Digital business applications usoe

By moving from information to action—think virtual coworkers able to complete complex workflows—the technology promises a new wave of productivity and innovation.

Over the past couple of years, the world has marveled at the capabilities and possibilities unleashed by generative AI (gen AI). Foundation models such as large language models (LLMs) can perform impressive feats, extracting insights and generating content across numerous mediums, such as text, audio, images, and video. But the next stage of gen AI is likely to be more transformative.

We are beginning an evolution from knowledge-based, gen-AI-powered tools—say, chatbots that answer questions and generate content—to gen AI–enabled “agents” that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Broadly speaking, “agentic” systems refer to digital systems that can independently interact in a dynamic world. While versions of these software systems have existed for years, the natural-language capabilities of gen AI unveil new possibilities, enabling systems that can plan their actions, use online tools to complete those tasks, collaborate with other agents and people, and learn to improve their performance. Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner. A virtual assistant, for example, could plan and book a complex personalized travel itinerary, handling logistics across multiple travel platforms. Using everyday language, an engineer could describe a new software feature to a programmer agent, which would then code, test, iterate, and deploy the tool it helped create.

Agentic systems traditionally have been difficult to implement, requiring laborious, rule-based programming or highly specific training of machine-learning models. Gen AI changes that. When agentic systems are built using foundation models (which have been trained on extremely large and varied unstructured data sets) rather than predefined rules, they have the potential to adapt to different scenarios in the same way that LLMs can respond intelligibly to prompts on which they have not been explicitly trained. Furthermore, using natural language rather than programming code, a human user could direct a gen AI–enabled agent system to accomplish a complex workflow. A multiagent system could then interpret and organize this workflow into actionable tasks, assign work to specialized agents, execute these refined tasks using a digital ecosystem of tools, and collaborate with other agents and humans to iteratively improve the quality of its actions.

In this article, we explore the opportunities that the use of gen AI agents presents. Although the technology remains in its nascent phase and requires further technical development before it’s ready for business deployment, it’s quickly attracting attention. In the past year alone, Google, Microsoft, OpenAI, and others have invested in software libraries and frameworks to support agentic functionality. LLM-powered applications such as Microsoft Copilot, Amazon Q, and Google’s upcoming Project Astra are shifting from being knowledge-based to becoming more action-based. Companies and research labs such as Adept, crewAI, and Imbue also are developing agent-based models and multiagent systems. Given the speed with which gen AI is developing, agents could become as commonplace as chatbots are today.

What value can agents bring to businesses?

The value that agents can unlock comes from their potential to automate a long tail of complex use cases characterized by highly variable inputs and outputs—use cases that have historically been difficult to address in a cost- or time-efficient manner. Something as simple as a business trip, for example, can involve numerous possible itineraries encompassing different airlines and flights, not to mention hotel rewards programs, restaurant reservations, and off-hours activities, all of which must be handled across different online platforms. While there have been efforts to automate parts of this process, much of it still must be done manually. This is in large part because the wide variation in potential inputs and outputs makes the process too complicated, costly, or time-intensive to automate.

Gen AI–enabled agents can ease the automation of complex and open-ended use cases in three important ways:

• Agents can manage multiplicity. Many business use cases and processes are characterized by a linear workflow, with a clear beginning and series of steps that lead to a specific resolution or outcome. This relative simplicity makes them easily codified and automated in rule-based systems. But rule-based systems often exhibit “brittleness”—that is, they break down when faced with situations not contemplated by the designers of the explicit rules. Many workflows, for example, are far less predictable, marked by unexpected twists and turns and a range of possible outcomes; these workflows require special handling and nuanced judgment that makes rules-based automation challenging. But gen AI agent systems, because they are based on foundation models, have the potential to handle a wide variety of less-likely situations for a given use case, adapting in real time to perform the specialized tasks required to bring a process to completion.
• Agent systems can be directed with natural language. Currently, to automate a use case, it first must be broken down into a series of rules and steps that can be codified. These steps are typically translated into computer code and integrated into software systems—an often costly and laborious process that requires significant technical expertise. Because agentic systems use natural language as a form of instruction, even complex workflows can be encoded more quickly and easily. What’s more, the process can potentially be done by nontechnical employees, rather than software engineers. This makes it easier to integrate subject matter expertise, grants wider access to gen AI and AI tools, and eases collaboration between technical and nontechnical teams.
• Agents can work with existing software tools and platforms. In addition to analyzing and generating knowledge, agent systems can use tools and communicate across a broader digital ecosystem. For instance, an agent can be directed to work with software applications (such as plotting and charting tools), search the web for information, collect and compile human feedback, and even leverage additional foundation models. Digital-tool use is both a defining characteristic of agents (it’s one way that they can act in the world) but also a way in which their gen AI capabilities can uniquely be brought to bear. Foundation models can learn how to interface with tools, whether through natural language or other interfaces. Without foundation models, these capabilities would require extensive manual efforts to integrate systems (for example, using extract, transform, and load tools) or tedious manual efforts to collate outputs from different software systems.

How gen AI–enabled agents could work

Agents can support high-complexity use cases across industries and business functions, particularly for workflows involving time-consuming tasks or requiring various specialized types of qualitative and quantitative analysis. Agents do this by recursively breaking down complex workflows and performing subtasks across specialized instructions and data sources to reach the desired goal. The process generally follows these four steps (Exhibit 1):

1. User provides instruction: A user interacts with the AI system by giving a natural-language prompt, much like one would instruct a trusted employee. The system identifies the intended use case, asking the user for additional clarification when required.
2. Agent system plans, allocates, and executes work: The agent system processes the prompt into a workflow, breaking it down into tasks and subtasks, which a manager subagent assigns to other specialized subagents. These subagents, equipped with necessary domain knowledge and tools, draw on prior “experiences” and codified domain expertise, coordinating with each other and using organizational data and systems to execute these assignments.
3. Agent system iteratively improves output: Throughout the process, the agent may request additional user input to ensure accuracy and relevance. The process may conclude with the agent providing final output to the user, iterating on any feedback shared by the user.
4. Agent executes action: The agent executes any necessary actions in the world to fully complete the user-requested task.