How to get started with AI agents in business

Remember the excitement of watching R2-D2 in Star Wars solve problems independently while helping the heroes? That's the essence of agentic artificial intelligence (AI) – creating systems that think and act autonomously, much like the robots from our favorite sci-fi films. But unlike the fictional worlds, this technology is becoming a reality, with game-changing applications set to revolutionize how businesses operate.

Consider supply chain management, where data scientists optimize order fulfillment with a dynamic army of AI agents. A master agent handles strategic planning, while specialist agents track inventory and coordinate shipments. Controller agents manage timing, and execution agents integrate seamlessly with databases, shipping systems, and customer platforms. This approach ensures smooth, accurate, and efficient order processing under vigilant human oversight.

While machines orchestrating complex business workflows through reasoning, planning, and independent decision-making is a significant advancement, the technology is still evolving and faces several challenges.

Let's explore the three key areas that will bring this innovation to life.

Agentic AI thrives on data. The quality, variety, and timeliness of data determine how well an agent can learn and make effective decisions. As these systems scale and become more advanced, their data demands grow substantially, presenting significant challenges around data infrastructure, integration, and security.

Here's what to consider when building agentic AI systems:

• Integrate data efficiently: Break down silos using data lakes and integrated data pipelines, enabling agents to access a unified view of information
• Prioritize data security: Implement encryption, authentication protocols, and real-time monitoring to guard against unauthorized access
• Comply with regulations: Ensure adherence to all applicable AI rules and privacy laws to safeguard personal data from misuse
• Incorporate foundational models: Use large language models (LLMs) and multimodal models to process diverse data, including text, images, and video
• Boost your computational power: Make the most of cloud and distributed systems to support real-time processing and scalability
• Design a modular architecture: Allow for the addition or modification of system capabilities without extensive overhauls
• Manage large language model operations (LLMOps): Oversee agent API controls, observability, and performance tracking, and establish feedback loops

Agentic AI ushers in a new way of working, often requiring a cultural shift to fully harness its potential. Success depends not only on the technology but also on its adoption through effective AI change management.

Here's how you can begin to tackle the cultural challenges:

• Build trust: Foster transparency around AI decisions. Explainable AI helps employees, stakeholders, and customers understand how these systems work
• Redefine roles: Reskill staff for tasks that emphasize judgment and oversight, creating a workforce equipped to guide and monitor AI effectively
• Invest in adaptability: Help employees feel empowered to work with agentic AI while embracing new responsibilities
• Encourage cross-functional collaboration: Promote collaboration across IT, data science, operations, and risk management to align AI with broad organizational goals

Governance in agentic AI comes with its own set of challenges, especially given agents' capacity for autonomous action. Accountability can become fuzzy, particularly in regulated industries. That's why embedding responsible AI practices early in the process is critical.

Here's how to promote the safe and ethical use of AI:

• Implement tight governance controls: Develop and enforce clear policies on how agents can make decisions and what actions are allowed. Integrate these policies within the agent's decision-making framework, often called compliance by design. This process will make audits easier and help establish accountability when issues arise
• Address bias risks: Set a responsible AI framework with clear ethical guidelines for data use, run regular bias audits, and incorporate diverse perspectives to foster fairness and impartiality
• Uphold compliance with industry regulations: Take proactive measures like regular audits, maintaining detailed records, and implementing real-time monitoring. These practices build trust with customers and regulators

Agentic AI holds massive potential, but it's up to us to mold it into something practical, secure, and revolutionary. With the right foundations, we can transform autonomous AI from a concept to a powerful tool driving business innovation for years to come. Let's take the leap and make it happen.