What are AI Agents?

What are AI Agents?

AI agents are AI-based applications that make decisions and perform tasks independently with minimum human oversight. Powered by advanced models, agents can decide on an action plan and use multiple software tools to execute that plan. Their ability to reason, plan, and take action allows agents to handle a wide range of situations that are impractical or impossible to automate with pre-configured rules and logic.

This technology is transforming many modern conveniences, from simple virtual assistants that provide users with ready answers to autonomous vehicles navigating in traffic. With recent innovations in generative AI, today's agents are taking on much more challenging and dynamic roles with greater expertise. Furthermore, multiple AI agents can work together and provide coordination with many users.

All agents operate on a flexible scale. Rule-based AI agents with no or limited memory represent the most rigid forms, performing tasks based on pre-determined conditions. The most autonomous AI agents can handle irregular, multi-stage problems and find effective solutions. They can also correct errors on their own and adapt to new information.

These advanced capabilities allow AI agents to automate complex business functions, expanding their potential use cases. Through multi-agent systems, teams of AI agents collaborate across different departments and organizations. Companies can also create their own agents to realize their unique business processes and goals.

How Do AI Agents Work?

Although their complexities vary, intelligent agents are created by following four basic design patterns, which allow them to adapt to different scenarios. Let's break down these central agentic capabilities and follow how an advanced agent uses them to resolve a complex purchase order.

1. Designing a Plan

To determine the steps required to complete assigned tasks, AI agents use highly advanced and large-scale AI models called "frontier models." This allows agents to adjust their routes and create new workflows instead of strictly following pre-defined paths.

Example: The user asks the AI agent to select a third-party supplier that best matches company priorities such as cost-effectiveness. In response, the agent creates a custom agentic workflow to find the best supplier. Steps include researching company selection criteria, identifying qualified suppliers, and requesting and evaluating proposals to make a recommendation.

2. Using Software Tools

AI agents combine different tools to realize their plans. Common tools allow agents to collect and analyze data, perform calculations, and create and run new code. Application programming interfaces (APIs) simplify communication with other software, so agents can perform tasks within business systems. Large Language Models (LLMs), a type of generative AI that interprets and generates computer code and natural language text, also allow agents to communicate by chatting with users. This intuitive interaction helps users easily review the work of the agents.

Example: The AI agent uses document and web search tools to scan supplier information scattered across company emails, PDF files, databases, and websites. Coding and calculator tools help the agent compare different supplier proposals and payment terms and make a selection. Within minutes, the agent generates a detailed written report recommending a supplier.

3. Reflecting on Performance (Reflection)

Using LLMs as reasoning engines, AI agents improve their performance by repeatedly evaluating and correcting their own outputs. Multi-agent systems evaluate their performance through feedback mechanisms. Large memory capacities allow agents to store data from past scenarios, creating a rich knowledge base for handling new obstacles. This reflection process allows agents to troubleshoot emerging issues and identify patterns for future predictions, all without extra programming.

Example: By self-evaluating results, the AI agent improves the quality and accuracy of purchase selection. The agent can also include more decision factors such as environmental sustainability in the process.

4. Collaborating with Team Members and Other Agents

Instead of a single agent doing everything, a network of agents specialized for specific roles can work together in multi-agent systems. This agentic collaboration allows the agent team to solve complex problems more effectively. AI agents can also coordinate with different users when necessary, asking for information or approval before continuing with processing.

Example: Before sending an order, the agent asks the user to review the workflow and approve the final selection. To manage more complex orders, the purchasing agent can be replaced with multiple specialized agents such as a purchasing clerk agent or a contract manager agent. This multi-agent format helps automate more complex workflows, especially when embedded in the company's unified data systems and applications.

What are the Benefits of AI Agents?

Equipped with thoughtful reasoning and learning capabilities, autonomous AI agents offer many advantages as companies grow:

• Increases Productivity: Takes over the constant decisions required for complex tasks without intensive human intervention, allowing teams to focus on innovation.
• Improves Accuracy: Can review its own outputs, notice information gaps, and correct errors.
• Expands Accessibility: Continues to manage processes and customer questions independently in the background outside of business hours.
• Cost Savings: Significantly reduces operational expenses created by manual processes and errors.
• Breaks Down Silos: Interconnected agent network simplifies cross-departmental data collection and workflows.
• Scalability: Easily adapts to increasing task volumes, increasing operational agility.

What Types of AI Agents are There?

AI agents come in different types, from simple to complex:

• Reactive Agents: Follow classic rule-based systems. Suitable for repetitive and short-term scenarios.
• Proactive Agents: Identify problems before they occur and suggest solutions using predictive algorithms.
• Hybrid Agents: Combine the speed of reactive systems with the depth of proactive systems.
• Utility-Based Agents: Focus on choosing the action plan that will provide the highest user satisfaction. Common in navigation and financial trading.
• Learning Agents: Learn from past experiences and feedback, increasing accuracy over time.
• Collaborative Agents: Networks that coordinate to complete complex cross-departmental tasks by breaking down corporate silos.

How Do You Use AI Agents?

Agents serve as both role-specific assistants and general solution partners that can be applied to multiple lines of business:

The Best Way to Implement AI Agents in the Workplace

• Ethical Principles: Follow transparency, privacy, and "human-in-the-loop" design process.
• Human Oversight: Expert individuals should have final authority over the decision-making process.
• Data Readiness: Agents work on the basis of quality business data; invest in data governance.
• Collaborative Mindset: Carefully determine the areas where agent automation will free up teams.

The Difference Between AI Agents and Copilots

In conclusion, AI agents are technologies that move beyond just responding and take action, taking the operational agility and efficiency of organizations to a new dimension.