Welcome to not just a world of AI agents but a multi-agent world. Yet while these functional generative AI (Gen AI) tools have great promise for personal and professional productivity, deploying them is a significant challenge for designers and developers.
The authors of a recent Deloitte report suggested agents have caught people’s attention — 26% of organizations are exploring autonomous agent development. At least 52% of executives are interested in pursuing agentic AI development, and 45% want to extend development to multi-agent systems. However, while agentic AI will be a key enabler of sustainable value, the report suggested it’s no silver bullet.
Getting Started with Agents
The best bet for percolating AI agents throughout the organization is to keep things as simple as possible. "Companies and employees that have already found ways to operationalize intelligent agents for simple tasks are best placed to exploit the next wave with agentic AI," said Benjamin Lee, professor of computer and information science at the University of Pennsylvania.
Refining the Agentic Approach
Quality data is also key, Rowan added: "It’s the foundation for AI agents to work effectively. If data is inaccurate, incomplete, or inconsistent, the agents’ outputs and actions may be unreliable or incorrect, creating both adoption and risk issues. It’s therefore essential to invest in robust data management and knowledge modeling."
Conclusion
Implementing AI agents can be costly, but it’s a necessary step towards achieving sustainable value. To get started, companies should adopt a crawl, walk, run approach, beginning with a pilot program to explore the potential of multi-agent systems in a controlled, measurable environment. Additionally, investing in robust data management, knowledge modeling, and workforce upskilling is crucial for the success of agentic AI.
FAQs
Q: What are the key barriers to deploying agentic AI?
A: Regulatory uncertainty, risk management, data deficiencies, and workforce issues are the key barriers to deploying agentic AI.
Q: What is the best approach to get started with AI agents?
A: The best approach is to keep things as simple as possible, starting with operationalizing intelligent agents for simple tasks.
Q: What is the importance of data in agentic AI?
A: Quality data is essential for AI agents to work effectively. Inaccurate, incomplete, or inconsistent data can lead to unreliable or incorrect outputs and actions.
Q: How can companies ensure the success of agentic AI?
A: Companies can ensure the success of agentic AI by investing in robust data management, knowledge modeling, and workforce upskilling, and adopting a crawl, walk, run approach to deployment.
