From Multi Agent Systems to Institutional Learning in the Enterprise - with Papi Menon of Outshift by Cisco

2026-03-19 · Source: The AI in Business Podcast · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Advanced, extended

Summary

Papi Menon, VP of Product Management and Chief Product Officer at Outshift by Cisco, discusses why enterprises struggle to scale multi-agent AI systems despite early successes. The core issue is the absence of a "cognitive layer" that allows agents to share context and learn as a coordinated system, rather than operating as isolated tools. While individual agents improve within their silos, collective learning across multi-agent environments is currently lacking. Outshift by Cisco is actively developing this "Internet of Cognition" to enable shared understanding and collaborative improvement among diverse agents. Menon emphasizes that scaling requires embracing heterogeneous agent environments and building on open, interoperable foundations to avoid vendor lock-in as the technology rapidly evolves. He cites a financial services company's network debugging tool, built with Outshift, as an example of a complex multi-agent system in production, highlighting its current one-off context sharing and the future goal of a shared protocol for collective learning.

Key takeaway

For CTOs and AI Product Managers navigating multi-agent system adoption, recognize that early agent wins often stall due to a lack of shared cognitive context. Your strategy should prioritize building on open, interoperable foundations to prevent lock-in and enable collective agent learning. Begin with low-risk, high-impact projects to develop operational expertise, allowing your organization to iterate and scale effectively without compromising security or data integrity.

Key insights

Scaling multi-agent AI requires a shared cognitive layer for collective learning and contextual understanding, moving beyond isolated agent performance.

Principles

• Multi-agent systems need shared context, not just connection.
• Prioritize open, interoperable foundations for agent deployments.
• Experiment with low-risk, high-impact agent applications.

Method

Develop a "cognitive layer" or "Internet of Cognition" to enable agents to share contextual understanding and collectively learn from experiences, moving beyond syntactic communication to semantic collaboration.

In practice

• Build a digital twin for network change simulation.
• Integrate homegrown and third-party agents via common interfaces.
• Start with low-risk, high-impact business problems.

Topics

• Multi-Agent Systems
• Enterprise AI Adoption
• AI Cognition
• Interoperability
• Digital Twin Simulation

Best for: CTO, VP of Engineering/Data, AI Product Manager, Director of AI/ML, Executive, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.