Imagine having a business partner that can proactively automate tasks, provide knowledgeable collaboration, and work largely free of human interactions. This is the promise of AI agents, which are transforming the future of work by providing developers, business users, and others with a role-based partner that gets work done. According to Ram Palaniappan, CTO of TEKsystems, AI agents have evolved from rule-based systems to intelligent, genAI-driven tools capable of natural language interaction, with diverse use cases in procurement, customer support, and healthcare.

So, what exactly are AI agents, and how do they differ from APIs and other web services? AI agents accept natural language and other non-technical inputs, whereas APIs only accept system-oriented inputs like JSON and XML. AI agents connect to RAG, language models, and other genAI models for relevant knowledge, and can reason the next courses of action based on their assigned role and defined guardrails. In contrast, APIs are rules-driven and require more programming time and effort to simulate different roles, decisions, and actions.

Developers building AI agents for use cases like customer service should use natural language to encode business logic instead of code, says Deon Nicholas, co-founder of Forethought. This will unlock truly agentic AI, which can take action and resolve issues, delivering a true value add. There are several types of AI agents, classified by how they make decisions and perform actions, including model-based agents, goal and utility-based agents, AI learning agents, and hierarchical agents.

When developing AI agents, there are several prerequisites to be aware of, involving platforms, data, integration, security, and compliance. A foundational platform is required to handle data integration, effective process automation, and unstructured data management. AI agents can be architected to align with strict data policies and security protocols, making them effective for IT teams to drive productivity gains while ensuring compliance. A high volume of clean and labeled data is also necessary to train and validate models, with a robust data pipeline essential for preprocessing, transforming, and ensuring the availability of real-time data streams.

Regarding security and compliance, AI agents have identities, so access to complex AI chains and knowledge graphs requires controls as if they were human. It is essential to capture frequent changes in regulations and business agreements in an access control solution and enforce them on all potential human and machine actors. Keeping up with changing business rules is essential to ensure AI agents are not developed based on outdated usage agreements.

Several enterprise platforms have announced AI agent capabilities embedded in their workflows and user experiences, including Appian, Atlassian, Cisco Webex, Cloudera, Pega, Salesforce, SAP, ServiceNow, and Workday. Some platforms also have capabilities for subject matter experts and non-technical business users to develop their own AI agents, such as Salesforce Agent Builder, Cisco Webex AI Agent Studio, ServiceNow Agentic AI, and Tray.ai Merlin Agent Builder.

Testing AI agents requires human testers, automation, and synthetic data for basic accuracy testing, while more sophisticated techniques leverage secondary AI models and use generative adversarial networks (GANs) to test at scale. To accelerate agent development, companies will need a robust set of tools that allow them to design, customize, deploy, and monitor agents at scale, including models optimized for function calling, middleware to orchestrate agents and connect them with broader enterprise toolsets, optimized runtime, technical guardrails, and governance capabilities to ensure they operate as intended.

As AI agents continue to transform the workforce, new human and AI responsibilities will likely emerge. Agentic AI will reshape the workplace and create new roles, such as 'Agent Managers' to oversee specialized agents, strategically guide these systems, and ensure alignment with business roles, similar to supervisors managing teams today. As multi-agent systems grow, HR-like departments may emerge to manage a hybrid workforce of human and AI agents, focusing on training, coordination, and performance metrics. This hybrid approach could blend human intuition with machine efficiency for better productivity.

The key to growth may not be in how easy it is to develop AI agents, but in whether and how organizations will trust them and whether employees will embrace their capabilities. As AI agents continue to redefine the workforce, it is essential for developers, business users, and others to understand the potential and implications of this technology.