Jeff Auriemma
GraphQL is in Gartner’s Trough of Disillusionment in the 2026 Gartner® Hype Cycle™ for APIs. Good. The trough is what happens when expectations set during the hype phase meet the reality of actual production requirements. People stop hyping and start asking the key questions: Does this thing work? Does it solve a problem we actually have? Can we operate it in production without making everyone miserable?
Those are the right questions, and any proposed platform tech has to answer them.
GraphQL adoption vs. GraphQL “readiness”
With GraphQL in the trough, Gartner notes this observation:
“Despite the potential, GraphQL adoption for AI and agentic workflows have not translated to increased adoption due to its limited penetration in key agentic use cases.”
That strikes us as basically right. MCP and other agent-oriented protocols arrived with an obvious, legible connection to AI. GraphQL, like other mature technologies, is adapting. Most companies adopted it years earlier to solve the problems of the day: frontend development, service integration, data aggregation, and the general difficulty of getting a large organization’s systems to behave as if they belong to the same company.
But we think the sequencing matters. Are agents causing companies to adopt GraphQL? There’s some early signal that they are. But a more immediately interesting question is whether companies that already adopted GraphQL are better prepared to put agents into production. The answer is an unequivocal “yes.” There is another interesting signal in the Gartner report. GraphQL is in the trough, but Gartner forecasts that it will reach the Plateau of Productivity in less than two years. That’s sooner than MCP, gRPC, and AI Agent Communication Protocols. In our read, that tells us something about maturity. GraphQL is no longer new enough to receive credit for its potential. It has to earn credit for being useful. That is a healthier place for infrastructure technology to be.
It helps that GraphQL is not a speculative protocol waiting for its first serious workload. It is a Linux Foundation-backed open standard that has been used at enterprise scale for years. This matters when you are choosing infrastructure that could outlive several executive teams, architecture strategies, and AI hype cycles. Proprietary protocols can disappear when their owner’s priorities change. Open standards have a chance to compound.
In our view, Gartner’s own description of GraphQL makes this case more plainly. The report describes its capacity to serve as:
“A unified enterprise data model, decoupled from the underlying service implementations. This shared schema fosters a common understanding of business data across teams, streamlines integration efforts, and accelerates the delivery of new features and digital products. As GraphQL adoption grows, it also supports advanced use cases, such as enterprisewide orchestration and AI integration, further amplifying its business value.”
You could remove “AI integration” from that paragraph and it would still describe why many organizations adopted GraphQL in the first place. Put it back in, though, and the connection becomes pretty obvious. Agents need to find data, understand how it relates, retrieve what they are allowed to retrieve, and take actions across systems. A governed graph does not solve the entire agent problem—nothing does—but it gives agents a coherent surface over systems that were not designed with agents in mind.
What “built the graph first” looks like in production
American Airlines is a useful example because its story does not begin with an LLM, but it ends with one.
Its core system of record is SABRE, which dates to the 1960s. Years before every software roadmap acquired an “agentic” section, the company built a federated graph spanning customer, loyalty, reservations, and check-in experiences across web, mobile, and kiosks. When its CEO later asked the organization to put AI agents into production on a 90-day delivery cycle, the company did not also have to invent a context layer from scratch. It had already done the foundational architecture work.
As Senior Principal Architect Pragadeesh Prakasam recently explained at the 2026 AI Agent Conference, the team placed Apollo’s MCP Server on top of the graph it already operated. Persisted queries became the agents’ tool surface. Existing field-level security scopes applied to agent interactions. Authentication, governance, and observability were not exciting new items on an AI infrastructure backlog; they were capabilities the company already had.
“We could just focus on the experience,” Prakasam said. “Not on rebuilding the plumbing.”
This is the part we think gets lost when people compare adoption curves. GraphQL did not need to arrive wearing an AI costume to be useful for AI. American Airlines built a graph to solve one generation of software problems and discovered that the same investment helped with the next one. Booking.com, Indeed, and Yahoo! have followed a similar pattern: build a mature, governed graph, then give agents access to that foundation.
So yes, Gartner is right that GraphQL has not spread through agent use cases with the speed or visibility of MCP. But that is a claim about adoption speed, not about value. GraphQL can still be valuable infrastructure for agents even if agents are not yet driving much new adoption of it. In practice, some of the companies best positioned to ship agents today are the ones that built the graph first. We do not think that is a coincidence.
Where AI gateways fit and where they fall short
The report’s treatment of AI gateways makes the connection even more interesting. Gartner defines an AI gateway as:
“A tool that acts as an intermediary between applications and various artificial intelligence services or models. Its purpose is to simplify and manage connectivity between AI applications, agents, LLMs and enterprise applications by providing a central point to enable security, governance and observability of AI workloads.”
Most AI gateways today focus on traffic management: model routing, rate limits, token controls, and defenses against attacks such as prompt injection. We at Apollo believe that all of that is necessary. It is also insufficient.
A gateway can control where a request goes without understanding what the underlying data means. It can know that an agent is allowed to call an API without knowing whether the agent should see a particular customer field returned by that API. Without a semantic model underneath it, an AI gateway risks becoming a very sophisticated traffic cop. That’s useful, but lacking the semantics to answer many of the authorization and governance questions that make managing enterprise agents difficult.
Our bet is that the category will move toward combining both layers: gateway controls for AI traffic and a governed model of enterprise data and capabilities. Agents need routing and rate limits, but they also need context. They need to know what systems contain, how concepts relate, what actions are available, and what a particular user or agent is authorized to see.
In our view, Apollo GraphOS can play that role when it is implemented as a context layer for agents. It provides a centralized way to control, observe, and govern what agents do across tools and APIs, using the same federated graph that companies such as American Airlines, Indeed, and Yahoo! already operate in production. The field-level access controls, authentication, and governance are not decorations added after the agent demo gets executive attention. They are part of the graph. The ceiling is much higher with these capabilities in hand.
MCP and GraphQL solve different and complementary problems, and American Airlines’ architecture is interesting precisely because it uses both. The hard part of enterprise agents is providing the model useful context while preserving the security, governance, and observability the organization already requires. Companies with a mature graph have a head start because they have already spent years doing that work.
Call it the trough if you want. We call it a head start.
If you are sorting through what GraphQL, MCP, AI gateways, and the growing pile of agent protocols mean for your architecture, Apollo is offering a complimentary copy of the full 2026 Gartner Hype Cycle for APIs research. If you want to talk through how GraphQL fits into your API strategy, get in touch. *Gartner, Hype Cycle for APIs, 2026, By Shameen Pillai, Mark O’Neill, 22 May 2026. *
*Gartner and Hype Cycle are trademarks of Gartner, Inc. and/or its affiliates. *
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.