Press Alt/Option + S to skip to the main content. AI agents are autonomous software applications that perform specialized roles. They can plan steps, use tools, self-correct errors, reason through issues, and collaborate with other entities with minimal human oversight. AI agents, or intelligent agents, are artificial intelligence-based software applications that can make decisions and perform tasks autonomously. Thanks to their diverse and dynamic capabilities, AI agents power many of today’s modern amenities—from simple virtual assistants answering common questions with stock responses, to self-driving vehicles using intricate reasoning paradigms and video tracking to navigate through traffic. If AI overall describes computing technology that can learn, reason, and solve problems, AI agents are built to channel that nuanced capacity within specific executive roles. In practice, AI agents focus these AI-driven models and tools to work towards targeted goals without requiring constant human input and supervision. All AI agents act on a sliding scale of flexibility. Rule-based AI agents with no or limited memory represent the most rigid forms, performing tasks based on preset conditions. The most autonomous AI agents tackle more complex functions. They can independently choose a course of action, design a plan, gather relevant data, and employ multiple software tools to complete each step. By learning from evolving information, AI agents improve over time—analyzing feedback, self-correcting errors, and solving emerging problems. Multiple AI agents can also work together, even coordinating with humans to achieve different tasks. These advanced abilities allow AI agents to work at deeper levels of specialization, making the potential business use cases expansive. Through multi-agent systems, individual AI agents collaborate across different departments and organizations. Companies can also build their own agents, integrating custom goals, instructions, and access to internal software and databases. While ranging in complexity, AI agents share similar features in how they function. Let’s break down how AI agents work and follow how one advanced agent can tackle a complex procurement order. AI agents can use natural human expression, allowing users to communicate conversationally with them through interfaces. To achieve this organic exchange, agents employ large language models (LLMs), a type of generative AI that can interpret and produce human-like speech and text. Example:  Through a chatbot, the user writes instructions for an autonomous AI agent to choose a third-party supplier that best matches company priorities like cost effectiveness. AI agents begin by designing plans to complete assigned tasks. Instead of strictly following predefined steps in a specific order, AI agents can adapt and adjust, using their own internal decision-making process to create new workflows. Example: Based on user instructions, the AI agent builds a custom agentic workflow that finds the best supplier, assembling the necessary data and software tools to execute each stage. AI agents combine different tools to complete multilayered projects. Different applications allow agents to collect and analyze data, create and run new code, communicate using speech or text, coordinate between different software components, or manage automatic functions. Example: The AI agent uses document and web search tools to scan supplier information scattered through company e-mails, PDF files, databases, and websites. Coding and calculator tools help compare and choose between different supplier quotations and payment terms. Within minutes, the agent recommends a third-party supplier, using LLM tools to generate a detailed written report. AI agents can reflect on their performance by repeatedly self-evaluating and correcting their output. In multi-agent systems, agents can assess each other’s performance through feedback mechanisms. Their substantial memory also allows agents to store data from past scenarios, building a rich knowledge base to tackle new obstacles. This reflection process allows agents to troubleshoot problems as they arise and identify patterns for future predictions—all without specific programming. Example: By self-assessing the results, the AI agent improves its procurement selection quality and accuracy. The agent can also incorporate additional decision factors, like a supplier’s sustainability credentials. Instead of a single do-it-all agent, a network of agents specialized for specific roles can work together in multi-agent systems. This agentic collaboration provides higher levels of performance for complex projects. AI agents can also coordinate with different team members where needed, from requesting additional information to asking for confirmation before proceeding with sensitive tasks. Example: Before submitting an order, the agent prompts team members to review the agentic workflow and approve the final selection. For more complicated orders, the procurement AI agent can split into separate dedicated roles, such as a purchasing clerk agent or contract manager agent. This multi-agent format allows the system to cover more nuanced details, from guideline compliance to contract negotiations. Equipped with nuanced reasoning and learning capabilities, autonomous AI agents offer deeper levels of specialization when compared to other standard solutions. This increased functionality offers many benefits for companies as they grow and expand. When integrated into business workflows, AI agents can: Increase productivity: agent AI tools save teams time by taking over the constant decisions needed for complex tasks without heavy human intervention, boosting overall efficiency. Improve accuracy: AI agents can self-examine their output, spotting information gaps and correcting errors. This allows agents to maintain high accuracy levels while accelerating multiple processes. Expand availability: AI agents can work behind the scenes, from completing tasks for ongoing projects to troubleshooting customer questions beyond the usual office hours. Liberate team responsibilities:  through adaptable agentic workflows, AI agents free teams from heavy operational workloads, so they can instead focus on big-picture investments and innovation. Save on costs: AI agent automation can reduce operational expenses dramatically by removing the costly inefficiencies and errors of manual processes. Break down silos: a network of interconnected collaborative agents can reduce common obstacles that emerge from complex projects by streamlining data collection and workflows across different departments. Create specialized applications:  organizations can create teams of bespoke agents to perform functions unique to their needs, training agents on internal data to generate precise customized results. Scale to changing needs:  AI agents can easily adapt to increasing volumes of tasks, letting companies expand their operations without sacrificing performance accuracy and quality. Identify trends: through data analysis, AI agents can identify patterns within complex data sets and suggest potential insights into future outcomes, empowering companies in their decision-making process. AI agents come in different forms. By combining them, organizations can create customized multi-agent systems to suit their specific needs. Here are six types of AI agents and how they work best for different scenarios: Reactive AI agents follow classic rule-based systems. Also called reflex agents, they can understand and autonomously act to prompts following preset rules. This approach works best for repetitive tasks. For example, a reactive AI agent can use a chatbot to process common requests like resetting a password from conversational keywords or phrases. Due to their lack of memory, they can only respond to limited, short-term scenarios. On the plus side, reactive AI agents prove low maintenance, needing minimal programming to function. Far more nimble than reactive agents, proactive AI agents create predictive algorithms to drive more nuanced functions. These models identify patterns, forecast probable outcomes, and choose the best course of action without human prompting. These agents can monitor complex systems like supply chains, proactively identifying issues and recommending solutions. Like their name suggests, hybrid systems combine the efficiency of reactive agentic AI with the nuanced discernment of proactive AI agents. The combination offers the best of both worlds. When a quick response is needed, they can react efficiently to predictable scenarios following preset rules but can also adjust when conditions change. Utility-based AI agents focus on finding the best possible sequence to achieve a desired outcome. They grade each potential action based on user satisfaction metrics, then select the option with the highest marks. Utility-based agents are the driving force behind car navigation systems, robotics, and financial trading. Learning AI agents can refine their performance based on previous experiences. They use problem generators that create test scenarios to try new strategies, collect data, and evaluate the results. Learning AI agents also track user feedback and behavior to hone the best approach, improving overall nuance and accuracy over time. Current learning AI agents help create sophisticated virtual assistants that adapt to users’ needs. Collaborative AI agents describe a network of agentic AI systems coordinating together to complete complex tasks, able to operate across departmental siloes. They can build custom workflows and delegate tasks to other entities, even people and other AI agents. AI agents can be used across diverse industries thanks to their adaptability to a vast range of roles. Within an individual company, specialized agents from different departments can even coordinate tasks, simplifying interdepartmental projects. Agents can be integrated into interactive applications and conversational user experiences, or work in the background—streamlining scheduled processes or reacting to events. Though their potential applications are boundless, here are the most common business use cases of AI agents today: The potential applications of autonomous AI agents are broad in scope. To achieve its full promise, however, the technology works best with thoughtful integration and coordination. Consider these best practices before incorporating agent AI systems. Emphasize human oversight Expert individuals should still maintain final authority over the agent AI decision-making process. Users should establish the agents’ level of autonomy and require final approval before agents complete sensitive tasks. Experts can also review internal workflow charts through agentic interfaces to prevent potential ethical pitfalls and biased data interpretations. Provide internal data for training The full potential of AI agents depends entirely on access to information. Their value to a company improves the more they train on internal data. Connect agents to relevant data sources, so that they can tailor their automation and analysis to the organization’s specific needs. Foster a collaborative mindset AI agents only work if team members know how to use agentic autonomy effectively. Teams should carefully consider where AI agent automation can relieve operational obstacles to ease work responsibilities. Support ongoing training As AI agent technology evolves, organizations should prioritize continuous training. Regular educational sessions can help teams stay updated on the latest innovations, applications, and best practices. Measure and evaluate Organizations should regularly evaluate an AI agent’s efficiency and productivity within the company’s overall workflow. This review process should include monitoring feedback from both employees and customers. Regular evaluations can give insights into possible areas for improvement and optimization. At first glance, AI agents seem to overlap with another AI-powered technology—AI copilots. Using LLMs and other generative AI tools, both systems can naturally respond to users’ requests and simplify complicated projects. Copilots, however, are more limited in comparison to the advanced capabilities of autonomous AI: Levels of independence AI copilots are designed to work alongside users, providing on-demand assistance when requested. Some copilots may even respond proactively. In contrast, AI agents can operate as stand-alone entities, silently running in the background of applications without user input. Adaptability AI copilots can complete preconfigured tasks, even complex ones, but are ultimately limited to established workflows. AI agents, however, can build their own workflows, adapting to a broader range of functions and scenarios. Coordination Working in harmony, AI copilots can function more as interfaces for AI agents. Users can submit requests through copilots, which then prompt agents into action. From there,  AI agents take the lead, performing each step to reach the users’ targeted goals. Specialization Both AI agents and copilots can be customized for specific fields and functions, from coding to financial analysis. AI agents, however, go deeper. Agents build expert knowledge bases over time to manage more complex roles within their specialty. Both AI tools offer significant value when combined as part of multi-agent systems. Teams can choose between a helpful hand or hands-off automation. See how multi-agent AI systems in Joule can streamline your business workflow. Explore more What does an AI agent do? AI agents can automate specialized tasks, make decisions, and improve performance over time without human intervention. What are the six types of AI agents? The six common types of AI agents are reactive, proactive, hybrid, utility-based, learning, and collaborative. What are multi-agent systems? Multi-agent systems are networks of specialized AI agents that work together to achieve common goals. These systems break down a complex task into subtasks that are assigned to different agents designed for that role. How do I create my own AI agent? Build your own network of AI agents specialized to your organization’s unique needs with SAP Build. /content/sapdx/countries/en_us/fragments/insights/article-details Automate complex tasks and enhance productivity with collaborative AI agents in Joule. Learn more /content/sapdx/countries/en_us/fragments/insights/article-read-more