Why Agents are the Next Frontier of Generative AI

Generative AI (gen AI) has amazed us with its capabilities to extract insights and generate content across various mediums. Now, the technology is evolving from knowledge-based tools, like chatbots, to action-based "agents" that can execute complex workflows. These agents could function as virtual coworkers, completing tasks and collaborating seamlessly with humans.

The Rise of Agentic Systems Agentic systems, digital systems capable of independent interaction, are not new. However, the integration of gen AI's natural language capabilities is revolutionary. These systems can plan actions, use online tools, collaborate with other agents and humans, and continuously improve. For instance, a virtual assistant could handle a personalized travel itinerary, while an engineer could instruct a programmer agent to develop and deploy software.

Implementation and Potential Building agentic systems traditionally required complex programming. Gen AI changes this by leveraging foundation models trained on extensive unstructured data, allowing the systems to adapt to various scenarios. This adaptability means tasks that once needed manual intervention can now be automated efficiently. For example, planning a business trip with multiple variable inputs can be streamlined by these agents.

Business Value The real value of gen AI agents lies in their ability to automate complex and open-ended use cases, such as:

1. Managing Multiplicity: Handling unpredictable workflows that rule-based systems can't manage.
2. Natural Language Direction: Simplifying the automation process by using natural language instructions instead of code, enabling non-technical employees to automate tasks.
3. Integration with Existing Tools: Communicating and working across a broader digital ecosystem, utilizing software applications and other foundation models to complete tasks.

How Agents Work Agents support high-complexity use cases by breaking down workflows into manageable tasks. The process involves:

1. User Instruction: The user provides a natural-language prompt.
2. Planning and Execution: The agent system processes the prompt, breaks it down into tasks, assigns them to subagents, and executes them.
3. Iterative Improvement: The agent requests user feedback and iterates to improve the output.
4. Final Action: The agent completes the necessary actions to fulfill the task.

Future Outlook Although still in its nascent phase, gen AI agent technology is rapidly developing, with significant investments from tech giants like Google, Microsoft, and OpenAI. As the technology matures, gen AI agents could become as ubiquitous as chatbots, unlocking new levels of productivity and innovation in businesses.

This transformative leap from information to action signifies the next frontier for generative AI, promising to revolutionize how we work and interact with digital systems.

Reference: McKinsey Digital (July 24, 2024)

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