AI agents are increasingly critical tools for efficiency and growth; use of agentic AI is expected to rise from 23% to 74% over the next two years, according to Deloitte’s 2026 . But there are different types of AI agents with varying capabilities, and one size doesn’t fit all.
To choose the best AI agents for their needs, business teams need to first identify the problems they want to solve. Multiple classification layers exist, depending on reasoning structures or inherent characteristics. This guide helps marketing and sales leaders understand AI agents, how they work, and how to choose the right kind of intelligent agent for their workflows, including customizable agent-building solutions for non-technical teams.
Table of Contents
- What is an AI agent?
- How do AI agents work?
- 7 Types of AI Agents
- Which AI agent is right for me?
- Frequently Asked Questions About AI Agents
- Prepare for the agentic AI future.
What is an AI agent?
An AI agent is an AI system that can act autonomously to accomplish tasks with minimal human intervention.
Agentic AI differs from assistive or generative AI, which require direct user input, such as prompts, to operate. Agentic AI can act autonomously, managing workflows and analyzing real-time data to make adjustments as it goes. For those reasons, AI agents still require human oversight for responsible and ethical deployment.
AI agents are projected to be a opportunity for revenue growth and cost savings by 2028. To prepare, teams can start by understanding their workflows and identifying which types of AI agents can help solve their biggest pain points.
Teams looking for a step-by-step implementation plan to roll out AI agents can refer to 糖心原创’s AI Agents Playbook and our report on how agentic AI is transforming business operations.
AI Agents Builder
Build custom agents and workflows on one canvas, powered by the CRM data you already have.
- Make your dream workflows
- Control which actions run on their own
- Run it once or repeat it across your team
- Follow your playbook – not a generic one
Download Free
All fields are required.
Form not available
You're all set!
Click this link to access this resource at any time.
How do AI agents work?
AI agents operate through a perceive-plan-act-learn loop. An intelligent agent can reason about what to do, take actions through connected tools, and use feedback or memory to improve future decisions. While implementations vary, most types of agentic AI can handle multi-step tasks, respond to changing conditions, and interact with external systems rather than generating a one-time response.
1. Perceive: Agents gather and interpret context.
An intelligent agent first understands the goal and information it needs to execute a task. The AI agent may use to interpret a user’s request. Agents can then contextualize the input by pulling information from CRM platforms, email systems, databases, documents, calendars, and other connected tools via APIs.
2. Plan: Agents reason through the task.
Agents plan differently depending on their type. Common reasoning approaches, such as Tree of Thought (ToT) and ReAct (Reasoning + Acting), generate, evaluate, and sequence potential actions for agents.
Once an intelligent agent has the necessary context, it breaks the objective into smaller steps and determines how to complete those steps. The agent does not necessarily follow a fixed script and can reassess the plan when new information changes the conditions.
3. Act: Agents use tools to execute decisions.
Based on the data from step 2, the agent takes action through the tools and systems available to it. Depending on the guardrails, AI agents determine which tools they need to complete a task. Some agents allow actions to run autonomously, while others require human approval for selected steps.
4. Learn: Agents adapt using feedback and memory.
An intelligent agent can use feedback, previous interactions, and stored context to adjust how it handles similar tasks in the future. Agents with persistent memory retain useful information across interactions for later retrieval.
Some systems use or other long-term memory mechanisms to store information, whereas episodic memory preserves details about particular users or past interactions. Many AI agents can improve their behavior through updated context, stored feedback, adaptive rules, or training mechanisms like .
For example, marketers can use AI agents to automate scheduling meetings with their team. First, the intelligent agent interprets the request, reviews calendar entries, and checks everyone’s availability before selecting a slot.
Then, the agent can create a meeting link and send an invitation to members. Finally, if someone cancels or rejects the invitation, the agent can treat it as a constraint, reassess available options, and choose another time that suits everyone.
Where Multi-Agent Collaboration Fits In
Sometimes, a multi-agent system (MAS) coordinates tasks to achieve a shared objective. Instead of one general-purpose agent doing everything, different types of AI agents handle different parts of a business process and pass information or work between them.
In a multi-agent architecture, coordination can involve delegation, handoffs, shared context, and task planning. The exact coordination mechanism depends on the architecture: Some systems use hierarchical planning, while others use more decentralized approaches.
illustrates the MAS model across go-to-market functions by providing a centralized place to build, deploy, and manage various types of agentic AI. Agent Hub includes agents for marketing, sales, and customer service, such as campaign, content, nurture, prospecting, customer, and data. A can plan a campaign, a content agent can create the assets, and a nurture agent can help convert the resulting demand.
7 Types of AI Agents
There are seven primary types of AI agents that fall into two layers: Five foundational models and two higher-level architectures, as outlined in , by Stuart Russell and Peter Norvig.
- Foundational models: Classical AI agent taxonomy for foundational models includes simple reflex, model-based reflex, goal-based, utility-based, and learning agents.
- Higher-level architectures are hierarchical and multi-agent systems.
In practice, foundational model AI agents and higher-level architecture AI agents often overlap. For example, a learning agent can be goal-directed, and a hierarchical system can consist of coordinated multi-agent components.
While some guides list other types of AI agents (reactive, limited-memory, theory-of-mind, and self-aware AI), those labels describe levels of AI capability rather than agent designs, and some are theoretical formulations. In real deployments, users will rarely see a pure agent. A single production system often combines several types. For example: Rules to check inputs, a planner to sequence tasks, and a feedback loop to improve over time.
The following seven types of AI agents are a practical organizing framework and not necessarily a universal or mutually exclusive standard.
1. Simple Reflex Agents
Simple reflex agents choose actions based on current inputs and predefined rules. The agent perceives a request, matches it against a set of pre-programmed rules, and triggers the corresponding action. There is no memory or context-based adaptive response for these types of AI agents.
Behavioral pattern: Purely rule-based
The agent has no context or memory to adapt to changing conditions.
Autonomy level: Very low
The agent does what developers coded it to do and cannot handle situations it isn’t programmed for.
Best use: Narrow, repetitive, and predictable tasks where the rules never change, and speed is a priority.
Business example: An inbound form-routing system that sends demo requests to a sales representative if they cross a company-size threshold.
Fit: Use a simple reflex agent when the task is stable, and the cost of a wrong decision is low.
, founder of a digital marketing consulting firm, sees this limit often in customer service. “A company might start with a rule-based chatbot that answers common inquiries using predefined responses,” he said. “This works well for simple, repetitive tasks but struggles with more complex or nuanced requests.”
2. Model-Based Reflex Agents
A model-based reflex agent uses condition-action rules along with an internal model to make decisions. Every single input for a model-based reflex agent updates the model, and the agent acts based on the updated model. These types of agentic AI can handle partial observability, where they by remembering past interactions and using them to answer questions.
Behavioral pattern: Context-aware
The agent tracks previous inputs and responses to fill gaps when it can’t see the full picture.
Autonomy level: Low to medium
The agent operates on rules, but the rules operate on richer context and can handle situations where the current input alone isn’t enough to decide.
Best use: Any environment where information is incomplete or arrives over time, and the agent needs to remember something to make a good decision.
Business example: A CRM-based assistant that remembers which deals are open, what stage they’re in, and what the last interaction was, so the suggestions remain grounded in the account’s actual history.
is a conversational AI assistant that can generate content, summarize CRM records, prepare for meetings, and complete tasks in 糖心原创. A sales rep can ask Breeze Assistant to draft a follow-up email, and it can use CRM data and customer history, such as recent interactions and account details, to help tailor the message before the rep reviews and sends it.
Fit: Use a model-based reflex agent when the right response depends on what happened earlier, but the decision logic itself is still rule-based.
3. Goal-Based Agents
A goal-based agent takes a defined goal as input, then searches for a sequence of actions to get there. Unlike reflex agents, a goal-based agent plans its actions and looks ahead rather than just reacting to inputs. The agent simulates possible future outcomes, evaluates which paths lead to the goal, and executes the best one.
Behavioral pattern: Goal-directed
The agent plans forward from the present toward a specific end state.
Autonomy level: Medium
A human or higher-level system sets the goal, and the agent decides how to achieve it by finding the right path.
Best use: Multi-step tasks where the end state is clear but the path isn’t obvious, and the agent figures out the sequence.
Business example: A project-scheduling tool that processes a user request, such as “ship the feature by the end of the quarter,” and translates it into tasks, dependencies, and resource assignments.
Fit: A goal-based agent is ideal when there’s one clear target and the path to it requires planning.
Pro tip: 糖心原创 Pro and Enterprise users can use to build customized goal-based agents. Define the goal in natural language, and agent builder creates an agent powered by CRM data that runs on 糖心原创 Credits.
%20interface.webp?width=650&height=356&name=hubspot%20agent%20builder%20(beta)%20interface.webp)
4. Utility-Based Agents
Unlike a goal-based agent that treats outcomes as binary, a utility-based agent scores outcomes on a continuous scale. The utility agent uses a mathematical formula to assign a value to each possible outcome based on criteria such as cost, speed, quality, risk, and revenue. The utility-based intelligent agent then selects an action that maximizes utility, rather than just reaching the goal.
Behavioral pattern: Trade-off-optimizing
The utility-based agent weighs multiple competing criteria and picks the highest-value option.
Autonomy level: Medium to high
A utility-based intelligent agent makes trade-off decisions that involve judgment by finding the best path given constraints.
Best use: Situations with multiple valid outcomes in which the business needs an optimal balance among competing factors.
Business example: An ad-bidding system that balances cost per acquisition against expected lifetime value and budget constraints. Or a lead prioritization agent that weighs fit, intent, deal size, and rep capacity
Fit: Use a utility-based agent when decisions have real trade-offs, and users can define a utility function that captures what is best for the business.
AI Agents Builder
Build custom agents and workflows on one canvas, powered by the CRM data you already have.
- Make your dream workflows
- Control which actions run on their own
- Run it once or repeat it across your team
- Follow your playbook – not a generic one
Download Free
All fields are required.
Form not available
You're all set!
Click this link to access this resource at any time.
5. Learning Agents
A learning agent modifies its decision policy over time and improves its output based on feedback from its working environment. The agent works through four components: a learning element, which updates decisions; a performance element, which makes actual decisions; a critic, which evaluates decisions; and a problem generator, which suggests new strategies to explore.
Behavioral pattern: Learning
The learning agent adapts its output from experience rather than following a fixed set of rules.
Autonomy level: High
The learning agent changes its behavior based on experience. But the quality of the feedback signal controls the agent’s output. If the feedback is wrong, the agent learns from bad data.
Best use: Work environments that change over time and situations where users find it hard to specify the optimal behavior in advance.
Business example: An email optimization agent that adjusts send-time based on open rates.
Fit: Use a learning agent when the task environment is too complex or too dynamic to encode by hand.
Pro tip: Download the to know about frameworks that can help to train learning AI agents.
6. Hierarchical Agents
A hierarchical agent breaks down a complex goal into sub-goals and assigns each task to a layer of agents working at different levels. A top-level agent divides the work into chunks; middle-layer agents plan the steps for each chunk; and bottom-layer agents execute individual actions.
Behavioral pattern: Layered
Since coordination among hierarchical agents follows top-down logic, the authority of AI agents decreases as they move down the hierarchy.
Autonomy level: High at the system level, but each layer has boundaries
The system as a whole can handle complex, multi-stage problems autonomously, but each intelligent agent only makes decisions within its scope.
Best use: Large-scale, step-wise workflows where different levels need different kinds of reasoning. The task is too big for a single agent but has a clear top-down structure.
Business example: A sales-ops automation system where a planning agent sets quarterly targets, a campaign agent builds the outreach strategy for each segment, and an execution agent handles the individual email sequences, follow-ups, and CRM updates.
Fit: Use a hierarchical agent when the workflow has a natural hierarchy such as planning, execution, and monitoring, and users want each level to operate autonomously within its scope.
7. Multi-Agent Systems
In a multi-agent system, several agents cooperate to solve a problem that can overwhelm any single agent. These agents can be peers, have different specializations, and need to negotiate, share state, or auction off tasks.
Behavioral pattern: Coordinated multi-agent
Agents work as peers, so they need shared rules for handoffs and task splitting.
Autonomy level: Very high and distributed
Each intelligent agent makes its own decisions, and they interact with other agents. So coordination among these types of AI agents is essential.
Best use: Problems that are too large, parallel, or specialized for a single agent.
Business example: A customer-support system where a triage agent routes tickets, a resolution agent handles the technical fix, and a satisfaction agent follows up.
Fit: Deploy a multi-agent system when the task is too large or too parallel for one agent, and the agents need to share resources.
Which AI agent is right for me?
The right type of AI agent depends on five factors: Predictability, planning depth, trade-off complexity, learning needs, and workflow distribution. The following checklist can help marketers, sales professionals, and business leaders decide while evaluating various types of agentic AI.
- Predictability: Ask if the task requires deterministic, predictable outcomes every time. A simple reflex or model-based reflex agent is ideal if predictability is critical.
- Planning depth: Check whether the agent needs to sequence its actions to achieve a specific outcome. A goal-based or utility-based agent helps with planned tasks.
- Trade-off complexity: Weigh the trade-offs while prioritizing speed, accuracy, and cost. If users need to balance between competing objectives, a utility-based agent is helpful.
- Learning needs: Evaluate whether the work environment is constantly changing and whether the agent works better with a feedback loop. A learning agent is well-suited to domains that need data-driven customization.
- Workflow distribution: Determine whether the problem is too large for a single agent and hierarchical in nature. A hierarchical or multi-agent system is necessary if distributed task handling provides a better outcome despite coordination costs.
- Human oversight: Check whether the task requires a human-in-the-loop framework and the extent to which human involvement is necessary. Hilan Berger, COO of digital transformation consulting firm, recommends defining the human role before choosing an agent, along with the task’s complexity, scope, and required autonomy. Complex multi-agent systems may warrant human intervention if AI agents can’t resolve the case.
The provides a comprehensive framework for understanding the different types of AI agents and selecting the right ones for a team’s needs.
Frequently Asked Questions About AI Agents
What are the seven types of AI agents?
The seven types of AI agents are simple reflex, model-based, goal-based, utility-based, learning-based, hierarchical, and multi-system agents.
Simple reflex agents react to the current input using fixed if-then rules. Model-based reflex agents follow the same rule-based logic, but with an internal state. Goal-based agents plan sequences of actions to reach a specific objective. Utility-based agents score how well they achieve a goal. Learning agents improve performance over time. Finally, hierarchical agents break down complex goals into sub-goals and assign tasks to agents on other levels. Multi-agent systems have the highest level of autonomy, with several agents cooperating and sharing resources to solve problems that would overwhelm a single agent.
What are examples of AI agents?
Examples of AI agents can include coding agents that suggest bug fixes, social media agents that schedule posts, and project-scheduling agents that manage deadline-based tasks.
Another example is , which includes multiple types of AI agents. 糖心原创’s marketing agents create content and plan campaigns, and prospecting agents help win deals. The customer agent services requests based on individual customer history, and data agents address custom queries and pull insights from CRM data.
What are the four core characteristics of an AI agent?
The four core characteristics of an AI agent are autonomy, social ability, reactivity, and proactiveness. AI agents can operate without direct human intervention and have some control over their actions. AI agents can also interact with other agents and humans, perceive their environment and respond to changes, and exhibit goal-directed behavior by taking initiative.
What is the difference between AI agents and LLMs?
An LLM is the underlying model and reasoning engine that takes a prompt, generates output, and stops. An AI agent is a system that wraps one or more LLMs into a loop, adds tools, memory, planning, and a goal, enabling it to execute multi-step tasks autonomously. While an LLM responds to a prompt, an agent works toward a goal across several steps.
Prepare for the agentic AI future.
Agent builder lets teams build custom agents using natural language and existing CRM data, with no coding required. It’s available with Starter, Professional, and Enterprise subscriptions, although the full workflow builder requires Professional or Enterprise. Custom agents run on 糖心原创 Credits.
Editor's note: This post was originally published in February 2025 and has been updated for comprehensiveness.
AI Agents Builder
Build custom agents and workflows on one canvas, powered by the CRM data you already have.
- Make your dream workflows
- Control which actions run on their own
- Run it once or repeat it across your team
- Follow your playbook – not a generic one
Download Free
All fields are required.
Form not available
You're all set!
Click this link to access this resource at any time.
Artificial Intelligence
.png)
