous agents (e.g. robots, drones) to assist first responders

5) Proposed Architecture (Blueprint)

Core components (Updated)

  1. Agentic runtime (multi-agent native):
    Multi-agent workflows (e.g. Goose-based) with standardized tool integration (MCP), supporting patterns such as planner / executor / observer, task decomposition, coordination, and shared state across agents.

  2. Edge platform integration (cloud, edge, and on-prem):
    Open, interoperable platform patterns across cloud, edge, and on-prem, enabling seamless workload placement, edge-to-cloud handoff, and policy enforcement across distributed environments. The first phase will focus on the runtime aspects of a platform.

  3. On-Prem Data Center integration:
    Seamless connectivity and workload orchestration between edge nodes and centralized on-premises data centers, including data synchronization, latency-aware routing, and hybrid policy enforcement across the continuum.

See and buy: a design that can start small and scale to millions of users without changing the architecture.

Use AI to help modernize existing CPU-based code and optimize the path toward GPU-enabled deployment.

  1. Project-level guidance:
    AGENTS.md conventions for consistent agent operation across repos/toolchains, extended to support multi-agent coordination standards and lifecycle management.

  2. Security + governance:
    Identity, authorization, audit logging, incident playbooks, and differential privacy, with added focus on multi-agent trust boundaries, communication security, and cross-environment policy consistency.

  3. Operations (incl Observability, AIOps, AgentOps + orchestration):
    OTEL as the core observability framework (for development, security, performance, troubleshooting, capacity planning, scalability etc) towards AIOps as the core mechanism to operate blueprint components, including multi-agent orchestration observability, guardrails, and closed-loop automation across the cloud–edge continuum.

    1. Leveraging EdgeLake for storing distributed Telemetry data for centralized or decentralized AIOps agents operation.

5.1 Sandbox (controlled execution & validation layer):

A attested, trusted and verfied sandbox environment for safe agent execution, testing, and validation, supporting isolated multi-agent runs, simulated tool/data interactions, policy and guardrail verification, replay/audit capabilities, and CI/CD integration for reproducible pre-production evaluation.

5.2 Agent Orchestration (Unified Stateful Runtime & Secure Communication Layer)

To address the fragmentation identified in the Problem Statement—specifically inconsistent agent runtime environments and lack of standardized cross-environment communication—this blueprint introduces a unified Agent Orchestration layer.

This layer serves as the control, coordination, and trust plane for multi-agent systems across the cloud–edge–on-prem continuum, enabling agents to operate as persistent, portable, attested, verified and secure runtime entities.

 

5.2.1 Unified Stateful Runtime Environment

Defines a consistent execution model for agents across heterogeneous environments (cloud, edge, on-prem), enabling persistent state reuse, token efficiency, and seamless mobility.

Key capabilities include:

  • Persistent context reuse
    Agents maintain reusable working context, memory, and execution history, reducing repeated context reconstruction and significantly lowering token consumption for iterative and long-running tasks.

  • State-preserving migration
    Agents can move across environments (edge-to-cloud, cloud-to-cloud, or across distributed clusters) while preserving execution state, enabling:

    • workload rebalancing based on latency, cost, or resource availability

    • fault recovery and failover without losing progress

    • continuity of long-lived agent processes across distributed systems

  • Portable runtime semantics
    A unified runtime model ensures consistent agent lifecycle handling (init, run, suspend, resume, migrate) and execution behavior across heterogeneous deployment targets.

  • State Lifecycle Tasks

    • Preservation of state (“save to disk”) if agent mesh communication times out - triggered by explicit operator action

    • State restoration (“load from disk”) after a state has been preserved prior - triggered by explicit operator action

    • Reset (“reset” - re-establishes the same agent mesh setup) if the same agent mesh (swarm) needs a restart - triggered by explicit operator action, or policy

    • Deletion (“delete”) of all states across the connected runtimes if the outcome has been achieved or aborted. - triggered by task completion, explicit user action or retention policy expiration

    • Ownership transfer - in case the master/owner is not-responding; policy driven; shared ownership model requires quorum for onwership transfer;

      • In a Single owner scenario: transfer can be policy driven to transfer

      • Shared ownership requires quorum;

      • All scenarios need the new and previous owner to be authenticated, authorized and transfer fully audited.

    • (add 26June) Examine EdgeLake as a agent state and mesh state persistence mechanism and distribution/backup mechansims using it’s blockchain based metadata eventually consistency

      • Needs agent lifecycle management approach.

This enables agents to function as persistent, portable runtime entities rather than isolated stateless inference calls.

TODO: we need to distinguish between Mesh state and local agent state lifecycle.

 

5.2.2 Stateless Runtime Environment

To build a distributed, stateless runtime environment for AI agents, we need to define how to maintain long-running, multi-step agent logic while keeping the underlying compute infrastructure ephemeral and stateless.

 

5.2.3 Unified Lightweight Dynamic Encryption for Agent-to-Agent Communication

Defines a unified secure communication layer for agent-to-agent interaction across cloud, edge, and on-prem environments.

Key capabilities include:

  • Efficient, cross-environment encryption
    A common encryption mechanism designed for efficient operation across heterogeneous systems, including resource-constrained edge environments.

  • Dynamic key rotation
    Communication keys are refreshed dynamically to reduce long-lived credential exposure, minimize attack surface, and improve resilience against interception and replay risks.

  • Unified trust establishment
    Provides a consistent security model for autheticated and identfied agents communicating across different attested runtime environments, supporting secure identity verification, session establishment, and protected data exchange.

  • Portable secure communication semantics
    Defines a common encrypted communication baseline independent of any specific vendor, transport protocol, or deployment stack, enabling interoperability across cloud-to-cloud, edge-to-cloud, and edge-to-edge interactions.

This layer establishes a secure and interoperable communication foundation for distributed agent systems, while remaining lightweight enough for practical deployment across the cloud–edge continuum.

 

5.2.4 Distributed Agent Runtime Layer (“Swarm”)

Introduces a unified distributed agent runtime that allows for both stateless and stateful (5.2.1) execution and a secure communication layer (5.2.2) to provide a logical distributed execution environment across cloud and edge.

Swarm abstracts heterogeneous infrastructure into a single coherent logical agent execution domain (“zone”), where agents can operate, communicate, and migrate seamlessly.

Key capabilities include:

  • Encrypted execution environment with attested, trusted, and verifiable runtime properties (e.g. Trusted Execution Environment, Confidential Computing, Swarm zone security)
    Each Swarm operates as a secure logical domain where agent communication and state exchange are protected by the unified dynamic encryption layer, ensuring consistent security across environments.

  • Stateful agent mobility and failover within the Swarm
    Agents can migrate across nodes or sites within the Swarm while preserving execution state, enabling:

    • automatic failover when a site or node becomes unavailable

    • continuity of long-running agent processes

    • resilient distributed execution across cloud and edge

  • Elastic scalability across distributed infrastructure
    The Swarm enables dynamic scaling of agents across available resources, supporting:

    • horizontal expansion across edge nodes and cloud clusters

    • adaptive workload distribution based on demand and resource availability

  • Hybrid execution model (edge responsiveness + cloud intelligence)
    The Swarm supports a split execution model where:

    • edge agents provide low-latency, real-time response close to data and devices

    • cloud agents provide deeper reasoning, planning, and large-scale coordination

  • This allows systems to balance fast local response with deep global intelligence within a unified runtime environment.

 

Together, the unified runtime, secure communication layer, and Swarm virtualization layer establish the foundation for an agent-native distributed system, where agents operate as persistent, mobile, and secure computational entities across the cloud–edge continuum.

 

6) Deliverables

  • SuperAI SuperBlueprint Spec (architecture + constraints + interfaces)

  • Reference Implementation (open source) with CI evaluations and benchmark scripts

  • Blueprint evaluation harness (quality, latency, reliability, safety)

  • Adoption kit: docs, sample AGENTS.md templates, integration guides, and demo deployments

  • Publication (paper, whitepaper, open source code)

7) Governance & Community Plan

  • Hosted within Linux Foundation umbrella:

    • LF Edge for edge blueprint alignment and ecosystem adoption

    • AAIF directed fund alignment for agentic interoperability standards and neutrality

  • Define maintainers, contribution guidelines, release cadence

  • Quarterly public roadmap updates and cross-foundation working group syncs

8) Roadmap

Phase 1 (0–6 weeks [2026-05-10- 2026-06-20])

  • Charter draft, blueprint scope selection, stakeholder mapping

  • Define target LF Edge blueprint alignment and AAIF standard dependencies

Phase 2 (6–16 weeks [2026-06-21 - 2026-08-29])

  • MVP reference implementation + first blueprint “stack”

  • Evaluation harness + initial benchmarks on edge deployment targets

  • PoC Session

    Conduct a Proof-of-Concept session with participating partners to validate the first SuperAI SuperBlueprint stack, including agent orchestration, sandbox execution, edge/cloud deployment, observability, and security controls. The session will collect technical feedback, identify implementation gaps, and define next steps for pilot deployment.

  • Benchmark & Performance Validation

    Develop and execute a comprehensive benchmark framework to evaluate the SuperAI SuperBlueprint across cloud, edge, and hybrid environments. The benchmark program will measure:

    • Agent orchestration performance (task completion latency, throughput, scalability)
    • Agent-to-agent communication efficiency and security overhead
    • Sandbox execution performance and isolation effectiveness
    • Edge-to-cloud workload migration and state preservation efficiency
    • Resource utilization (CPU, GPU, memory, network, power consumption)
    • Reliability and fault recovery under distributed deployment scenarios
    • Observability and operational metrics collection effectiveness

    Benchmark results will be published as part of the blueprint evaluation harness and used to establish baseline performance targets, identify optimization opportunities, and validate production readiness for pilot deployments.

Phase 3 (16–24 weeks [2026-08-30 - 2026-10-24])

  • Hardened release: security, HITL, telemetry, governance process

  • Community adoption + partner pilots

9) Metrics

  • Time-to-deploy a new agentic edge workload (baseline vs blueprint) [owner: tbd]

  • Interoperability: agent runtime communication across stateful/stateless runtimes, incl # of MCP tool integrations validated under the blueprint [owner: tbd]

  • Reliability + safety: incident rate, false positive/negative rates, recovery time [owner: tbd]

  • Community adoption: contributors, forks, and blueprint reuse in partner orgs [owner: tbd]

  • Operations: Anomaly detection, RCA and Remediation: MTTD/MTTR (Mean Time to Detect/Repair) [owner: AIOps stream]

10) Ask

  • Linux Foundation support for hosting and governance alignment

  • Joint LF Edge + AAIF working group sponsorship

  • Partner commitments for pilot deployments on edge stacks and MCP-based tool ecosystems

11) Contributors

We would like to acknowledge the individuals and organizations contributing to the SuperAI SuperBlueprint proposal and related discussions.

Individual contributors:

  • Andreas Spanner (Red Hat)

  • Felix Sun (

  • Haruhisa Fukano (1Finity)

  • Jeff Brower (Signalogic)

  • Kelly Swift

  • Liang Cheng

  • Leo Li

  • Nathan Mallamace

  • Tina Tsou (Supermicro)

  • Vijay Pal (PalC)

  • Wes Jackson (Red Hat)

  • Xuan Yang

  • Simon ZHOU

Participating organizations and current areas of interest (in alphabetical order):

  • 1Finity (Fujitsu): physical AI, edge-integrated automation scenarios and distributed computing platform

  • AAIF: agentic AI interoperability, MCP, AGENTS.md, and open governance alignment

  • LF Edge: edge infrastructure, open blueprint alignment, and deployment patterns

  • Meta: connectivity-related collaboration and integration opportunities

  • PalC Networks: open-source networking and data center integration, leveraging advanced AI-driven innovation.

  • Red Hat: platforms (k8s, Linux OS), orchestration for distributed environments (Open Cluster Management), security (StackRox)

  • Signalogic: physical AI, autonomous agents networks, SFF AI servers for robotics and drone applications

  • Supermicro: hardware platforms, edge-to-data-center deployment, and reference infrastructure

  • SUSE: operating system and orchestration layer across heterogeneous environments

  • Additional industry and academic participants: use cases, pilot deployments, research collaboration, and ecosystem feedback

This section will continue to evolve as more contributors join and as roles and contribution areas become more clearly defined.


12) Open Technical Meetings

 

Occurs every Thursday effective Apr 16, 2026 until Oct 16, 2026 from 6:30 PM to 7:00 PM, Pacific Time.

______________________________________________________________________

Microsoft Teams meeting

Join: https://teams.microsoft.com/meet/231343341018592?p=LpLZmq528EX8KqbpcK

Meeting ID: 231 343 341 018 592

Passcode: 8da7di78

 

 

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Phone conference ID: 986 670 92#

 

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