From generative AI APIs to agentic AI models, new technologies are redefining what’s possible and what efficiency looks like in enterprise workflows. Understanding these emerging trends, the growing AI value chain, and how forward-thinking enterprises are using APIs to scale innovation securely, can help unlock your path to AI success.
AI adoption by the numbers
AI adoption is accelerating around the globe. Gartner reports that 29% of organizations in the US, Germany, and the UK had deployed generative AI by late 2023. It also projects that, by 2026, over 80% of independent software vendors will have embedded generative AI capabilities in their enterprise applications. That’s up from under 1% in 2023.
Gartner also points out that it’s not just humans who are ramping up demand for innovative tech solutions. It predicts that, by 2026, over 30% of the increase in demand for APIs will come from AI and tools using large language models (LLMs).
What’s trending: LLMs, MCP, and agentic AI
LLMs are AI systems trained on massive amounts of data. They power generative AI solutions, producing text or other content in answer to questions and prompts.
Model Context Protocol (MCP) servers help AI systems securely interact with external services, tools and resources in a standardized way. MCP enables LLMs and AI agents to discover and use tools, and to interact with their environments.
Agentic AI is where autonomous AI agents make decisions about how to achieve tasks, set goals, and take actions in order to deliver on those goals. This more independent AI goes well beyond generative AI, with agents taking the initiative and acting autonomously.
With AI agents on the rise, the AI value chain is growing rapidly. This interconnected system of models, interfaces, data and governance tools enables enterprises to adopt, integrate and scale AI technologies in a composable manner that supports flexibility, security and efficiency, while avoiding vendor lock-in.
Where MCP fits – and where it falls short
Many enterprises were excited about the potential of MCP, with its promise of enabling a plug-and-play tooling approach to LLM clients. However, as Tyk CEO Martin Buhr recently discussed, there is a danger that MCP is “overhyped and undercooked.” A lack of server-side components has led to significant potential for security vulnerabilities, while the absence of established versioning practices, with standardized compatibility and change management, is a whole other headache.
The rise of AI agents and what they mean for enterprise teams
Many enterprise teams are now turning to AI agents to deliver automation and efficiency in their workflows. Agents can autonomously handle repetitive workflows, making informed decisions on how to achieve goals and orchestrating tools via APIs. This has vast potential for reshaping productivity at scale – provided the right governance guardrails and oversight are in place.
For enterprises to effectively harness the potential of agentic AI while also checking all the right security and compliance boxes, governance frameworks are a must. Agentic AI systems must walk a fine line between autonomy and clarity over decision-making, with each agent’s actions needing to be visible. This is where API governance comes into play as a crucial element of AI readiness in relation to enterprise workflows…
Why a universal API management platform is essential to AI success
With APIs feeding data to your LLMs and enabling the flow of data between models, agents, and other tools and resources, robust API management is more important than ever. API management platforms such as Tyk enable you to manage, secure, govern and scale AI APIs, facilitating seamless and reliable workflow integrations. Tyk AI Studio is leading the way with this, providing a governance-based, tool use approach that supports you to embrace all that AI offers in a well-governed, strategic and secure manner that is eminently scalable.
To find out more about the role of APIs in empowering AI in your enterprise workflows, download Tyk’s new ebook: AI, APIs, and AI readiness.
