The AI agent landscape is evolving rapidly — and with it, the need for enterprise leaders to separate genuine innovation from hype. As interest in agentic AI explodes, so too does market confusion, fueled by vague promises and rebranded technologies.

According to Gartner®, “Many vendors are contributing to the hype by engaging in ‘agent washing,’ rebranding existing products, such as AI assistants, RPA tools and chatbots, to capture buyers’ attention without substantial agentic capabilities.”

This explosion of vendor activity has created what Gartner® calls “a fragmented market landscape that is hard to navigate.” But for those who understand how to evaluate platforms and align them to meaningful use cases, the opportunity is significant.

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Understanding the Landscape

Gartner® defines AI agents as“autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments.” These agents can be created, deployed, and managed through a growing variety of platforms that differ in scope, complexity, and user skill requirements.

AI agent platforms fall into four core categories:

• Prebuilt agents - Ready-to-use agents built for specific domains or tasks
• No-code agent builders - Tools for business technologists to create agents without coding
• Agent development platforms - Code-first environments with rich customization options
• AI agent training platforms - Environments for training agents, often using reinforcement learning in simulations

"LLM-based agents are the most visible part of the market,” Gartner® notes, “but they are not the only way to build AI agents. For example, more sophisticated AI agents can be built into simulation platforms using techniques like reinforcement learning for specific use cases.”

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Build vs. Buy: Start Simple, Scale Smart

In theory, organizations can start from scratch. As Gartner® explains, “It is possible to build AI agents from scratch, manually integrating different components like AI models and codelibraries. However, this requires a high level of expertise and is both challenging and time-consuming for most organizations.”

Unless you’re pursuing a highly specialized use case — such as robotics, gaming, or advanced industrial applications — building from scratch is rarely the best starting point. Instead, Gartner® recommends leveraging prebuilt agents or no-code builders to reduce complexity, speed up deployment, and build internal confidence.

“Begin your AI agent journey by experimenting with prebuilt agents and no-code builders to navigate the extensive range of buy-to-build options without feeling overwhelmed.”

For organizations that do require deeper customization or advanced behavior modeling, AI agent training platforms and simulation environments can offer the flexibility and depth needed. But for the majority, buying — not building — is the most efficient path to business impact.

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Choosing the Right Use Cases

While the promise of AI agents spans everything from customer service to business process automation, Gartner® cautions that “their current use is mostly experimental, and only a few AI agents are in production that provide significant business value.”

To move beyond experimentation and realize real outcomes, Gartner® advises leaders to “Tackle the challenge of a fragmented market by identifying and focusing on the use cases where AI agents could be most valuable and feasible.” These might include automating multi-step business workflows, streamlining customer support across languages and channels, or assisting employees with decision support.

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Avoiding Common Pitfalls

In addition to agent-washing, Gartner® flags several risks to watch for:

• Reliability issues – especially in agents built solely on large language models
• Security and governance – with increased integration comes greater exposure
• Agent anarchy – without orchestration, multiple agents can work at cross-purposes
• Vendor lock-in and interoperability challenges – slowing flexibility and innovation
• Cost overruns – caused by unclear usage controls and variable pricing models

To manage these risks, Gartner® encourages leaders to broaden their strategy: “Evaluate AI agent training platforms that can help build AI agents for more specialized use cases,” including logic-based systems, simulations, and knowledge graphs.

And importantly, “minimize uncertainty in proving business value by initially focusing on low-risk pilot use cases that deliver tangible business outcomes before committing to significant investments and organization wide rollouts.”

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A Path Forward

Gartner® notes, “AI agents have seen a rapid surge of interest. Gartner saw a 750% increase in AI-agent-related inquiries between the second and fourth quarters of 2024, with Agentic AI becoming one of the biggest trends of the year”.

But success in this space won’t come from simply adopting the flashiest tools. It will come from thoughtful planning, grounded experimentation, and a clear-eyed view of what’s real — and what’s merely been rebranded.

As Gartner® emphasizes, “AI agent platforms have broad applicability, with potential use cases across many business domains. However, their current use is mostly experimental, and only a few AI agents are in production that provide significant business value.”

Realizing the potential of Agentic AI depends on informed, strategic evaluation and adoption.

Source:

Gartner, Innovation Insight for the AI Agent Platform Landscape,Leinar Ramos, Gabriel Rigon, et al. 26 March 2025.

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