Understanding the different types of AI agents

Simple reflex agents

These agents operate on a straightforward "condition-action" basis. They react to immediate environmental stimuli based on a predefined set of rules without considering past events.

For example, a rule might be: IF the temperature is high, THEN activate the cooling system.

Simple reflex agents are best suited for simple, fully observable environments where the current perception is sufficient to make a decision. Common examples include automated doors, thermostats and basic vacuum-cleaning robots that change direction upon hitting an obstacle.

Model-based reflex agents

As an evolution of simple reflex agents, these agents maintain an internal "model" or state of the world. This allows them to function effectively in partially observable environments where the current perception alone is insufficient. They use memory of past perceptions to understand how the world changes independently of their actions.

This internal state allows for more informed decisions. A self-driving car, for instance, can use its model to anticipate the movement of other vehicles even when they are temporarily obscured from view, leading to safer and more intelligent navigation.

Goal-based agents

Goal-based agents take decision-making a step further by considering the future consequences of their choices. They can select actions from a range of possibilities that will help them achieve a specific, predefined goal.

Utility-based agents 

Utility-based agents advance this concept by choosing actions that maximize utility, which is a measure of happiness or success. This allows them to handle conflicting goals by selecting the option with the best overall outcome, providing a more nuanced approach to complex decision-making.

Learning agents

These sophisticated agents can improve their performance over time by learning from experience. A learning agent typically consists of four main components:

• Learning element
• Performance element
• Critic
• Problem generator

The performance element executes actions, the critic evaluates how well the agent is doing, and the learning element makes improvements based on that feedback. The problem generator encourages the agent to explore new and potentially better actions, refining its decision-making capabilities through continuous iteration.

> Read more | Understand the potential of AI agents

The enterprise automation spectrum: From scripts to agentic AI 

Automation has evolved far beyond simple scripts and first-generation chatbots. Today, it represents a full spectrum of capabilities, with agentic AI at the most advanced end. Agentic AI doesn't just respond to commands; it proactively perceives its environment, reasons through problems and acts to drive business outcomes.

This fundamental shift enables organizations to move from automating isolated, repetitive tasks to orchestrating complex, end-to-end processes with intelligent systems. It marks the transition from basic automation to true intelligent automation, where digital entities become active partners in achieving strategic goals.

> Read more | Ultimate guide to enterprise automation 

Key AI agent types in an enterprise setting

Within a business context, AI agents are often categorized by their function and scope. Hyland focuses on three primary types that deliver the most significant impact and value: task agents, collaboration agents, enterprise agents and RAG agents.

> Read more | AI agents for business: Driving smarter decisions

Task agents: Automating structured processes

Task agents are single-purpose agents designed to execute a specific, defined task with a structured input and output. They typically run synchronously within workflows, relying on hard-coded prompts or configurations to ensure consistency and precision in their execution.

These agents excel at automating high-volume, rules-based processes. Use cases include automating invoice matching in accounts payable, where one retailer saved $2 million annually. Another example is processing citizen service requests, where a U.K. city council achieved 98% faster case processing and cleared a significant backlog.

Find out more about Hyland Knowledge Discovery.

Collaboration agents: Augmenting human workflows

Collaboration agents are conversational agents designed for iterative dialogue with users to retrieve information or execute tasks. Unlike task agents, they can manage multiple conversations, stop mid-flow to ask for clarification and incorporate human feedback to refine their actions.

The agents in Knowledge Discovery are a prime example of this type. They empower users to ask natural language questions and receive consolidated, context-aware answers drawn from multiple enterprise content sources, effectively augmenting the user's ability to find information.

Enterprise agents: Governed, context-aware intelligence

Enterprise agents provide enterprise-grade AI capability designed to operate within the rigorous constraints of mission-critical workflows. These agents are governed, context-aware and embedded directly into the systems where work happens. In sectors like healthcare, insurance and finance, these agents leverage deep context to facilitate tasks such as intelligent medical records review, financial claims assessment and loan exception processing.

Orchestration happens through a dedicated framework rather than the agents themselves. Using tools like OnBase WorkView, AI-enhanced Hyland Automate, and the agent mesh, organizations coordinate multiple agents to solve broad, industry-specific problems. This orchestration ensures that AI-powered insights fuel complex, multistep solutions from end to end. The result is improved and trusted process outcomes and enhanced productivity across the enterprise.

RAG agents: Enhancing context and accuracy

Retrieval-Augmented Generation (RAG) is a technique that connects large language models to external, up-to-date knowledge bases. This allows an AI to generate more accurate and contextually relevant answers by pulling from trusted enterprise information rather than relying solely on its training data.

Agentic RAG enhances this process by using AI agents to orchestrate information retrieval. Instead of connecting to a single data source, these agents can intelligently route complex queries to multiple knowledge bases, plan multistep searches and use different tools to find the best information.

This creates a more adaptive and powerful system. Agentic RAG is ideal for enterprise applications like sophisticated customer support bots that need to access both technical documentation and CRM data, or internal knowledge portals that provide employees with comprehensive answers drawn from across the entire organization.

The power of collaboration: Multiagent systems (MAS)

While single agents can automate specific tasks, the true power of agentic AI is unlocked when multiple agents work together. This collaborative approach, known as a multiagent system (MAS), enables organizations to tackle problems that are too large or complex for any single agent to solve alone.

How multiagent systems work

An MAS is a computational system where multiple autonomous agents interact to achieve individual or collective goals. These agents can be cooperative, working toward a common objective, or competitive, pursuing conflicting goals. They can also be organized in hierarchies to manage complex workflows.

This structure enables emergent behavior, where complex, system-wide patterns and solutions arise from simple, local interactions between individual agents. It provides a scalable and flexible framework for solving dynamic business problems.

Hyland Enterprise Agent Mesh: A network of specialized agents

Enterprise Agent Mesh is a Hyland innovation that provides a framework for coordinating how different Enterprise Agents work together. Powered by Hyland Enterprise Context Engine, it enables agents to be aware of each other and the broader business context, allowing for intelligent automation of complex, enterprise-wide processes.

This mesh uses open standards to facilitate communication between Hyland and third-party agents, creating a connected and scalable network rather than a collection of isolated tools. This approach ensures that automation efforts can grow and adapt with the organization.