Explore the power of AI agents

What are AI agents?

AI agents are autonomous digital entities that use intelligence to automate tasks often involving multiple steps and decisions. These agents are capable of addressing complex objectives, processing information, responding intelligently to inputs and improving over time.

For example, Knowledge Discovery, part of Hyland Content Intelligence, is powered by AI agents configured to answer natural language questions, delivering information to accelerate decisions and boost efficiency. By creating Knowledge Discovery AI agents tailored to their needs, organizations can go beyond storing content and access relevant business insights quickly.

AI agents are revolutionizing workflows by combining intelligence, autonomy and adaptability to enhance efficiency and scalability. Key traits that make them valuable for modern businesses include:

• Autonomous: Operate independently, making decisions based on rules and learned patterns.


• Intelligent: Leverage AI to process data, learn, and improve over time.


• Task-oriented: Handle specific tasks, from simple queries to complex workflows.


• Configurable: Adapt to diverse use cases with customizable settings.


• Interoperable: Integrate seamlessly with other systems and workflows.


• Monitorable: Offer tools to track performance and outcomes.


• Evolvable: Continuously improve via feedback and updates.


• Reusable: Adapt across workflows and departments, offering modularity and scalability.

These characteristics enable organizations to extend workforce capabilities as needed and implement AI at scale.

The benefits AI agents offer businesses

Artificial intelligence itself has already boosted the productivity of organizations. AI agents will accelerate this by offering:

• Enhanced autonomy and intelligence: AI agents work independently, making decisions based on set rules and learned patterns. This frees up human teams for more strategic work.


• Scalability: AI agents enhance human workforce capabilities by handling tasks at scale, allowing for greater efficiency and productivity.


• Task specificity: AI agents handle a wide range of tasks, from simple tasks to complex workflows involving multiple steps and decision-making, ensuring both efficiency and accuracy.


• Integration with business processes: AI agents easily integrate into your existing systems, working with other agents, software and even humans to improve overall efficiency.


• Continuous improvement: Business processes are closed-loop systems. When an agent or human acts, its outcome is observed and recorded according to set goal metrics. These insights will be used to improve agent behavior allowing AI agents to constantly learn and evolve. Tools like Hyland’s Agent Builder, as well as our domain expertise, can facilitate this feedback and update process, helping organizations stay current with the latest business trends and AI advancements.


• Cost management: Control AI costs by enabling access to various LLM providers, which optimizes usage based on task complexity and importance for cost-effective execution.


• Workflow automation: AI agents automate complex tasks, allowing you to achieve goals quickly and efficiently. This means less manual work and more focus on high-level objectives.

How to ensure a successful AI agent deployment

From data quality to security and process alignment, organizations need a strategic approach for successful AI agent deployments.

Hyland Content Intelligence makes this possible with products like Knowledge Discovery, Knowledge Enrichment and Agent Builder, which transform unstructured data into actionable, AI-ready insights — driving automation, scalable AI adoption and innovative solutions. Key strategies include:

Ensuring access to clean, contextual data

One of the biggest obstacles to AI adoption is the quality and state of the data being fed into it.

Source: Forrester™ fifth annual Content Services Pulse Study

As inaccurate or incomplete information leads to unreliable insights and poor decision-making, it is important to ensure the data fed into the AI is sanitized and enriched.

Knowledge Enrichment transforms raw, unstructured content into meaningful, structured data that is ready for use in AI, automation and solution building.

Integration with human processes and automation

AI agents are more effective when used to automate specific business processes. Achieving this requires developing tools that enable AI agents to understand how to do the assigned work.

Agent Builder empowers business process SMEs to chain AI agents that autonomously execute complex workflows, adapting to evolving inputs and outputs that can’t be pre-programmed. With built-in human-in-the-loop capabilities, it ensures critical decisions receive human oversight — blending automation with human judgment for greater accuracy, accountability and trust.

You can also leverage Hyland’s industry expertise, domain knowledge, services and products to build and manage business process model and notation (BPMN) workflows.

Hyland brings decades of experience in content services, intelligent automation and industry-specific AI applications. Our team works closely with organizations to:

• Assess AI readiness: Evaluating data quality, security and governance frameworks to ensure AI solutions operate effectively.


• Design tailored AI strategies: Aligning AI capabilities with business goals to maximize automation and efficiency.


• Ensure seamless integration: Embedding AI solutions into existing workflows while minimizing disruption.

With deep industry knowledge and a commitment to innovation, Hyland helps organizations harness AI in a way that enhances automation, improves insights and drives better business outcomes.

How AI agents work

AI agents are like digital experts who understand your requests, plan the steps and get things done. They are specialized and always available, enabling businesses to assemble virtual teams to boost productivity, maintain consistency and adapt quickly to changing demands. These agents do this by:

Understanding the goal

First, the AI agent needs to understand what you want to achieve, whether it's a simple task like "prioritize documents based on deadlines" or a complex one like "assign initial severity scores based on the described damage and policy limits.” It’s important to identify processes and workflows that you want to automate using AI agents.

Breaking it down

For complex tasks, the agent analyzes the details of the tasks and breaks them into smaller, manageable parts. This modular design enhances reusability and flexibility, allowing the agent to adapt to different situations.

Gathering information

The agent collects the necessary data from various sources, such as the internet, internal databases or through API integration with other systems. It does this using tools and parameters that users provide during configuration. Intelligently using the right tools based on their description and limitations provided.

Making decisions

This is where the "intelligence" comes in. The agent uses its knowledge and the gathered information to decide on the best course of action, often guided by prompt templates that allow for dynamic input and personalized actions.

Taking action (workflow integration)

The agent executes the plan, performing content-driven tasks by analyzing and taking action on insights from enterprise content or interacting with other software. AI agents can fit naturally into your existing BPMN workflows.

For more complex tasks, multiple AI agents may work together in hierarchical workflows, each handling a specific part of the overall task.

Learning and improving

AI agents improve over time by learning from feedback and historical analysis. This allows them to refine their performance and make better decisions. Benchmarking helps ensure the agent is operating effectively by comparing its performance to standards or other agents.

Cost management

Efficient cost management is crucial. This involves resource allocation to select the right AI models for different tasks, tracking usage to identify savings opportunities and adjusting the agent's usage based on needs. Provider flexibility allows you to choose the best balance of cost and performance across different AI providers.

By combining these elements, AI agents become powerful tools for automating tasks, improving efficiency and making smarter decisions.

How to implement AI agents into your organization

Implementing AI agents requires thoughtful planning. Follow these key steps for successful deployment:

• Define objectives: Identify clear goals for your AI agent, such as automating customer support, improving decision-making or optimizing processes. Well-defined objectives will shape your implementation strategy.


• Prepare your data: AI agents rely on high-quality, AI-ready data. Ensure your data is accessible, understandable, actionable and AI-ready for optimal performance. Knowledge Enrichment, part of the Hyland Content Intelligence product line, has tools that can help you do this seamlessly.


• Choose the right agent: Select an AI agent that aligns with your goals, considering factors like task complexity, environment and autonomy level.


• Monitor and optimize: Continuously assess your AI agent's performance. Use feedback and data to fine-tune its algorithms, ensuring it adapts to evolving needs.

If you are interested in unlocking AI’s potential more broadly in your organization, you may want to consider how to bridge the skills and technology gap.

> Read more | Before you invest in AI, assess your AI-readiness

Use cases for AI agents

AI agents are versatile tools that serve a variety of functions across different industries. Below are several key use cases and possible future developments demonstrating how AI agents can work with other solutions and tools to enhance efficiency and drive innovation.

Knowledge discovery

Agents can enhance search capabilities by understanding your specific needs to provide better results.

Example: A knowledge discovery agent can sift through vast amounts of unstructured data to find the latest updates on a product, pulling information from documents, internal notes, emails and even external sources. This streamlines the process and reduces the time spent on manual research.

Customer support

Together with other solutions, AI agents can help automate customer service workflows by processing support tickets, categorizing challenges, escalating for human intervention and suggesting responses or solutions.

Example: In a customer support scenario, the agent can route a billing challenge to the right department and automatically pull from past cases to suggest an appropriate response. It can also prioritize urgent tickets based on predefined criteria, improving response times and customer satisfaction.

> Read more | Elevating experiences: The power of AI in customer service 

Healthcare data analysis

In the near future agents can be developed for analyzing healthcare data to identify patterns, trends and anomalies, these agents can potentially work together with other technologies in generating reports for predictive healthcare to improve patient outcomes.

Example: In a healthcare setting, an AI agent can be configured to analyze patient data to calculate the number of diabetes diagnoses over the past year, identifying potential risk factors. It can also track trends in patient outcomes, helping healthcare providers make data-driven decisions to improve care.

Financial services

AI agents can work in tandem with other tools to automate routine tasks, assess financial risks and ensure compliance with regulations. They can help process large datasets quickly and more accurately, reducing human error and increasing operational efficiency.

Example: An AI agent can help assess the risk of a new client by analyzing their financial history, including credit scores, transaction data and other relevant factors. It can generate risk profiles and provide actionable insights to financial analysts or automated systems, enabling faster and more informed decision-making.

Contract analysis

In synergy with other solutions, agents can assist legal teams in reviewing contracts, identifying key terms and flagging potential risks. This helps to automate routine tasks, such as summarizing legal documents or checking for compliance with regulations.

Example: In a legal context, an agent could quickly scan through large numbers of legal contracts, summarizing key clauses and highlighting any potential legal risks or ambiguities. This makes it easier for legal teams to review large volumes of contracts in less time and with higher accuracy.

Insurance claims processing

Agents can work together with other tools to help automate the initial review of insurance claims, speeding up the process and ensuring accuracy. AI agents can also help in flagging potentially fraudulent claims, reducing the time and resources required for manual review.

Example: After a natural disaster, insurance claims flood in. An AI agent can help to quickly validate claims, identify patterns of fraud and flag claims that need further investigation. This speeds up the approval process, ensuring legitimate claims are processed quickly while maintaining fraud detection.

Minutes of meeting automation

Government agencies and public sector organizations often hold lengthy meetings with extensive agendas, generating hours of recordings and transcripts. Manually reviewing these materials to create meeting minutes is time-consuming, often delaying their availability.

Example: An AI-powered meeting assistant can analyze transcripts, identify key decisions and action items, and generate a summary of the meeting minutes. This accelerates the process, reducing manual effort while ensuring accuracy and compliance with documentation standards.

This is just a glimpse into the diverse possible applications of AI agents. They can be tailored to various needs and industries, offering increased efficiency and valuable insights.

The future of work with AI agents

The future of work is evolving alongside the rise of AI agents. These intelligent tools are driving new levels of efficiency, innovation and productivity as they become more integrated into workflows. To fully realize their potential, organizations need a robust content intelligence system that transforms content and unstructured data into actionable, AI-ready content to power intelligent automation.

Hyland is here to help. Our Content Intelligence offerings and industry expertise provide the foundation for deploying AI agents effectively. From making content AI-ready with Knowledge Enrichment to unlocking key business insights with Knowledge Discovery and creating autonomous AI agents with Agent Builder, Hyland empowers organizations to scale AI across the enterprise and achieve meaningful transformation.

Ready to get started? Connect with Hyland to see how we can help you unlock the potential of AI agents in your organization.

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