We’re seeing the next frontier of the future of work unfold before our eyes. Organizations are moving beyond digital transformation into a new era: agentic transformation. This shift is driven by the widespread adoption of AI agents, autonomous software systems embedded across business processes with minimal to no human intervention. But what exactly is an AI agent?

AI agents are goal-oriented software systems that operate autonomously, learn and adapt over time, and use tools to take real action within enterprise environments. They represent a transformative leap in AI, evolving beyond traditional automation and generative AI to unlock new levels of intelligence, efficiency, and growth.

What Makes Up an AI Agent?

AI agents are modular and made from different components that form their “DNA” to take actions reliably:

• Instructions: Outline the steps needed to accomplish the desired goal
• Guardrails: Establish safety controls and ethical boundaries that limit what an AI agent can say or do
• Models: Power cognitive functions through a reasoning engine
• Tools: Enable agents to take actions and gather information by connecting to various data sources, systems, and APIs.
• Memory: Enable contextual awareness and data persistence with the ability to recall history

What Are the Types of AI Agents?

The potential for designing and building AI agents is virtually limitless. There are many possibilities for their structure, functionality, and applications. Different types of agents can address different needs across the organization. However, broadly speaking, an AI agent typically falls into one of five types:

• Simple reflex agents: These act purely based on preprogrammed rules, often working under an “if-then-else” framework. They do not retain past experiences or learn over time. Example: If an order is placed in the inventory system, then the AI agent automatically updates inventory levels in the CRM.
• Model-based reflex agents: These use an internal model or storage of information to handle more complex tasks. This means they can react in slightly changing environments with relatively limited functionality.Example: A customer support chatbot that follows scripted responses but can adapt based on the conversation.
• Goal-based agents: These make decisions autonomously by evaluating different possible actions to reach a specific goal. They are actively able to understand and interpret the environment around them. Example: A retail company builds a goal-based agent to automatically prioritize and route customer orders to the nearest warehouse to meet delivery deadlines while minimizing shipping costs.
• Utility-based agents: These evaluate multiple potential outcomes and select actions that provide the best possible result. This type of agent is typically used when trade-offs between different actions need to be considered.Example: An ecommerce company develops a utility-based AI agent for dynamic pricing optimization. This agent evaluates multiple factors (for example, demand, inventory levels, and competitor prices) and determines the ideal products to discount to boost sales when inventory is high.
• Learning agents: These continuously learn from experience to improve performance. They automatically add each new interaction to their learning base and use machine learning to adapt to changing environments. Example: A manufacturing company uses a learning agent to analyze historical sales and market data to improve inventory forecasting. The agent adapts its predictions over time to optimize stock levels and reduce inefficiencies in supply chains.

How Do AI Agents Work?

After understanding the different types of AI agents, it is also essential to know how AI agents generally work. While agents can use an organization’s own data and information to carry out specific tasks and processes, many carry out the following steps to do their jobs:

• Prompt processing: The agent receives a prompt and uses natural language processing (NLP) to understand the request and determine the context.
• Data retrieval: The agent retrieves relevant data (internal and/or external) to determine how it aligns with the goal or request. Data retrieval is a pivotal guiding step for AI agents: if the data is incorrect, outdated, or unqualified, agents will produce inaccurate or lackluster responses.
• Action execution: Once the agent has processed the input and necessary data, it executes the appropriate action autonomously.
• Learning and adaptation: After execution, the agent collects feedback to evaluate the effectiveness of its decisions. It learns from past experiences to improve future decision-making, and as it gathers more data, it becomes more efficient and accurate.

What Are Some Best Practices for Using AI Agents?

AI agents work closely with company data to provide business value. That’s why governance, security, and privacy are critical considerations for companies looking to invest in AI agent solutions. Best practices include:

• Steward responsible AI: Transparency, human oversight, customer opt-out options, data privacy and security, fairness, and environmental sustainability are intrinsic to responsible AI development and implementation. This includes ensuring your AI agents cannot access sensitive information and cannot store data that has been processed inappropriately. Responsible AI provides safety, fairness, and accountability, essential for building trust with your teams and customers regarding your AI solutions.
• Control data management: It’s essential to control the data used to ground AI agents, ensuring that they use the correct and appropriate information. The success of an organization’s AI solutions often depends on how well it can collect, store, and use data.
• Include human guidance: While autonomy is a fundamental characteristic of AI agents, keeping people in the loop to monitor performance and solve issues when they arise is essential. This fosters accountability and empowers organizations to control the business value their AI delivers to customers.
• Implement an AI management platform: Give your organization flexibility by using an AI management platform to govern hundreds or even thousands of deployed agents. This will help manage your data, create automation around agents, and effectively manage and control them all.

How Can AI Agents Drive Business Value?

AI agents can help drive business value for an organization across numerous functions. Here are some examples of AI agents and the benefits they bring to the table:

• Supplier Risk Agent: Monitor suppliers, risk feeds, and sentiment across your production operations and mitigate risks.
• Employee Lifecycle Management Agent: Coordinate the full lifecycle of an employee onboarding and offboarding, saving time on manual tasks and processes.
• Forecasting Agent: Identify anomalies in sales forecasts, adjust models according to real-time data and updates, and trigger reviews to keep humans in the loop.
• Invoice Reconciliation Agent: Ensure supply continuity and financial accuracy by automating three-way match and safeguarding supplier relationships.
• Claims Processing Agent: Improve data quality, prevent downstream process failures, and reduce risk by automating the detection of incomplete, invalid, or anomalous data across business transactions.

AI agents are more than just hype. They’re transforming the way organizations operate. Leveraging the power of AI agents enables teams to streamline processes, unlock hyperproductivity, and make smarter decisions.

Explore Boomi AI to set yourself up for success in the future of work.