AI agent development platforms enable developers to create and manage AI agents through code, configuration and metadata, offering flexibility and control for implementing complex logic and integrations. Many of the tools provide low-code, programmatic, or natural language development experiences. This category includes both low-code and pro-code offerings. These platforms address the critical gap between raw LLM capabilities and production-ready AI agents, providing developers with the frameworks, tools, and infrastructure needed to create agents that can reliably operate in enterprise environments.
Gartner defines AI application development platforms as those that offer the required technology and workflows to design, build, test, and deploy AI applications. These platforms provide access to foundation models and the capability to ground and place guardrails around them. Software engineering teams use these platforms to build AI applications, such as assistants, agents, and multimodal applications. Software engineering leaders face increasing pressure to incorporate AI into their products. AI application development platforms host the necessary tooling for enterprise developers to build AI assistants, agents, and multimodal apps without extensive knowledge of machine learning. AI application development platforms focus on providing the features developers need to ground models with organizational knowledge. They also reduce risk by implementing responsible AI processes and guardrails within their AI applications. These platforms help scale the development of AI-embedded applications by offering governance, evaluation metrics, and support throughout the application life cycle. Not every platform will offer access to first-party models or application-testing capabilities.
Gartner defines AI evaluation and observability platforms (AEOPs) as tools that help manage the challenges of nondeterminism and unpredictability in AI systems. AEOPs automate evaluations (“evals”) to benchmark AI outputs against quality expectations such as performance, fairness and accuracy. These tools create a positive feedback loop by feeding observability data (logs, metrics, traces) back to evals, which helps improve system reliability and alignment. AEOPs can be procured as a stand-alone solution or as part of broader AI application development platforms.