Understanding AI agent types: A guide to categorizing complexity

In their most advanced form, AI agents are autonomous systems designed to perceive their environment, make decisions, and take actions to achieve specific goals. As AI technology evolves, agents are becoming pervasive across many industries like finance, healthcare, manufacturing, and customer service. However, not all AI agents are created equal—their capabilities vary widely in terms of autonomy, decision-making, adaptability, and interaction with their environment.

When thinking about AI agent use cases and the effort to implement them, it is important to understand the complexity of every agent being considered. This article proposes eight key AI agent types to assist in that effort. Each agent type is categorized by increasing complexity from simple function interactions to advanced multi-agent collaborations. 

This progression can help us understand how agents scale in capability—from reactive responses in predictable settings to adaptive, learning-driven behaviors in dynamic, uncertain environments—offering a clear lens for evaluating AI agent use case complexity, and when selecting the right AI agent for your needs. The classifications in this article draws from a rich body of existing research and industry frameworks, including foundational academic work like the taxonomy in Stuart Russell and Peter Norvig's Artificial Intelligence: A Modern Approach that outlines core agent architectures, IBM’s agent classification within agentic workflows, the work Anthropic is doing with agents, and recent scholarly contributions such as Naveen Krishnan's 2025 paper, "AI Agents: Evolution, Architecture, and Real-World Applications." 

This article proposes eight classification types for AI agents, summarized here and ranked by complexity level (low to high). Complexity is assessed based on AI agent implementation factors like conversation memory, planning depth, learning ability, handling of uncertainty, and multi-agent coordination.

1. Functional agents: Prioritizing essential functionality

At the lowest end of the complexity level, functional agents focus on bridging AI capabilities with external systems through deterministic, rule-based integrations. They emphasize practical utility without the need for memory, planning, or learning, making them ideal for adding simple but essential functionality to AI systems.

Key characteristics:

• Deterministic and reliable, leveraging predefined rules for integration.
• Relies on traditional developer skills like API handling, hardware interfacing, event processing, and data pipelining.
• No adaptability or autonomy, excels in targeted, no-frills automation.

Examples:

• API-wrapping agents: Translate LLM outputs into structured API calls, such as an AI shopping agent querying inventory availability for purchases.
• Event-driven agents: React to real-time triggers, such as monitoring email inboxes to draft responses or flag urgent messages.
• Data aggregation agents: Compile data from social media APIs to generate summarized reports for marketing dashboards.

These agents are highly accessible and efficient for straightforward tasks but are constrained to their predefined scopes, serving as a foundational layer before more reactive types.

2. Simple reflex agents: The basics of reaction

At the next level, simple reflex agents operate like basic if-then rules. They perceive the current state via sensors and respond immediately using predefined condition-action pairs, without storing past experiences or anticipating future states.

Key characteristics:

• Sensors for input, actuators for output.
• Works best in fully observable, static environments.
• No internal state or reasoning beyond the present.

Examples:

• A factory sensor that halts a machine upon detecting an obstruction.
• An email auto-responder that sends a canned reply based on keywords.

These agents are straightforward and efficient for repetitive tasks but falter in dynamic or incomplete scenarios.

3. Model-based reflex agents: Adding internal awareness

Building on simple reflex agents, model-based agents introduce an internal "world model" to handle partial observability. They track how the environment evolves and infer unseen aspects, allowing reactions in less transparent settings.

Key characteristics:

• Maintains state history to fill perceptual gaps.
• Still rule-based, but uses models for better context.
• Suitable for environments where sensors don't capture everything.

Examples:

• A smart thermostat that adjusts based on inferred room occupancy.
• Network monitoring tools that detect issues in IT infrastructure.

This type increases complexity by adding memory-like modeling, enabling operation in real-world variability without full planning.

4. Goal-based agents: Planning for the future

Goal-based agents focus on desired outcomes. They search for action sequences that lead to goals, using algorithms to predict and evaluate future states.

Key characteristics:

• Incorporates a world model and planning mechanisms.
• Assesses actions based on goal proximity.
• Handles well-defined objectives in predictable domains.

Examples:

• Project management software that organizes tasks to meet deadlines.
• Robotic systems in warehouses that plan paths to retrieve items.

Complexity rises here with forward-thinking planning, making these agents more versatile than reflex types but they are still limited to single goals.

5. Learning agents: Adaptation through experience

Learning agents introduce adaptability, improving performance over time via feedback from interactions. They aren't just programmed—they evolve by exploring, critiquing outcomes, and refining behaviors.

Key characteristics:

• Components include performance evaluators, critics, and problem generators for exploration.
• Uses machine learning (ML) techniques like reinforcement learning.
• Are effective in unknown or changing environments.

Examples:

• Streaming services that refine recommendations based on user behavior.
• Chatbots that learn from conversations to enhance responses.

This marks a complexity shift, as learning enables long-term optimization, though it requires data and can be resource-heavy.

6. Utility-based agents: Optimizing trade-offs

Utility-based agents go beyond goals by quantifying desirability with a "utility function." They select actions that maximize overall satisfaction, balancing multiple factors like cost, time, and risk.

Key characteristics:

• Assigns numerical values to states for comparison.
• Builds on goal-based planning with multi-objective evaluation.
• Ideal for trade-off-heavy decisions.

Examples:

• Stock trading bots that weigh profit against risk.
• Building management systems that optimize energy and comfort.

High complexity stems from sophisticated evaluation, making these agents robust for nuanced, real-world optimization.

7. Hierarchical agents: Structured decomposition

Hierarchical agents manage complexity through layers: high-level agents break tasks into sub-tasks, delegating to lower-level ones. This mimics human organization, scaling to intricate problems.

Key characteristics:

• Tiered architecture for abstraction and modularity.
• Higher levels handle strategy, lower ones execute tactics.
• Reduces overload by dividing responsibilities.

Examples:

• Autonomous vehicles where top-level plans routes and low-level controls steering.
• Enterprise workflow systems automating multi-step processes like approvals.

This type's sophistication lies in hierarchical coordination, enabling handling of large-scale tasks that overwhelm flat structures.

8. Multi-agent systems: Collaboration and emergence

The current pinnacle of complexity, multi-agent systems involve multiple agents interacting—cooperating, negotiating, or competing—to solve problems no single agent could tackle alone. Emergent behaviors arise from these dynamics.

Key characteristics:

• Agents communicate via protocols.
• Different agents may have shared or conflicting goals.
• Handles distributed, uncertain environments.
• Requires mechanisms for conflict resolution and synchronization.

Examples:

• Supply chain networks where agents optimize logistics across suppliers.
• Traffic management systems coordinating signals for flow.

Very high complexity arises from inter-agent dynamics, offering scalability but demanding robust design to avoid chaos.

Conclusion: Choosing the right complexity for your AI agents

Categorizing AI agents by complexity—from simple functional agents to highly complex multi-agent systems—reveals a clear spectrum of capabilities, each suited to different challenges. When getting started with AI agents, it's often best to begin with lower-complexity functional or reactive agents for efficiency in stable tasks. As the complexity of your problems increases and your expertise grows, scaling up to deliberative, learning, and ultimately multi-agent systems will provide the adaptability and power needed for more volatile and intricate scenarios. The future of AI lies in our ability to not only understand these distinct types of AI agent, but also to effectively blend their strengths.

Get started with AI agents

• Official Red Hat Overview: For a comprehensive understanding of agentic AI, explore this article.
• Building Enterprise-Ready AI Agents: Learn how to streamline development with Red Hat AI in this Red Hat blog article.
• Agentic AI Examples with Red Hat AI: Explore various Agentic AI frameworks and LLMs running on Red Hat AI platforms.
• Red Hat AI Agentic Demo: Experience a full agentic AI workflow with real-time interactions across multiple systems including CRM, PDF generation, and Slack integrations.