AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals based on logic and reasoning. AI agents offer the promise to create systems for complex workflows, deliver on goals and learn from outcomes. They can improve marketing’s ability to meet customer expectations, with reasoning, learning and context. AI agency exists on a spectrum, ranging from current systems that act on user guidance to future systems that learn and perform tasks autonomously.
Gartner defines AI agents in the HR technology submarket as autonomous or semiautonomous software entities that leverage AI techniques to perceive, make decisions and take actions within HR environments. These agents enable HR organizations to automate and execute multistep HR tasks (such as information retrieval, workflow initiation and communicating with end users) to achieve HR goals. AI agents in HR can function independently of core HR systems or be integrated into broader HR platforms, such as human capital management suites, which are essential components of the HR technology stack.
Gartner defines business orchestration and automation technologies (BOAT) as a consolidated software platform that delivers enterprise process automation by enabling capabilities including orchestration of business processes, enterprise connectivity, low code development and agentic automation. A BOAT platform includes a cross section of certain capabilities from different markets such as business process automation (BPA), low-code application platforms (LCAP), integration platform as a service (iPaaS), intelligent document processing (IDP), robotic process automation (RPA), collaborative workflow management and document management. However, this list is not necessarily all-encompassing.
Gartner defines an enterprise AI assistant (EAIA) as an AI-first application, powered by one or more GenAI models. EAIAs are designed to augment human capabilities and support human-led actions by offering agentic RAG search, agentic tools and enterprise-grade security. The EAIA is becoming an essential “front door” application for employees to routinely use GenAI to streamline content creation, research, analysis and collaboration, and to access AI agents.
Gartner defines enterprise AI coding agents as autonomous or semiautonomous software engineering solutions that perceive context, translate human intent into multistep plans, and execute and verify those steps across code, tests and related engineering artifacts. Enterprise AI coding agents enable developers to prompt, steer, delegate and supervise workflows through synchronous or asynchronous modes with varying human oversight, delivered via IDEs, CLIs, cloud environments and collaboration platforms. This market focuses on solutions designed for enterprise software engineering organizations and their requirements for governance, integration and scale. Enterprise AI coding agents are an evolution of AI code assistants. While code assistants primarily suggest code, complete snippets and answer questions in a chat interface, enterprise AI coding agents enable software engineering teams to delegate and offload a greater portion of development work through dynamic task planning and tool use.
Gartner defines the generative AI (GenAI) knowledge management apps/general productivity submarket as technologies that enable companies to better retrieve and contextualize information and insight from their knowledge bases, including enterprise AI search, conversational AI platforms, and productivity tools for communications and content development.
Generative AI (GenAI) model providers focus on developing and providing generative AI technologies and make them available to other developers, businesses and general public through APIs or commercial licenses. Generative AI refers to technologies that can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. This layer of vendors offers access to commercial or open-source foundation models such as LLMs and other types of generative algorithms (such as GANs, genetic/evolutionary algorithms or simulations). These models can be provided for developers to embed into their applications or be used as base models for fine-tuning customized models for their software offerings or internal enterprise use cases. This helps businesses gain the benefits of advanced generative AI technologies while avoiding the high costs, expertise requirements and time needed to develop these technologies in-house. Please note that this market is based on Beta research and is continuously evolving. We will be making changes as and when there are new updates.