SECO Launches Clea 2.0 Agentic Framework
The new software framework allows for one-click deployment of AI models and applications to various edge devices.
Updated on Oct. 28, 2026 in Artificial Intelligence
Live Poll
Do you plan to consolidate your organization's AI infrastructure under a single, unified software platform?
SECO has released the Clea 2.0 Agentic Framework to streamline the deployment of AI models across edge hardware. The platform provides tools for managing application lifecycles on both proprietary and third-party hardware architectures.
Why it matters
The framework aims to bridge the gap between initial development and industrial-scale deployment. By centralizing management, it helps resolve the complexity caused by fragmented tools across diverse edge platforms.
The Clea 2.0 framework introduces a native Model Context Protocol server that enables fleet orchestration by converting complex, multi-step requests into coordinated operations. The software further enhances security through integrated role- and attribute-based access controls.
The players
SECO
SECO is an international technology company that specializes in providing solutions for the internet of things and artificial intelligence.
The details
The software simplifies the integration process by unifying application lifecycle management across heterogeneous hardware. By acting as a central orchestration layer, it enables users to deploy AI capabilities to edge devices with a single click.
Timeline
SECO officially launched the Clea 2.0 Agentic Framework on 28 October 2026.
Free registration for early access to the platform expires on 30 April 2027.
The Tech Race
The integration of the Model Context Protocol within Clea 2.0 marks a significant step toward standardizing how AI models interact with diverse hardware environments. This shift reflects a broader industry movement to replace fragmented management silos with unified protocols that simplify large-scale edge orchestration.
Developers and industrial users can now streamline their workflow by managing heterogeneous hardware through a single, centralized interface. Those interested in testing these capabilities can register for free early access before the program closes next spring.
The takeaway
Organizations should evaluate how their current edge infrastructure handles disparate management tools as they scale AI applications. Utilizing standardized frameworks can significantly reduce the operational overhead required to maintain model performance across different hardware.
Further reading
For more on the current landscape of deployment tools, visit Artificial Intelligence.
Source note: This article includes information reported by EEJournal.
Live Poll
Do you plan to consolidate your organization's AI infrastructure under a single, unified software platform?
