How a new protocol is making Agentic AI the standard
Agentic AI is considered the next stage in the evolution of artificial intelligence – but in practice, there has been no connecting technical foundation until now.
The Model Context Protocol (MCP) is now establishing itself as an open standard that does exactly that: it makes specialised AI agents interoperable, scalable and ready for use in real business processes. Read the commentary by Roman Zednik, Field CTO at Tricentis, to find out why MCP could become the USB-C of AI – and what that means for areas such as software quality.
The next phase of AI integration in companies does not begin with a new model – but with a protocol. The Model Context Protocol (MCP) is a new technical standard that has the potential to bring Agentic AI out of the research niche and into widespread business practice. MCP was originally developed by Anthropic and is now being adopted by major players such as OpenAI, Microsoft and Google.
The common language of AI agents
What sets MCP apart is its role as a common ‘interface’ for AI agents: a standardised framework through which specialised agents – for analysis, automation or data retrieval, for example – can communicate with each other securely, contextually and in an orchestrated manner. This transforms many isolated AI functions into an interactive, productive AI system for the first time.
MCP delivers exactly what many AI initiatives have failed to achieve so far: a modular infrastructure that enables interoperability – without individual integrations or proprietary silos. The effect is comparable to USB-C in the hardware sector: a uniform, universally connectable standard that reduces complexity and accelerates innovation.
What makes this approach special is not only the technical architecture, but also the paradigm shift it brings with it. AI is no longer thought of as an isolated tool, but as a networked system of specialised agents. Each of these agents performs a specific function – such as extracting information, processing data or executing automated steps – and draws on common protocols and contextual information.
Pioneering work in quality assurance
An example from software quality assurance shows how this new logic works in practice – and who is already using it successfully. Tricentis was one of the first companies to embed the MCP protocol in test automation, creating a blueprint for agent-based AI workflows. For example, a requirement formulated in natural language – for example, from a Jira ticket – can be analysed by an AI agent and converted into structured test cases. A second agent automatically generates the appropriate test data, a third transfers the test case to the test environment, and a fourth visually prepares the results, including risk assessment and management report. Each of these steps runs via MCP – as a common language that makes the interaction possible in the first place. All this works via standardised prompts and interfaces – without manual scripting or lengthy integration. The decisive added value lies in the combination: MCP finally turns loose AI functions into real workflows – tailored to the IT system landscape and the context of the respective organisation.
From AI experiment to operational reality
This brings AI out of the experimental phase. While many applications have been limited to proof-of-concepts in recent years, MCP creates the conditions for productive, sustainable solutions. But with this freedom comes responsibility. Anyone orchestrating AI agents must ensure that they function reliably, deliver traceable results and guarantee the protection of sensitive data.
The discussion about data protection, hallucinations and the testability of generative systems will continue to gain importance with the spread of agentic AI. Nevertheless, the direction is clear: MCP is creating a new technical basis on which AI can not only be used, but also controlled.
Companies that embrace it early on will not only gain efficiency but also strategic flexibility. They will become less dependent on individual providers, reduce integration efforts and create an architecture that grows with the demands of their business.
Conclusion: MCP makes Agentic AI ready for practical application
MCP is more than a technical building block – it is the missing link that will bring Agentic AI to industrial maturity. Anyone who wants to not only use AI but also orchestrate it will not be able to ignore this standard in the future. Only with a common protocol can specialised AI agents become a networked system that can be flexibly integrated into existing infrastructures – securely, transparently and scalably.
This makes it clear that the future of AI in companies will not be determined solely by the performance of individual models, but by the architecture that connects them. MCP provides the foundation for a new form of AI integration: as a key technology for anyone who sees AI not just as a tool, but as a strategic component of their IT landscape. A real turning point – not just for software testing.
