So, what is an AI agent?
AI agents are the next evolution in automation, enabling intelligent systems to process information, reason, and act autonomously to achieve specific goals. Unlike traditional software with static workflows, AI agents adapt dynamically to changing inputs and environments.
While RPA is like a factory robot following programmed steps, AI agents are more like interns tackling complex problems and figuring out the best solutions. For example, instead of a chatbot simply routing queries, an AI agent in customer support can handle inquiries, refine responses based on past interactions, escalate issues, and improve over time.
Key components of AI agents include:
- Memory: Retaining context and learning from past interactions for consistent, effective actions.
- AI models: Using techniques like task decomposition and LLMs to execute workflows seamlessly.
- Systems integration: Leveraging tools like IDP and APIs to interact with real-world systems effectively.
By combining these elements, AI agents push automation further than ever before, showcasing how far we’ve come—and how much further we can go.
Where do AI agents operate?
AI agents are driving innovation across industries by integrating tools like intelligent document processing (IDP) and Process Intelligence (PI) to automate and optimize complex tasks. Here’s a few examples of what’s possible:
- Customer service: AI agents enhance customer experience by providing real-time support, personalized recommendations, and seamless issue resolution. With IDP, they can process forms and contracts, ensuring smooth interactions.
- Data automation: From invoices to contracts, agents leverage tools like IDP to extract and process data accurately at scale. For example, financial agents can retrieve ERP data, process reports, and ensure compliance autonomously.
- Supply chain management: Using PI, agents predict demand, optimize inventory, and manage logistics, dynamically adjusting to delays keeping things on track.
- HR and recruitment: AI agents streamline hiring by automating resume screening, interview scheduling, and creating tailored onboarding programs.
- Workflow orchestration: For cross-system workflows, agents combine IDP to extract data, PI to validate it on compliance, and RPA to automate processes like claims approval or exception escalation.
IDP and PI are essential enablers for these transformations, equipping AI agents to deliver efficiency and intelligent decision-making across industries.
Challenges and opportunities
As we delve deeper into the use of agentic AI systems, it’s important to weigh the potential benefits against the complexities they bring. While these systems offer unprecedented capabilities, they also come with unique challenges that organizations must navigate carefully. Below, we explore both the hurdles and the opportunities presented by agentic AI.
-
Challenges
The deployment of agentic AI systems offers great potential but also brings complexity and risks. A key challenge is data quality—AI agents are only as effective as the data they use, and poor or unstructured data can lead to errors. This makes intelligent document processing essential for their success.
Equally important is the ethical dimension. When AI makes autonomous decisions—especially those affecting people’s lives—human oversight is essential. These systems must follow clear ethical and legal standards, requiring strong governance for transparency and accountability. At the same time, automation may displace roles, so organizations must retrain and reallocate talent thoughtfully, balancing strategy with empathy and clear communication.
-
Opportunities
Despite the challenges, the opportunities of agentic AI systems far outweigh the risks. These systems can drive efficiency, reduce costs, and enable faster decision-making, giving businesses a competitive edge. With advanced capabilities like memory, planning, and reasoning, agentic systems offer unmatched personalization, higher customer satisfaction, and better scalability.
Hybrid approaches that combine AI with traditional logic-based automation provide a balanced solution, leveraging the strengths of both for effective and reliable systems. Businesses that embrace agentic AI can achieve transformative growth and innovation, positioning themselves as industry leaders.
Why AI agents rely on tools
AI agents don’t act alone—they depend on tools to act effectively. Without access to external systems for data extraction, analysis, or task execution, their capabilities are limited.
For example, an insurance claims agent might use IDP to accurately extract data from submitted claims and pair it with LLMs to determine anomalies or potential fraud. It then submits validated findings through an RPA system for approval. Combining different technologies ensures the process is not only intelligent but also highly accurate and compliant.
Hybrid approaches often prove optimal in complex workflows, where AI provides adaptability and insight, while rule-based systems ensure structure and compliance.
Do you want to learn more?
At ABBYY, we’re at the forefront of these innovations. Subscribe today for exclusive insights via our AI Pulse podcast, where industry leaders share cutting-edge trends, use cases, and strategies to optimize automation in your enterprise.
Don’t just watch the future happen—shape it with AI-driven solutions, purpose-built to your industry.
