7 Key Differences: AI Agents vs. RPA — Which Is Best for Full-Stack Automation?

Meta Description: In this comprehensive guide, we explore AI Agents vs. RPA: Which Is Best for Full-Stack Automation? We compare their strengths, weaknesses, and use cases to help you choose the right automation technology for your enterprise.

Outline

• Introduction to Full-Stack Automation: Explain the concept of full-stack automation and why enterprises are comparing AI agents vs. RPA.
• What Are AI Agents? Define AI agents, how they use artificial intelligence (ML/LLMs) to perform tasks, and key features of agentic automation.
• What Is RPA (Robotic Process Automation)? Define RPA as software robots automating repeatable tasks, and discuss its core characteristics.
• Key Differences Between AI Agents and RPA: Contrast their approaches – AI agents are adaptive and cognitive, RPA is rule-based and deterministic.
• Benefits of RPA: Outline RPA advantages like reliability, accuracy, speed, and cost-effectiveness.
• Benefits of AI Agents: List AI agent advantages such as flexibility, decision-making, handling unstructured data, and learning capabilities.
• Use Cases for RPA: Present common RPA use cases (data entry, form processing, back-office tasks) and industries (banking, finance, healthcare).
• Use Cases for AI Agents: Present AI agent scenarios (customer support bots, predictive analytics, complex decision tasks, code generation) and industries (support, IT, marketing).
• RPA in Full-Stack Automation: Explain how RPA fits into full-stack strategies – connecting systems, automating workflows, and ensuring consistency.
• AI Agents in Full-Stack Automation: Explain the role of AI agents in end-to-end automation – planning, adapting, and orchestrating multi-step processes.
• Combining RPA and AI (Hyperautomation): Discuss the synergy of RPA + AI (called Intelligent Automation or Hyperautomation), enabling broader automation.
• Limitations and Challenges: Cover RPA’s limitations (rigidity, maintenance) and AI agents’ challenges (complexity, unpredictability, data issues).
• Choosing the Best Solution: Advise how to decide which is best: assess task complexity, data structure, cost, and desired outcomes; often a hybrid approach works.
• Future of Full-Stack Automation: Explore trends like the rise of AI agents (a16z’s vision), ongoing RPA relevance, and how both will evolve in enterprise automation.
• Frequently Asked Questions (FAQs): Provide answers to common questions about AI agents vs RPA and full-stack automation.
• Conclusion: Summarize key points and reaffirm how enterprises can leverage the right tools for full-stack automation.

Introduction to Full-Stack Automation

Full-stack automation means automating processes across all layers of an organization’s IT stack, from user interfaces to back-end systems. In other words, it’s about using technology to handle entire workflows end-to-end, rather than just individual tasks. In this context, two major technologies are often compared: AI Agents and Robotic Process Automation (RPA). This article will explore the key differences in the ai vs RPA debate, highlighting how artificial intelligence and other automation technologies compare in terms of capabilities, strategic value, and use cases. AI agents leverage artificial intelligence to make decisions and adapt, while RPA uses software “robots” to follow predefined rules. Both aim to boost efficiency, but they work differently. As companies seek smarter automation, asking “AI Agents vs. RPA: Which Is Best for Full-Stack Automation?” is crucial for strategy. This article breaks down their roles, strengths, and how they fit into full-stack automation.

Automation Technologies and Capabilities

The landscape of automation technologies has rapidly advanced, moving beyond traditional rule-based tools to embrace intelligent automation powered by artificial intelligence (AI) and machine learning (ML). AI agents are at the forefront of this evolution, offering advanced automation capabilities that enable organizations to analyze data, identify patterns, and make informed decisions with minimal human intervention. Technologies like natural language processing (NLP) and computer vision further expand the possibilities, allowing businesses to automate tasks that involve understanding text, speech, or images—tasks that were previously too complex for basic automation. By deploying AI agents and leveraging these cutting-edge automation technologies, organizations can automate repetitive tasks, streamline operations, and unlock significant cost savings. The ability to analyze data and automate processes at scale is transforming business operations, making intelligent automation a key driver of competitive advantage.

What Are AI Agents?

AI agents are autonomous digital systems that perform tasks by using advanced AI techniques. They often rely on large language models (LLMs), machine learning, and cognitive capabilities to plan, learn, and adapt. These agents frequently use machine learning models to analyze data, recognize patterns, and improve their performance over time. For example, an AI agent might parse emails, extract key details, and then decide the next action on its own. IBM defines an AI agent as “a system that autonomously performs tasks by designing workflows with available tools”. Unlike a simple chatbot, AI agents can connect multiple steps: they sense the environment, make decisions, and use other tools or services as needed. AI agents can handle unstructured data (like text, images, and speech) and learn from interactions. They are often used for tasks that require flexibility and reasoning, such as answering customer questions in natural language or generating code from instructions.

(Image: AI agent concept – an AI-powered robot planning tasks)AI agents (as shown above) combine AI and data to plan and execute tasks. They can adapt to new situations, unlike fixed-rule bots.

What Is RPA (Robotic Process Automation) for Repetitive Tasks?

Robotic Process Automation, or RPA, uses software “robots” to automate repetitive, rule-based tasks. RPA automates tasks that are repetitive and rules-based, making it ideal for streamlining business operations. Think of RPA bots as virtual assistants that mimic how a human clicks buttons or fills forms on a screen. They work best with structured inputs and well-defined processes. For example, an RPA bot can log into a web application, copy data from one form to another, and save the results – all without human help.

RPA is particularly effective at automating structured workflows, such as invoice processing and employee onboarding. It specializes in automating structured, repetitive workflows to optimize processes and improve operational efficiency. Crucially, RPA bots are deterministic: they follow the same steps every time and don’t learn or improvise. As the NICE guide explains, RPA robots perform tasks the same way on each repetition and won’t come up with new approaches.

RPA relies on defined rules and rule based automation to execute tasks consistently. This makes RPA very reliable and fast for clear, repetitive tasks. Typical examples include processing invoices, filling out compliance forms, or moving data between legacy systems. RPA is well-suited for rules based tasks and structured processes, such as data migration and transaction processing in finance and HR. By freeing employees from mundane work, RPA can increase accuracy and efficiency.

Key Differences Between AI Agents and RPA

The core distinction is adaptability vs. rule-based precision. AI Agents are adaptive and learn from data, whereas RPA is predicable and follows preset rules. In practice, traditional robotic process automation is limited to handling only rigid, rule-based tasks, making it less suitable for dynamic or complex workflows compared to the adaptability of AI agents:

• Task Complexity: AI agents excel at complex, cognitive tasks requiring interpretation or decision-making. RPA excels at simple, repetitive tasks. As one expert summary notes, “AI agents are best for complex, cognitive tasks requiring adaptability, whereas RPA excels in automating straightforward, repetitive processes”.
• Data Handling: RPA works with structured, orderly data (e.g., spreadsheet fields). AI agents can handle unstructured data (like text, images, or voice) and can infer meaning.
• Learning and Adaptation: AI agents can learn from outcomes and adapt their behavior; RPA bots do not learn and will break if the process changes.
• Deployment Speed and Cost: RPA can often be deployed faster and cheaper for simple tasks. AI agents may require more setup and computational resources but can adapt to new tasks.
• Error Handling: RPA yields consistent outputs with low error when the rules are followed. AI agents may make unpredictable mistakes if they encounter unusual cases.

A useful analogy: RPA is like a calculator doing the same math correctly every time, while an AI agent is like a smart assistant learning from experience to handle new problems. Both aim to automate, but one is rule-bound and the other is reasoning-driven.

Benefits of RPA

RPA’s strengths make it a proven automation tool in many industries. Key benefits include:

• Reliability and Accuracy: RPA bots follow rules precisely, which means fewer errors than humans might make in repetitive tasks. They create audit trails, important in regulated fields.
• Speed and Efficiency: Bots work 24/7 at high speed. They can complete tasks 4–5 times faster than a person, significantly improving throughput.
• Cost-Effectiveness: Implementing an RPA solution can be less expensive upfront than a full AI project. Many tools provide low-code interfaces, so businesses can automate tasks quickly.
• Consistency and Compliance: RPA always applies the same logic and keeps records. This ensures compliance with rules (e.g., in banking and insurance) and reduces the risk of human error.
• Employee Satisfaction: By automating mundane work, employees are free to tackle more meaningful tasks, which can improve morale.
• Mature Ecosystem: RPA technology is well-established (20+ years) and supported by major vendors (UiPath, Automation Anywhere, Blue Prism). Many organizations already have RPA experience or trained staff.

In summary, RPA is a dependable workhorse: it’s ideal for automating well-defined processes with clear rules. Its core strength lies in task automation, enabling organizations to improve efficiency, reduce manual effort, and maintain continuous operation in structured workflows.

Benefits of AI Agents

AI agents bring new capabilities that RPA lacks. Notable advantages include:

• Flexibility and Adaptation: AI agents can adapt to changes in the environment. They use machine learning to improve over time. For example, an AI agent might handle a variety of email formats and still understand what action to take.
• Cognitive Abilities: Agents can interpret natural language, recognize images, or make predictions. This lets them handle complex tasks like answering customer queries, fraud detection, or generating reports.
• Decision-Making: Instead of following fixed scripts, AI agents can make decisions on the fly. This is useful when business rules are ambiguous or data is incomplete. AI agents can also generate data-driven insights by analyzing complex market trends and forecasting demand, supporting strategic planning and proactive, data-backed business decisions.
• Handling Unstructured Data: AI agents shine when data is messy. They can read documents, transcribe speech, or analyze social media text, tasks beyond traditional RPA.
• Automation of Entire Goals: With agent frameworks, you can give a goal (e.g., “book a flight within budget”) and the agent will plan steps using tools to achieve it.
• Innovation and Evolution: AI agents often incorporate generative AI (like GPT) to create content or suggestions. As large language models advance, agent capabilities rapidly improve.

Because of these capabilities, AI agents can turn knowledge work into automation. For instance, startups use AI agents for automated customer support that can adapt to new types of queries. In short, AI agents are game-changers for tasks that require “thinking” and learning, not just rote execution.

Use Cases for RPA

RPA is widely used in businesses to handle routine processes by automating tasks that were previously manual processes. This shift from manual processes to automation helps organizations reduce errors, increase efficiency, and lower costs. Common RPA scenarios include:

• Invoice and Data Processing: Automatically reading invoice data and entering it into accounting systems, eliminating manual processes.
• Customer Service Back-Office: After a support agent handles a call, an RPA bot might update records, send follow-up emails, or log ticket details, automating routine tasks.
• Financial Transactions: Reconciling accounts, processing loan applications, or generating statements—ideal examples of automating tasks that are repetitive and high-volume.
• HR and Payroll: Onboarding new employees by filling forms across systems, or calculating payroll data consistently, replacing manual processes with automation.
• IT and Help Desk: Resetting passwords, provisioning accounts, or scheduling routine updates via scripts, all of which are routine tasks suited for RPA.
• Compliance and Reporting: Generating audit reports, checking regulatory rules, or flagging exceptions, automating tasks that would otherwise require significant manual effort.

Industries relying on RPA include banking, insurance, healthcare, and manufacturing. For example, a bank may use RPA bots to process mortgage applications faster, while an insurance firm automates claims entry. In each case, the tasks are repetitive and rule-driven. RPA’s predictability is especially valued in sectors “where there is a big downside for an error”. In essence, RPA fits any process that involves lots of structured data and clear steps, making it ideal for automating routine tasks and moving away from manual processes.

Use Cases for AI Agents

AI agents excel in scenarios where adaptability and cognition are needed. Examples include:

• Customer Interaction Automation: Advanced chatbots or virtual agents that understand queries, retrieve information, and even upsell services. For instance, an AI agent might handle a customer’s travel booking by asking clarifying questions and using APIs to reserve a flight.
• Predictive Analytics and Fraud Detection: AI agents can sift through data to predict trends or identify anomalies. E.g., detecting fraudulent transactions by learning patterns and making decisions with minimal oversight. AI agents can also analyze sensor data for real-time monitoring, predictive maintenance, and anomaly detection, helping to identify potential issues and ensure operational efficiency.
• Complex IT Automation: Agents that manage cloud infrastructure or software deployments autonomously. For example, an AI agent could monitor server performance, detect issues, and take corrective actions without manual rules.
• Content and Code Generation: Using generative AI (like GPT), agents can draft emails, write reports, or even generate code. This goes beyond RPA; an agent could update a website or write a script to solve a problem.
• Personal Assistants: Scheduling meetings, summarizing meetings, or managing to-do lists by understanding human preferences and external data.
• Multi-Step Workflows: AI agents can break goals into subtasks. For example, “research competitor marketing strategies” might involve searching websites, summarizing content, and preparing a report, all autonomously.

Companies are creating agents for specialized tasks. For example, a16z mentions agents in production for customer support and document analysis. AI agents often leverage intelligent document processing to extract and interpret data from unstructured or semi-structured documents, such as invoices or contracts, automating complex, document-heavy tasks by integrating technologies like OCR, NLP, and machine learning. In practice, AI agents are used in technology, retail, marketing, and research sectors. Essentially, any use case requiring “understanding, reasoning, or adaptation” is a candidate for AI agents.

RPA in Full-Stack Automation

In a full-stack automation strategy, RPA acts as the glue that connects disparate applications and automates routine steps. RPA tools are designed to integrate with existing systems via APIs, connectors, and user interface interactions, allowing organizations to automate processes without extensive system overhauls. Because RPA tools can interact with user interfaces, databases, and APIs, they help bridge gaps between systems. For example, RPA bots can integrate legacy software (without modern APIs) into an end-to-end workflow. The Thoughtful.ai blog describes how RPA “enables seamless interaction between interconnected systems” and ensures repeatable tasks are done accurately. In full-stack terms, RPA handles the well-defined parts of a process across multiple layers – from desktop apps to ERP systems – freeing human operators from repetitive bridging tasks. Business process management tools can further facilitate a unified data view and streamline data integration in RPA implementations, enhancing orchestration and data flow between applications. Thus, RPA is a foundational element for achieving full-stack automation, handling the parts of a workflow that are stable and structured.

AI Agents in Full-Stack Automation

AI agents complement RPA by handling the dynamic and decision-based aspects of full-stack automation. They bring intelligence to the stack, capable of orchestrating multiple steps without rigid scripts. For instance, an AI agent could initiate a process, make judgment calls, and call on RPA bots as needed. As the Andreessen Horowitz (a16z) analysis suggests, AI agents can fulfill the “original promise of RPA” by taking a goal and autonomously executing it end-to-end. In practice, an AI agent might plan the sequence of actions and use RPA bots for the structured parts. For example, to onboard a new client, an AI agent could evaluate documents (using OCR and NLP), then instruct an RPA bot to enter data into CRM, and finally follow up with personalized email correspondence.

By combining AI agents and RPA in this layered approach, organizations can create comprehensive automation solutions tailored to specific industry needs, integrating various processes and supporting vertical integration within industry value chains.

The CIO article notes that AI agents are used for “more flexible tasks such as customer interactions, fraud detection, and predictive analytics”. In a full-stack model, agents handle unstructured inputs and decision logic, while delegating predictable tasks to RPA. Over time, AI agents can adapt to changing business logic and refine their actions, effectively acting as the brains of full-stack automation. This layered approach – RPA for rules, AI agents for reasoning – maximizes coverage of the entire workflow, from front-end applications to back-end systems.

Combining RPA and AI (Hyperautomation)

Rather than choosing one or the other, many organizations use RPA and AI agents together in a strategy often called Intelligent Automation or Hyperautomation. Selecting the right automation solution based on specific business needs, capabilities, and long-term goals is crucial for maximizing the benefits of hyperautomation. This means applying multiple technologies (RPA, AI, ML, BPM) to automate processes end-to-end. In this model, RPA bots handle the repetitive steps, while AI fills gaps for complex tasks. For example, the NICE guide explains that “combining both RPA and artificial intelligence can create a fully autonomous intelligent process automation,” enabling true end-to-end automation.

Key advantages of this combination include:

• End-to-End Coverage: RPA handles structured parts; AI handles unstructured and decision parts, covering whole workflows.
• Increased Efficiency: Workflows become more efficient as fewer tasks need human review.
• Scalability: Organizations can scale up automation faster, using RPA for scale and AI for new capabilities. However, the scalability and adoption of hyperautomation depend heavily on an organization's technical capabilities, as these influence how effectively new automation solutions can be implemented and managed.
• Enhanced Capabilities: AI-driven insights can improve the processes that RPA automates, such as using predictive analytics to trigger RPA tasks at optimal times.

A real-world scenario: Opening a new bank account online can be fully automated by combining AI and RPA. The AI agent (chatbot) collects customer details, then an RPA bot fills in forms, and AI tools perform document checks. According to NICE, this kind of intelligent end-to-end automation leads to happier customers and cost savings.

In short, AI agents and RPA are converging. As one analysis puts it, they now work “hand in hand to achieve new levels of automation,” giving rise to more powerful solutions.

Cost Savings and ROI

One of the most compelling reasons organizations invest in automation technologies is the potential for substantial cost savings and a strong return on investment (ROI). By automating repetitive tasks and optimizing workflows, businesses can significantly reduce labor costs, minimize errors, and boost overall productivity. Industry research consistently shows that companies implementing automation technologies often achieve significant cost savings—sometimes reducing operational expenses by up to 50%.

Additionally, the ROI on automation projects can be impressive, with many organizations recouping their investment in less than a year. To maximize these benefits, it’s essential for businesses to strategically assess their processes, identify the most impactful areas for automation, and select the right technologies to support their goals. This targeted approach ensures that automation delivers measurable value and supports long-term business growth.

Automation Success Factors

Achieving success with automation initiatives requires more than just deploying the latest technologies—it demands a comprehensive automation strategy and the right mix of technical and human capabilities. Key factors include selecting automation solutions that align with business objectives, ensuring access to high-quality training data, and building a team with expertise in data science and IT. Organizations must also foster collaboration between technical experts and business stakeholders to design automation solutions that deliver real-world value. By focusing on these success factors, businesses can streamline operations, improve outcomes (such as patient outcomes in healthcare), and realize significant cost savings through AI-powered automation. A well-executed automation strategy not only enhances efficiency but also positions organizations to adapt and thrive in an increasingly digital world.

Limitations and Considerations

Each technology has trade-offs and challenges:

• RPA Limitations: RPA bots are brittle when processes change. If an application interface updates or rules vary, bots may break. Implementation often requires detailed mapping of workflows, and maintenance can be laborious. RPA also struggles with unstructured data (like handwriting or free text). It can handle predictable paths but not deviations. These limitations are characteristic of traditional automation, which relies on fixed rules and rigid procedures, making it less adaptable to change and unable to learn from new data. Overdependence on RPA can lead to high upkeep if business logic changes frequently.
• AI Agents Challenges: AI agents rely on quality data and models. They can produce unpredictable results or “hallucinations” when dealing with uncertain inputs. Developing and training AI agents can be costly and complex. They also raise concerns about security, bias, and explainability. In sensitive industries, companies may hesitate to fully trust AI decisions without human oversight. Additionally, performance might be slower for certain tasks, and fine-tuning agents requires expertise in AI.

As automation technology evolves, traditional automation tools are increasingly being surpassed by more flexible and intelligent automation technologies that integrate vision and language models, enabling better handling of unstructured data and adaptability to changing processes.

For example, the a16z article notes that early RPA bots “stumbled if the process was not rigid or clearly defined,” implying that without clear structure, even an AI agent might need to work harder or involve humans. Therefore, organizations must weigh these downsides. Factors like regulatory requirements (favoring RPA’s audit trails) or innovation needs (favoring AI’s adaptability) will influence the choice.

Ultimately, the limitations suggest a balanced approach: use RPA where reliability is critical, and use AI where flexibility is key. In practice, many firms start with RPA to cover low-hanging fruit and then layer in AI agents for more complex tasks.

Choosing the Best Solution

When deciding which is best for full-stack automation, consider:

Task Complexity: For highly routine, rule-based tasks, RPA is often the most efficient choice. It delivers quick ROI on simple processes. For tasks that involve judgment, language, or variability, AI agents are better suited.

Data Structure: If the work deals with structured data (forms, tables), RPA works well. If there is unstructured or semi-structured data (emails, documents, images), AI agents are needed.

Budget and Timeline: RPA projects can be faster to launch and might have lower initial costs. AI agent development may require more investment and expertise. However, AI agents can reduce long-term maintenance by handling changing scenarios.

Scalability Needs: RPA can scale by adding more bots, but it may not easily adapt to new types of tasks. AI agents, once trained, can be repurposed for similar tasks and may scale more flexibly.

Regulatory and Risk Factors: If compliance and consistency are paramount, RPA’s predictable nature is reassuring. If innovation and user experience are priorities, AI agents offer a competitive edge.

In many cases, the answer is not one vs. the other but a combination. As the Medium analysis notes, RPA and AI are converging, with Intelligent Automation blending their strengths. A hybrid strategy (hyperautomation) often ends up being the best for full-stack goals. Organizations may start with RPA to automate stable processes and gradually introduce AI agents to take on the complex parts. This way, the full stack is covered: RPA ensures consistency and efficiency, while AI agents introduce adaptability and intelligence.

Future of Full-Stack Automation

The landscape is evolving quickly. AI agents are poised to reshape how full-stack automation is achieved. According to experts, generative AI and agentic systems can finally fulfill RPA’s long-standing promises. As AI technology advances, it is expected to handle even more of the workload previously done by humans. The a16z article predicts a world where “AI agents are able to fulfill the original promise of RPA,” automating many roles that were manual before.

At the same time, RPA is not disappearing. Industry reports show RPA spending is growing, and vendors are integrating AI into RPA platforms. The CIO piece notes that although some believe AI will replace RPA, others see them working together and even remaining relevant in their niches. Organizations are likely to adopt hyperautomation – a mix of tools – rather than an either/or choice. For example, RPA vendors are adding AI agents to their suites, indicating the convergence of technologies.

Looking ahead, full-stack automation will involve several layers: AI agents planning and reasoning, RPA bots executing, and orchestration tools managing the flow. Emerging trends include self-building workflows (agentic process automation), smarter RPA with machine learning, and more plug-and-play automation components. In summary, the future is cooperative: AI agents and RPA together will drive more powerful automation solutions than either could alone.

Frequently Asked Questions (FAQs)

• Q: What exactly is the difference between an AI agent and RPA?
  A: AI agents are autonomous programs that use machine learning and advanced logic to perform tasks; they can learn and adapt. RPA is rule-based software that follows predefined instructions. In practice, AI agents tackle open-ended, unstructured tasks, while RPA handles structured, repetitive processes.
• Q: Can AI agents replace RPA entirely?
  A: Not entirely, at least not yet. AI agents can take over many complex tasks, but RPA is still very effective for straightforward processes. Many experts expect them to complement each other. A16z notes that AI agents may fulfill RPA’s promise, but also highlights that current software still needs RPA for known patterns. In the near future, the best approach is often a hybrid of both.
• Q: What is full-stack automation, and why is it important?
  A: Full-stack automation means automating every layer of an organization’s workflow, from the user interface to the back-end systems. It ensures processes are seamless end-to-end. This is important because it leads to greater efficiency and consistency across all operations.
• Q: Which industries benefit most from RPA vs. AI agents?
  A: Industries with high volumes of rule-based tasks – like finance, banking, insurance, and manufacturing – have historically gained the most from RPA. AI agents are valuable in sectors needing complex decision-making and user interaction, such as customer service, IT operations, and analytics. But both technologies are spreading across all fields as automation needs grow.
• Q: What are the risks of using AI agents or RPA?
  A: RPA risks include breaking down if processes change (since bots follow fixed steps). It can also require ongoing maintenance. AI agent risks include making incorrect decisions or “hallucinating” results, as well as data privacy and bias concerns. Companies must carefully manage AI training data and maintain oversight. Despite these risks, proper design and monitoring can mitigate problems.
• Q: What is hyperautomation and how does it relate?
  A: Hyperautomation (also called Intelligent Automation) refers to using a stack of automation tools – RPA, AI, BPM, and others – to automate as many processes as possible. It’s essentially the strategy of combining RPA and AI agents (and more) to achieve full-stack automation. By using them together, organizations automate both simple and complex tasks.

Conclusion

AI agents and RPA each shine in different areas of automation. RPA is a trusted tool for handling large volumes of repetitive, structured tasks with speed and precision. AI agents bring intelligence and adaptability, tackling complex, variable processes that involve learning and decision-making. For achieving full-stack automation, the best strategy is often to use them together. By leveraging RPA’s reliability and AI’s cognition, businesses can create end-to-end workflows that are efficient, intelligent, and resilient. In the evolving automation landscape, staying informed and flexible is key. Whether you’re optimizing an existing RPA solution or exploring new AI-driven agents, understanding both technologies will help you build a smarter, more automated enterprise.

Next Steps:

• Translate this article into another language to share these insights with a global audience.
• Generate blog-ready images (e.g., process diagrams or concept visuals) to illustrate AI Agents and RPA in action.
• Write a new article on a related topic, such as “How to Implement RPA and AI in Small Businesses” or “Top Hyperautomation Trends for [Year].”