22 different types of AI agents (with examples)

AI agent sophistication ladder

Before we get into the weeds of it, we need to unpack where on the sophistication ladder each agent clings to. Based on Yu Huang’s original sophistication ladder, here’s a similar framework that shows how agents evolve from simple task helpers to fully autonomous multi-agent systems. Think of it as a maturity curve. At Level 1, you’ll find the most basic automation: agents that handle routine, rules-based tasks like scheduling or sending reminders. By the time you reach Level 6, agents are proactive collaborators with distinct “personalities,” capable of planning, completing complex tasks, and even adapting their behavior to shifting business contexts.

At the heart of this evolution are large language models (LLMs), powering the leap from narrow, reactive systems to agents that can reason, plan, and coordinate in real time. As teams explore AI for project execution and enterprise workflows, the need to make sense of the shift from GenAI tools to purpose-built agentic AI becomes paramount.

7 main types of AI agents

When people talk about AI agents, they often focus on what they do — writing code, triaging tickets, or generating reports. But beneath those tasks lies a more fundamental question: how do they think? 

IBM popularized a framework that answers this, breaking AI agents down into five core types based on their decision logic — the way they process inputs, evaluate options, and choose an action. 

However, as AI systems became more complex in recent years, the need for multi-agent systems (MAS) and hierarchical agents became apparent. Therefore, there are now seven main types of AI agents. 

These are the brains of the operation. Regardless of the industry, task, or interface, every AI agent falls into one (or more) of these categories: simple reflex, model-based, goal-based, utility-based, learning, MAS, or hierarchical. 

1. Simple-reflex agents

At the very bottom of the AI agent sophistication ladder (Level 1) sit simple-reflex agents. These are the most basic form of AI agents — they respond directly to predefined, condition-action rules, and follow a strict rulebook: “if X happens, then do Y.” These are the agents you didn’t even know were classified as agents because they seem so basic (and you probably come across them often).

Simple-reflex agents don’t keep track of history, plan ahead, or learn, but their simplicity makes them fast and predictable.

Examples

•  A thermostat that turns on heating when the temperature drops below a threshold
• A spam filter that flags emails if they contain certain words
• A traffic light controller that changes signals based only on the state of sensors (e.g., cars present or not)

They’re perfect for simple, repetitive, and deterministic tasks, such as monitoring thresholds, auto-notifications, or running a status update when a condition is met. 

In other words, when the rule is black and white and the environment doesn’t change much, they’re reliable workhorses. Simple-reflex agents are the foundation of the ladder. Every step up in agent sophistication adds memory, learning, or reasoning to this reactive base.

2. Model-based agents

These agents add an important twist. Unlike simple reflex agents, which act only on what they see right now, model-based agents carry around an internal model of their world, like a mental map, that helps them interpret what they can’t directly observe. 

This is essentially a structured representation of what’s already happened and what might happen next. It’s what lets them reason through situations where the environment is incomplete or even misleading.

Their thinking process is a loop: 

1. Sense new information
2. Update their internal picture of the world
3. Simulate possible outcomes
4. Choose the best course of action

If reflex agents are like traffic lights reacting to cars, model-based agents are more like navigation apps: they track where you’ve been, anticipate obstacles ahead, and reroute based on conditions you can’t yet see.

In practice, this means they can spot hidden dependencies, anticipate blockers, or propose alternative paths forward. A type of project management agent, for example, might notice that a backend task marked as “Done” still hasn’t published an API contract, and flag it before the frontend team hits a wall. Or, it could predict workload conflicts next week and suggest shifting tasks today to prevent a bottleneck.

Examples

• GPS apps like Google Maps, which model both your location and the traffic network to guide you, even when you go through a tunnel where direct signals are lost
• Customer support bots that remember prior steps in a troubleshooting sequence and adapt answers based on that memory

On the AI agent sophistication ladder, model-based agents are at Level 2. They represent the leap from reactions to true reasoning because they make informed predictions about what will happen next.

3. Goal-based agents

If model-based agents think about “what’s happening,” goal-based agents think about “what should happen.” The agent evaluates different possible actions based on whether they move it closer to the desired outcome (the goal).

They simulate potential action sequences, weigh trade-offs, and pick the one most likely to achieve their goal. This might mean changing strategies midstream when obstacles appear or when a faster, cheaper path reveals itself. 

Unlike reflex-driven systems, these agents aren’t locked into “if X, then Y.” Instead, they ask, “Of all the moves I could make, which one gets me closer to my target?”

Pretend each type of AI agent is an autonomous delivery drone. 

• A reflex agent might simply avoid obstacles it encounters 
• A model-based agent could anticipate where obstacles might appear based on prior experience 
• A goal-based agent would plan the optimal route across the city to ensure the package arrives on time, rerouting if traffic builds, weather shifts, or no-fly zones change

In the project management world, this translates to agents that actively orchestrate work toward milestones. For instance, an agent managing a sprint backlog could identify dependencies, reprioritize stories mid-cycle, and allocate resources differently if a blocker threatens the overall release date.

Examples

• Self-driving cars are classic goal-based agents. Their explicit goal (reach destination safely and efficiently) drives continuous replanning in response to changing road conditions, traffic, and pedestrian movement.
• Supply chain optimization systems (like those used by Amazon or FedEx) are goal-based agents that continuously plan shipping routes, balance warehouse inventory, and minimize costs by adjusting to delays and demand spikes to achieve their delivery targets.

On the AI agent sophistication ladder, goal-based agents occupy Level 3. They bring a strategic quality to decision making, so they’re less like a traffic light and more like a chess player, always thinking a few moves ahead.

4. Utility-based agents

If goal-based agents are focused on what needs to be done, utility-based agents are obsessed with figuring out what’s worth doing most. These agents take planning a step further by assigning a measurable value to each possible outcome and then choosing the one with the highest payoff. In other words, they optimize goals.

At their core, utility-based agents rely on something called a utility function: a way to score different results based on multiple factors like speed, cost, quality, or risk. This makes them fundamentally different from simple goal-seekers. 

A goal-based agent might aim to “deliver a report by 5 p.m.” A utility-based agent would evaluate whether delivering it by 4 p.m. (but with lower accuracy) is better or worse than delivering it by 6 p.m. (with higher accuracy and fewer errors).

Examples

• Recommendation systems like Netflix or Spotify do not show random content, but rank options based on ​​trending content and your search history to add value, not just complete a task.
• When looking at airline travel, travel sites choose the “best” flights by balancing cost, layovers, and departure times to recommend the itinerary with the best overall value.

Utility-based reasoning represents a major step up in agent sophistication (Level 4) because it mirrors how humans make decisions in the real world: rarely absolute, often a compromise, and always tied to perceived value. For teams, this means less rigidity and more adaptability when circumstances shift.

5. Learning agents

Learning agents are where things start to feel less like automation and more like evolution. Unlike simple-reflex or utility-based agents that follow fixed rules or evaluate trade-offs, learning agents actually improve their performance over time. They adapt to feedback, changing environments, and past outcomes, so they get better at predicting what will work (and avoiding what won’t).

Each time a learning agent acts, it observes the results and asks:

• Did this action get me closer to the desired outcome?
• What patterns can I generalize from this success/failure?
• Should I adjust my internal model to make better decisions next time?

Over thousands (or millions) of iterations, the agent sharpens its decision-making ability. This is how systems like recommendation engines, self-driving cars, or fraud detection tools become more accurate with use.

Examples

• Tesla’s Autopilot or Waymo vehicles rely heavily on learning agents. The system continuously improves as it processes more driving data, refining its ability to predict hazards and make safer decisions.
• Systems like Mastercard’s AI use feedback on flagged transactions to continually reduce false positives while catching new fraud patterns.
• Microsoft’s Copilot and Google’s Bard (now Gemini) improve by observing how users respond to answers, tailoring future recommendations.

Learning agents sit high on the sophistication ladder at Level 5. At this stage, agents move beyond static logic or predefined optimization strategies because they actively learn from experience, feedback, and changing environments.

6. Multi-agent systems (MAS) 

Some problems are simply too big for a single agent. That’s where MAS comes in. Instead of one agent trying to handle every decision and action, MAS involves multiple interacting agents that collaborate, coordinate, or even compete to achieve a shared objective. MAS is considered an advanced AI system, which is a new development in agent technology. 

MAS is characterized by decentralized, peer-to-peer decision making, but the architecture of MAS can take several forms:

• Centralized: A single coordinator delegates and integrates results
• Decentralized: Agents work independently and interact to synchronize outcomes
• Hierarchical: Combines elements of MAS with structured layers of authority
• Holonic: Agents can act as both independent entities and as parts of larger collectives

Each agent in the system has its own knowledge and decision-making capabilities. Together, they negotiate, share information, and divide tasks in ways that allow the system as a whole to solve complex problems. 

This makes MAS especially valuable in environments that are too large or fast-changing for a single agent to manage alone. They’re foundational for building scalable, adaptive agentic workflows.

Real-world example

A good example of MAS architecture in the real world is Microsoft’s Magnetic-One, a multi-agent system comprising five different agents built to solve open-ended web and file-based tasks. 

7. Hierarchical agents

Not every problem can be solved in one sweep. Hierarchical agents are designed to tackle complexity by breaking big, abstract goals into smaller, more manageable sub-goals. Hierarchical agents are technically a form of MAS, but are commonly referred to as an agent because each agent within the hierarchy operates independently.

They’re like project managers for AI workflows. They create a tree-like structure where top-level objectives cascade into ordered steps, each handled by different layers of the hierarchy.

Instead of a single agent handling everything, hierarchical agents distribute responsibility: a high-level controller decides what needs to be done, and sub-agents handle execution details.

This top-down approach has several advantages: 

• Keeps decision making organized
• Prevents agents from getting overwhelmed by too many options at once
• Makes it easier to debug or adjust only part of the system without disrupting the whole system

However, the tradeoff is flexibility — hierarchical agents can be less adaptive in environments where goals change rapidly, since their structure is more rigid than decentralized or peer-to-peer systems like MAS.

Real-world example

Amazon Robotics LLC uses a central controller (job manager) that assigns tasks and routes to several mobile robots (inventory station agent and drive unit agent)  — i.e., a top-down hierarchy agent coordinating lower-level agent behaviors on the floor.

What are hybrid agents?

Hybrid agents are the “both/and” of the AI world. A hybrid agent combines the strengths of several approaches in one system. At the base, it can respond instantly to environmental triggers (reactive). On top of that, it has a deliberative reasoning system that plans toward broader goals (goal-based). Add in learning capabilities, and the agent improves its decisions over time by analyzing past outcomes. 

This blend makes hybrid agents especially powerful in dynamic environments where conditions change quickly, but long-term strategy still matters.

You’ll also see “hybrid” used in another, equally valid way: neuro-symbolic systems that pair machine learning (e.g., neural networks) with symbolic reasoning or search (e.g., a planner). Think of this as hybridizing how the agent thinks as well as how it acts.

Real-world example

While a game domain, Google DeepMind’s AlphaGo is a classic example of a hybrid architecture. Deep neural networks (learning) guide Monte Carlo tree search (planning) to create superhuman play. It’s frequently cited as a modern example of combining learned policies/values with a symbolic search planner.

Types of AI agents based on business processes

Up to this point, we’ve looked at how agents “think” — the decision logic that powers their sophistication. But in the real world, organizations don’t build agents for how they reason; they build them for what they do. 

These agents are best understood like job roles: each one is designed to take on a specific responsibility inside your workflow, whether that’s automating routine tasks or analyzing data.

What’s important to remember is that these roles can exist at any level of sophistication. A simple-reflex agent might automate status updates, while a learning agent could handle predictive resourcing or cross-project risk analysis. 

In this section, we’ll break down the different types of AI agents by how they relate to everyday project and operations work.

8. Conversational agents

Conversational agents specialize in natural language interactions, making them ideal for customer service. They can handle enquiries and complaints and order status checks with human-like responsiveness, powered by natural language processing (NLP). 

In practice, these agents reduce wait times, scale support without adding headcount, and ensure consistent answers across thousands of customer interactions.

9. Sentiment analysis agents

These agents monitor tone and intent in real time, scanning customer chats, emails, or feedback to detect frustration before it escalates. If a client’s message shows rising negativity, the system can trigger alerts, reroute to a live rep, or even adjust the agent’s tone mid-conversation. This keeps small issues from becoming churn risks.

10. Lead qualification agents

Sales teams lean on these agents to automatically score leads using behavioral signals like website visits, content downloads, and email replies, then prioritize those most likely to convert. Instead of sifting through endless contacts, reps start each day with a ranked list of opportunities.

11. Campaign optimization agents

Marketing teams deploy these agents to continuously analyze performance metrics like CTR, conversions, and cost per acquisition. They can automatically reallocate ad spend, A/B test creative, or suggest channel shifts in real time to ensure budgets stretch further and ROI climbs.

12. Task orchestration agents

In project management, these agents dynamically assign and reassign tasks across teams based on bandwidth and deadlines. For example, if a designer frees up earlier than expected, the agent routes the next task to them instantly. This prevents bottlenecks and keeps projects on track.

13. Risk status agents

A risk status agent is another type of AI agent used in project management. It can flag project delays or cost overruns early using historical data and predictive analytics. It applies predictive analytics to project data, flagging potential delays or budget overruns before they happen.

14. Triaging agents

When requests like IT tickets, support queries, or bug reports come pouring in, triaging agents automatically categorize, prioritize, and route them to the right person. This reduces time spent sorting issues and ensures critical problems don’t get buried.

15. Intake agents

These agents streamline the intake of new work by gathering requirements, validating details, and structuring requests before they enter the system. Whether it’s a new project proposal or a customer request, intake agents cut down on incomplete submissions that stall workflows.

16. Recruiting agents

In HR, recruiting agents handle the top of the funnel: scanning resumes, automating scheduling, and even conducting basic Q&A with candidates. This lets recruiters focus on the human side of hiring while the agent clears the repetitive tasks.

17. Employee support agents

HR teams also use agents as internal support hubs. Employees can ask about PTO policy, benefits enrollment, or payroll deadlines, and the agent responds instantly with policy-accurate answers. It’s like having an always-on HR desk without flooding the HR team’s inbox.

18. Expense audit agents

Finance teams use these agents to automatically scan expense reports for policy violations like duplicate submissions, over-limit charges, or missing receipts. They flag anomalies instantly, reducing fraud risk and freeing finance from manual line-item checks.

19. Contract review agents

Legal and compliance teams lean on these agents to scan agreements for risky clauses, nonstandard terms, or missed deadlines. Instead of slogging through dozens of pages, lawyers focus their time where judgment really matters.

20. Inventory optimization agents

In supply chain management, these agents forecast demand fluctuations and automatically adjust purchase orders. For instance, they might scale up inventory for seasonal spikes or dial back to prevent overstock. This results in fewer shortages, less waste, and smoother operations.

21. Shipment tracking agents

Logistics teams rely on these agents to provide live ETA updates and reroute shipments when delays occur. Customers get transparency, and businesses keep goods moving even when disruptions hit.

22. Data enrichment agents

Data agents pull from multiple internal and external systems to validate records, fill gaps, and ensure accuracy. Sales and marketing teams, for example, get complete and up-to-date customer profiles without the grunt work of manual data entry.

Ready to deploy AI agents?

The AI agent frenzy isn’t stopping anytime soon. Research from LangChain found 78% of companies plan to implement agents into production soon, but the goal shouldn’t be “AI everywhere.” Building AI agents seems like the next big thing, so the key is to start small.

Pick one high-impact workflow, like intake, triage, or risk surfacing, and put an agent to work. Wrap it with guardrails and turn on a few key performance elements (lead time, review load, etc.) to see if it works.

When you’re ready to integrate agents into the tools your teams already use, connect through the Wrike MCP Server. Pick a workflow, apply the right architecture, and let the results speak for themselves.