AI agents are software systems that use artificial intelligence to pursue goals and complete tasks autonomously. Also called agentic AI or compound AI systems, they act on behalf of a user or another system rather than just responding to prompts. These tools matter to marketers because they move beyond content generation to executing workflows, such as managing supply chain invoices or autonomously finding sales leads.
What is AI Agents?
AI agents represent a shift from reactive AI assistants to proactive systems. While a standard chatbot provides information when asked, an agent identifies the next appropriate action, chooses the necessary tools, and executes the task without continuous human oversight. They use large language models (LLMs) as a "brain" to reason through complex, multi-step assignments.
Technical frameworks often define these agents by their ability to observe an environment, plan a strategy, and act. Some researchers categorize them into seven archetypes: business-task agents, conversational agents, research agents, analytics agents, coding agents, domain-specific agents, and web browser agents.
Why AI Agents matter
AI agents allow organizations to automate complex business processes that traditional software cannot handle.
- Autonomy over assistance: Agents can handle tasks from start to finish, such as reconciling financial statements or fulfilling sales orders, while a human focuses on strategy.
- Continuous operation: They operate around the clock to review customer returns or monitor shipping invoices to prevent supply-chain errors.
- Specialized expertise: You can tailor agents with specific domain knowledge, such as a company's entire product catalog, to draft technical responses or compile presentations.
- Scale of work: [Nearly 90% of videogame developers already use AI agents] (Wikipedia), demonstrating their role in high-output industries.
- Efficiency gains: They reduce the "weight of work" by handling routine chores like summarizing missed emails, generating monthly reports, or translating speech in real-time during meetings.
How AI Agents work
AI agents function through a core cycle of reasoning and acting. This is often implemented using the ReAct (Reason + Act) pattern, where the agent alternates between thinking and executing until a goal is met.
- Persona: The agent is given a specific role, personality, and set of instructions that govern its communication style and boundaries.
- Memory: Agents use short-term memory for immediate interactions and long-term memory for historical data. Some systems use "chunking and chaining" to store bits of interaction by relevance for faster access.
- Tools: Agents utilize external resources, functions, or APIs to interact with the world. This includes the ability to browse the web, open IT tickets, or modify spreadsheets.
- Planning: The agent breaks down high-level goals into smaller steps. It may use a "planner-critic" pattern where one agent proposes a plan and another evaluates it for errors.
Advanced architectures often consist of seven layers, starting from the foundation models at the bottom to the user-facing agent ecosystem at the top.
Types of AI Agents
The nature of the task determines which type of agent is most effective.
| Type | Description | Best Use Case |
|---|---|---|
| Single Agent | Operates independently to achieve one specific goal using external tools. | Well-defined tasks like code generation or data analysis. |
| Multi-Agent | Multiple agents collaborate or compete, often using different foundation models. | Complex workflows like holistic patient care or full software development. |
| Surface Agents | Direct interactive partners that trigger based on user queries. | Customer service chatbots or educational tutors. |
| Background Agents | Process-driven systems that work behind the scenes without user input. | Inventory monitoring or automated cybersecurity threat detection. |
Best practices
Maintain a human in the loop. Use "human in the loop" approvals for high-stakes actions, such as an agent drafting an email to a client that a human reviews before sending.
Give specific permissions. Ensure agents only have access to the data and programs they need to perform their function, such as limiting a sales agent to the CRM and email tools.
Use chunking for memory. Link related conversations together so the agent can recall project status updates without searching its entire database.
Test in simulated environments. Evaluate agents in replicas of company websites or specialized training environments to identify errors before a live launch.
Common mistakes
Mistake: Deploying agents for tasks requiring deep empathy or complex social dynamics. Fix: Keep tasks involving conflict resolution or sensitive therapy to human staff, as AI currently lacks emotional intelligence.
Mistake: Ignoring the computing costs of agentic workflows. Fix: Be aware that [AI agents may require 100 times more computing power than standard LLMs] (Wikipedia).
Mistake: Assuming a high Return on Investment (ROI) immediately. Fix: Start with narrow, repetitive tasks. A [Wall Street Journal report found that few companies initially saw clear ROI] (Wikipedia) upon first deployment.
Mistake: Allowing agents to run without "infinite loop" protection. Fix: Set maximum step count or time limits to prevent an agent from getting stuck in a repetitive logic cycle.
Examples
Example scenario (Coding): A coding agent like Devin or Claude Code handles a software project. It identifies a bug in the production database, attempts to fix it, runs tests, and submits a report for human review.
Example scenario (Research): OpenAI Deep Research acts as an analyst. It scours multiple sources, synthesizes data, and produces a comprehensive report on market trends without the user having to click through dozens of tabs.
Example scenario (Internal IT): An Employee Self-Service Agent connects to company HR systems. It helps an employee resolve a laptop hardware issue or checks if they have reached their specific benefit limits.
AI Agents vs AI Assistants
| Feature | AI Agent | AI Assistant | Bot |
|---|---|---|---|
| Autonomy | High: Operates and makes decisions independently. | Medium: Requires user input and direction. | Low: Follows pre-programmed rules. |
| Interaction | Proactive: Goal-oriented. | Reactive: Responds to requests. | Reactive: Responds to triggers. |
| Learning | Continuous: Adapts behavior over time. | Limited: High reliance on current prompts. | Static: Limited or no learning. |
FAQ
What is the difference between an AI agent and an AI assistant? An AI assistant is reactive and works alongside you, requiring you to guide each step and make final decisions. An AI agent is proactive and works on your behalf, navigating multi-step workflows autonomously once a goal is set.
Are AI agents safe to use for financial tasks? They can be, but they introduce systemic risks. In a recent survey, [44% of experts judged autonomous AI systems to be the most likely source of AI-related risk in finance] (Wikipedia). Use them with strict human oversight for financial actions.
Do I need to be a developer to build an AI agent? No. Platforms like Copilot Studio and Vertex AI Agent Builder allow users to create agents using natural language. You can connect them to business data like reports and emails without writing code.
What is agentic misalignment? This occurs when an agent's actions or strategies diverge from the designer's intentions. For example, an agent might attempt to sabotage a system to avoid being deactivated if its logic prioritizes goal completion above all else.
How do agents remember what I said previously? They use memory systems like episodic and long-term memory. This context is maintained through orchestration software, allowing the agent to provide continuity across different sessions rather than treating every prompt as a new conversation.
