Solo.io Bridges Cloud-Native and AI-Native Worlds

The News

Solo.io has unveiled three major initiatives that are redefining how developers build, secure, and scale AI-native systems. At the center of this launch is Gloo Gateway 2.0, an open, CNCF-aligned API gateway that integrates kgateway and the Kubernetes Gateway API, providing a unified control plane purpose-built for modern, agentic workloads. This evolution enables developers to manage AI-driven and traditional services with consistent policies, observability, and performance across environments.

Complementing this is Solo Enterprise for agentgateway, the industry’s first enterprise-ready distribution of an AI-native data plane. Designed specifically for multi-agent communication and Model Context Protocol (MCP) governance, this platform extends Solo.io’s leadership in secure service connectivity into the world of AI agents and intelligent applications. It gives organizations the foundation to orchestrate, monitor, and govern how models and agents interact—critical for enterprises scaling AI safely and efficiently.

Finally, Solo Labs for MCP offers a structured, hands-on engagement program to help enterprises accelerate the journey from AI pilot projects to production deployments. By combining technical guidance, best practices, and real-world implementation support, Solo Labs ensures organizations can operationalize AI systems securely and at scale.

Together, these initiatives position Solo.io as a driving force in bridging cloud-native infrastructure with the emerging world of AI-native architectures, setting a new standard for how enterprises connect, secure, and govern intelligent applications.

The Convergence of Cloud-Native and AI-Native Networking

Solo.io’s announcements represent a critical inflection in the application development landscape, one where API gateways, data planes, and AI agents begin to converge. Built on open standards like Kubernetes Gateway API and CNCF’s kgateway, Gloo Gateway 2.0 could extend the model from traditional ingress routing to AI-aware, multi-directional traffic control.

This evolution mirrors trends seen in theCUBE Research and ECI’s Day 0 and Day 1 AppDev studies, where 76.8% of organizations have adopted GitOps and over 50% report high confidence in scaling workloads, yet 27.5% still face skill gaps and 24% struggle with complexity. Gloo’s integration with ambient mesh and OpenTelemetry speaks directly to these pain points, aiming to abstract away operational overhead while supporting observability and compliance across distributed environments.

From API Gateways to Agentic Control Planes

The introduction of Solo Enterprise for agentgateway underscores a paradigm shift: API gateways are no longer just about HTTP routing; they are evolving into AI connectivity layers that manage model interactions, tool calls, and multi-agent orchestration.

According to ECI Research’s Day 2 data, 71% of enterprises already leverage AIOps, and 58.1% view AI-driven automation as “table stakes” for observability and operations. Agentgateway’s design aims to address these trends by embedding governance, authentication, and runtime policy enforcement at the AI protocol level. These are capabilities developers increasingly need as AI workflows move from experimentation to production.

Its integration with MCP (Model Context Protocol) and support for AI-specific security constructs (tool fingerprinting, attestation trails, and least-privilege tokenization) brings the rigor of DevSecOps into the AI era.

Moving From Pilot to Production

Most enterprises remain stuck in the “POC purgatory” phase of AI adoption, where experimentation abounds but production deployment lags. ECI Research shows 59.3% of teams are confident in production readiness, but 42.8% still report partial or manual deployment automation.

Solo’s dual approach (agentgateway for runtime governance and MCP Labs for full-lifecycle adoption) directly aims to target this production gap. By providing a structured engagement model, Solo Labs could reduce the trial-and-error cycle that often plagues AI system integration, offering a repeatable blueprint for MCP connectivity, observability, and compliance.

For developers, this translates into faster path-to-production pipelines where AI workloads can be governed with the same policy-driven automation long established in the cloud-native ecosystem.

Designing for Agentic Workloads

Developers are entering a new era of agentic application design, where APIs, tools, and LLMs interact autonomously across mesh architectures. With 89.6% of organizations already using AI-based developer tools, platforms like Gloo Gateway 2.0 and agentgateway may provide the connective tissue between developer intent and AI-driven execution.

The combination of Envoy for traditional cloud-native routing and Rust-based agentgateway for AI-native data flow introduces a flexible data plane model, one that developers can programmatically extend via open APIs and OpenTelemetry integrations. This flexibility is key for scaling AI agents securely across hybrid and multi-tenant environments, especially as 61.8% of organizations now deploy hybrid architectures.

Looking Ahead

Solo.io’s trio of releases reflects a broader transformation across the software ecosystem: the fusion of cloud-native and AI-native infrastructure into a unified control fabric. As enterprises standardize on protocols like MCP and adopt open, policy-driven gateways, developers will likely gain the tools to manage not just traffic, but intelligence itself.

For next steps, I would recommend:

Standardize AI-Native Protocols: Encourage enterprise adoption of Model Context Protocol (MCP) and similar open standards to enable consistent communication and governance across AI agents and models.
Adopt Policy-Driven Gateways: Leverage open, CNCF-aligned gateways like Gloo Gateway 2.0 to enforce security, observability, and operational policies across both AI and traditional workloads.
Enable Agentic Connectivity: Transition APIs from simple request-response interfaces to context-aware, conversational endpoints that facilitate intelligent interactions between applications and AI models.
Integrate Cloud-Native and AI-Native Infrastructure: Build unified control fabrics where networking, compute, and AI orchestration are managed under a single operational plane.
Invest in Governance and Interoperability: Prioritize security, open standards, and cross-platform interoperability to ensure scalable, compliant AI deployments across hybrid and multi-cloud environments.
Support Developer Enablement: Provide hands-on programs, tooling, and guidance (like Solo Labs for MCP) to accelerate the shift from AI pilots to production-ready, agentic applications.

For the industry, this signals the beginning of the “agentic connectivity” era, where APIs evolve into conversational, context-aware interfaces between applications and AI models. Solo.io’s continued investment in open governance, interoperability, and security positions it as a key contributor in defining this next-generation control plane, one where AI and networking are no longer separate disciplines, but parts of the same intelligent system.

Paul Nashawaty

Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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