AI agents
Analyze, plan, and execute tasks autonomously
What are AI agents?
AI agents are advanced, autonomous, or semi-autonomous software systems that analyze, plan, and execute tasks independently. When used with proper governance, they leverage artificial intelligence to process information, make decisions, and perform actions while adhering to established business rules. Most importantly, these intelligent agents can optimize operations through continuous learning and adaptation, improving efficiency and quality of output over time.
Why are AI agents important?
AI agents have the potential to revolutionize the business world. When used effectively, they can significantly boost productivity, deliver personalized experiences, enhance operational efficiency, and drive competitiveness and growth for both large and small organizations. Rather than simply automating small tasks, AI agents can help businesses orchestrate an entire process from end to end, while applying continuous AI to improve processes over time.
Benefits of AI agents
- Seamless collaboration: AI agents work hand in hand with your employees, enhancing their capabilities, reducing manual labor, and improving productivity.
- Intelligent action: With proper guidance and oversight, AI agents can plan, reason, and act to help achieve business goals.
- Business transformation: AI agents are one of the keys to legacy transformation, helping businesses leave behind outdated technology to embrace the vision of the autonomous enterprise.
How do AI agents work?
AI agents operate by defining objectives, analyzing situations, and taking action through workflows. Typically, with large language models (LLMs) at their core, AI agents can reference algorithms and machine learning models to process data, recognize patterns, create subtasks, and ultimately make autonomous decisions. With human oversight and governance, teams can outline complex workflows for agents, rapidly increasing productivity while enabling continuous learning and adaptation.
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What’s the difference between AI agents and autonomous agents?
Components of AI agents
Center-out business architecture
Effective automation with AI agents requires a centralized approach to logic – ideally centered around the work being done and outcomes desired, rather than embedding logic into channels or chatbots, or burying it into the back-end of legacy systems.
Workflow automation
Without the structure of workflow automation, AI agents can become unmanageable. Combine AI agents with trusted workflows to define, execute, and manage complex processes automatically, coordinating multiple steps, handling exceptions, and ensuring compliance while maintaining efficiency and accuracy throughout end-to-end processes.
Orchestration
Without orchestration, AI agents in the enterprise quickly lose effectiveness. As part of a business orchestration and automation technology (BOAT) roadmap, orchestration is a critical component of ensuring agents deliver ROI and operate according to enterprise rules and boundaries.
Examples of AI agents in the real world
Customer service assistant
A customer service AI agent could autonomously handle customer inquiries by understanding intent, accessing relevant information from back-office workflows and systems, and guiding users through enterprise-approved resolution processes, while seamlessly escalating complex cases to human agents when needed.
Financial fraud detection
In financial services, an AI agent could monitor transactions in real time, identifying suspicious patterns and automatically initiating investigation workflows with human intervention, protecting customers while reducing false positives and operational overhead.
Healthcare process optimizer
AI agents in healthcare could streamline patient care workflows by coordinating appointments, managing documentation, and ensuring compliance with protocols and regulations, while involving human stakeholders as necessary and adapting to changing priorities.
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Potential challenges with AI agents
While AI agents present an incredible opportunity for modern organizations, tension is inevitable with any disruptive technology. For example, a recent Pega study showed that while 51% of workers use AI agents at least once a week in their current role, 44% expressed concerns about these tools’ inability to replicate the human intuition and emotional intelligence they see as essential to their jobs.
Solutions to AI agent challenges
Success requires implementing strong governance frameworks to promote trust and empathy, establishing clear operational boundaries, and maintaining human oversight. Organizations should focus on data quality, robust testing, and continuous monitoring while gradually expanding automation capabilities through a controlled, phased approach.
How to implement AI agents in the enterprise
The future of AI agents
AI agents will become increasingly sophisticated, offering enhanced autonomous capabilities and deeper integration with business operations. With end-to-end orchestration, they'll feature improved learning abilities, more natural interaction with humans, and greater adaptability to complex situations, driving the evolution of truly autonomous enterprises.
Frequently asked questions about AI agents
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