Four integration patterns, not one
Google Cloud has detailed Gemini Factory, a co-engineering programme designed to help software companies convert enterprise AI ideas into production integrations. The initiative brings partner engineers together with Google specialists in hubs including San Francisco, New York, Sunnyvale, London, Paris, Tel Aviv and Tokyo. Its focus is architecture, delivery and distribution rather than a new foundation model.
The programme reflects Google’s broader unified Gemini strategy. Enterprises can bring third-party systems into Gemini through Model Context Protocol connectors, Agent-to-Agent runtimes, REST APIs, declarative skills and plugins. For independent software vendors, the difficult choice is no longer simply whether to build a chatbot, but which integration primitive exposes their capability securely and with the right degree of autonomy.
From workshop to governed deployment
MCP connectors provide data and tool access registered in Gemini’s Agent Registry. Declarative skills are Markdown configurations that add domain instructions, guardrails and repeatable workflows over those connectors. Plugins package connectors and skills into a governed product that administrators can discover through Google Cloud Marketplace and make available to Gemini users.
A2A agents serve a different need. A partner whose value depends on proprietary algorithms, regulated sequences or protected intellectual property can operate an agent in its own isolated environment and expose it as a callable capability. Google says this arrangement can maintain deterministic execution and audit trails while allowing Gemini to pass shared session context and chain the result with other tools.
Marketplace distribution changes the equation
Gemini Factory workshops are intended to compress the path from an initial use case to a deployable integration. Partners receive help choosing an architecture, wiring authentication, orchestrating multi-step tool calls and presenting interactive results. Google also offers funding incentives and go-to-market support, linking technical readiness with customer activation rather than ending at a prototype demonstration.
The examples show why the primitives matter. A security-governance agent may need evidence-rich dashboards and protected analytics, while an identity agent needs tightly scoped actions, confirmation and auditable device management. A sales prospecting agent may preserve a partner’s proprietary ranking model while appearing as a capability within a broader enterprise conversation. These are materially different trust and hosting profiles.
Questions partners should resolve early
Marketplace distribution can shorten procurement for customers already operating on Google Cloud, but it raises the standard for support and lifecycle management. A listed agent must have clear permissions, version behaviour, regional availability and incident processes. Partners should avoid treating a successful workshop as proof that the integration is ready for every tenant or regulatory environment.
Before participating, an ISV should identify the narrow workflow it can improve, the systems of record involved and the actions that require human confirmation. It should also decide where customer data is processed, what context crosses the connector boundary and how administrators can revoke access. Those answers determine whether a connector, skill, plugin or isolated A2A service is the safer design.
Gemini Factory is ultimately a partner acceleration mechanism, not a guarantee of product-market fit. Its importance lies in giving enterprise AI integrations a repeatable architecture and commercial route. The strongest outcomes will come from partners that preserve their differentiated capability while adopting the identity, governance and observability expected inside an enterprise-wide assistant.
Partners should also plan for evaluation before marketplace publication. A polished demonstration may hide failures in identity propagation, ambiguous requests or partial tool completion. Test suites need realistic tenant data boundaries, revoked credentials, rate limits and downstream outages. For action-taking agents, evaluators should measure whether the system asks for confirmation at the right time rather than merely whether it reaches a successful final state.
Commercial incentives can accelerate a launch, but they should not determine architecture. A connector is attractive when the customer wants Gemini to control the workflow; an isolated A2A agent is stronger when the partner must protect execution logic or meet specialised regulatory controls. Plugins can simplify packaging, yet administrators still require a precise view of every connector, skill, permission and external processor inside that bundle.
For customers, the larger promise is a consistent front door to specialised services without flattening them into generic chat features. Success will depend on visible provenance and predictable hand-offs. Gemini should make it clear which partner capability produced an answer, what evidence it used and whether an action occurred. That transparency will matter more than the speed of the original factory workshop.
The programme may be particularly useful for vendors that already have mature APIs but lack enterprise assistant expertise. They can concentrate on domain value while Google helps with invocation and distribution patterns. Even so, partners should retain direct documentation and test environments. Marketplace packaging must not become the only way customers can understand permissions, limits, data flows or service dependencies.
Partners should define service-level objectives that cover the full chain, including Gemini invocation, connector execution and their own backend. Customers experience the combined workflow, so a fast assistant cannot compensate for an unreliable specialist service or unclear support ownership.