Pluto7 Announces Pi Agent with Alphaevolve: AI Agents Driving the ‘Living Factory’ Across Supply Chain Operations

Powered by Gemini Enterprise on Google Cloud, Pi Agent optimizes enterprise supply chains to deliver up to ~2% revenue growth and ~4% margin gains.

SAN JOSE, CA, UNITED STATES, August 3, 2026 /EINPresswire.com/ — Pluto7, a premier AI-native services and solutions provider, today announced a major milestone in enterprise intelligence: the launch of its fully autonomous Pi Agent platform powered by the Alphaevolve framework in Gemini Enterprise. This landmark release establishes the definitive foundation for Agentic AI, offering enterprises a structured, scalable path to automation with a hyper-focus on Inventory Management and Sales & Operations Planning (S&OP).

Designed specifically for complex manufacturing environments to enable the true “Living Factory” — and extending seamlessly across the broader supply chain and enterprise operations—Pi Agent addresses the critical pain points of the Semiconductor, Hi-Tech, Retail, and CPG verticals.
“We are seeing the living factory become a reality, connecting to the rest of the operations with a decisions-backward model. In this Agentic Era, AI is graduating from a system of intelligence to a true system of action, driving business outcomes we can measure. This has made outcome-based services with shared risk a real alternative to legacy models.

The old world of software licensing and hourly rates is being challenged. The companies that will win are those who co-innovate and scale quickly. Pluto7, alongside our global system integration partners, are prepared to help our customers lead this change.

“Our approach is clear: connect board-level strategy to the plant manager’s daily work. We cut through the AI hype and focus on solving one business problem at a time with our Ai Native Services, whether it’s optimizing inventory or improving yield. The results are clear, especially for our retail, CPG, and semiconductor partners: a tangible path to improving revenue by ~2% or margins by ~4%. It’s time to build an agile, agent-first foundation for modern commerce, keeping people in the Center” said Manju Devadas, CEO of Pluto7 and author of The AI Backbone book. “This release is a key milestone. With Pi Agent and Alphaevolve along with Gemini Enterprise, we are giving organizations the platform and the precise automation path required to build their AI foundation natively, securely, and autonomously.”

A Platform Built for Action: Pi OS and the “Progressive Glassbox”
Positioned as a comprehensive platform and operational system (Pi OS), Pi Agent with Alphaevolve redefines how enterprises manage AI. Pluto7 recognizes that to achieve a competitive advantage, a critical “system of action” cannot simply be bought off-the-shelf; it must be fundamentally built and tailored to an organization’s unique operational DNA.

The platform introduces advanced model management and tokenization capabilities, ensuring efficiency and scalability. Furthermore, Pluto7 is pioneering a “Progressive glassbox” approach—providing exactly the right balance of visibility, security, and interpretability so business leaders can trust the autonomous decisions being executed in their environments.

Seamless Co-Existence with Legacy Systems for an Agentic Foundation
Crucially, the Pi Agent platform is engineered to seamlessly co-exist with established and legacy supply chain software, including SAP IBP, Blue Yonder, Kinaxis, and o9, among others. Customers do not need to “rip and replace” their existing infrastructure; instead, Pi Agent integrates with and elevates these traditional systems.

Establishing this co-existence is critical right now to set the proper agentic foundation. By bridging these legacy platforms, Pi Agent enables a robust, orchestrated ecosystem of 100+ specialized sub-agents to operate simultaneously. This foundation allows enterprises to translate static data trapped in legacy planning tools into dynamic, autonomous workflows across the entire supply chain.
Deep Ecosystem Alignment & The Agentic GTM Blueprint

As an AI-native service provider, Pluto7 is enabling Gemini Enterprise Agent Platform directly within the customer’s own Google Cloud tenant to help ensure data sovereignty.

To accelerate this transition, Pluto7 is executing a specialized Agentic Go-To-Market (GTM) Blueprint, driven by strategic inputs from industry leaders at Google Cloud. This blueprint is tightly aligned with Google Cloud industry solutions to specifically target the manufacturing vertical. It provides a clear, actionable roadmap for enterprises to evolve from legacy Systems of Record (SOR) to Systems of Action (SOA), ultimately achieving a fully autonomous System of Action.
Driving the Agentic Era Evolution

The architectural and strategic principles driving Pi Agent are extensively outlined in Manju Devadas’s book – The AI Backbone serving as a blueprint for modern enterprises. Furthermore, the broader Pi Agent Community is actively collaborating to shape the evolution of the Agentic Era, sharing best practices, use cases, and driving continuous innovation in autonomous supply chain management and manufacturing.

For more information about Pi Agent, Alphaevolve, and how Pluto7 is transforming supply chains into autonomous systems of action, visit www.pluto7.com.

About Pluto7: Pluto7 is an AI-native solutions provider specializing in Google Cloud-native platforms for agentic commerce and supply chain optimization. By bridging the gap between data and actionable intelligence, Pluto7 empowers global enterprises to build, scale, and grow their operations autonomously.

Hema Karveti
Pluto7 Inc
+1 408-824-9213
marketing@pluto7.com
Visit us on social media:
LinkedIn
Instagram
Facebook
YouTube
X
Other

Legal Disclaimer:

EIN Presswire provides this news content “as is” without warranty of any kind. We do not accept any responsibility or liability
for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this
article. If you have any complaints or copyright issues related to this article, kindly contact the author above.

Media gallery