What AI Agents Are and How Marketers Use Them

What an AI agent is

An AI agent is software that uses a large language model to work toward a goal by deciding its own next steps and using tools to take them. Given a task such as "research these ten companies and draft a first email to each", an agent can search the web, read pages, look up records in your CRM, write drafts and check its own work, choosing the order as it goes.

Anthropic's engineering guide draws a useful line between two kinds of systems. In workflows, language models and tools are orchestrated through predefined code paths: the steps are fixed and the model fills in parts of them. In agents, the model dynamically directs its own process and tool use. Most products sold as agents today sit somewhere between the two, with a model making decisions inside guardrails a vendor or your team has set.

The practical difference is predictability. A fixed workflow does the same steps every time. An agent can handle open-ended tasks where you can't predict the number of steps, at the cost of more variation in what it does.

Agents, chatbots and automations compared

A chatbot answers questions in a conversation. A traditional chatbot follows scripted flows; one built on a language model answers more freely but usually doesn't take actions. An AI assistant helps a person inside a tool, drafting text or summarizing a record when asked. An automation runs a fixed sequence when something happens, such as adding a new form lead to the CRM and sending a welcome email.

An agent combines parts of all three: it can hold a conversation, use tools and take actions, and it decides the steps itself. That's what makes it useful for messy tasks and what makes it riskier to deploy.

Anthropic's guide recommends finding the simplest solution that works and adding complexity only when needed. Many marketing tasks are better served by a fixed automation with an AI step, which tools such as Zapier and n8n handle well, than by a fully autonomous agent.

Where marketers use agents today

Customer service is the most established use. Intercom's Fin AI Agent answers support questions from your help content and is priced from $0.99 per outcome. Tidio sells its Lyro AI Agent from $32.50 a month for 50 or more AI conversations, and HubSpot offers a Customer Agent that resolves support tickets and answers questions.

Prospecting and research come next. HubSpot's Prospecting Agent researches accounts, identifies contacts and drafts outreach, and Clay combines data enrichment with AI research for outbound teams. Content and campaign work is a third area: Jasper includes agents for core marketing workflows on its Pro plan, and its Business plan adds a no-code builder for custom agents. Reporting is a fourth. NinjaCat, for example, uses AI agents for reporting and analysis across many client accounts.

General automation platforms let you build your own. Zapier Agents can work across the apps Zapier connects, and n8n combines AI steps with its workflow automation, self-hosted or in the cloud.

How AI agents are priced

Agent pricing rarely follows one model, so work out what a unit of work costs in each.

Some vendors charge per seat. Jasper's Pro plan is $69 per seat per month on monthly billing or $59 on annual billing, with a 7-day free trial. Some charge per result: Intercom's Fin is priced from $0.99 per outcome on top of seat plans that start at $29 per seat per month on annual billing. Others meter activity. Zapier Agents include 400 activities a month on the free plan and 1,500 on Pro, where each trigger, app action, search or message counts as one activity; Zapier's Professional plan starts at $19.99 a month on annual billing. HubSpot bills custom agents built in its agent builder in HubSpot Credits. n8n's cloud Starter plan is €20 a month on annual billing for 2,500 workflow executions, and its self-hosted Community Edition is free.

To compare, estimate a month of real volume, such as 800 support conversations or 200 researched prospects, and price that volume in each model. Per-outcome and per-activity pricing can be cheaper at low volume and expensive at scale, or the reverse.

Risks to control before you connect an agent

Agents act on your accounts, so a mistake can be a sent email or a changed record instead of a bad paragraph. Anthropic's guide notes that an agent's autonomy means higher costs and the potential for compounding errors, and recommends extensive testing in sandboxed environments with appropriate guardrails.

Prompt injection is the security risk to understand. OWASP describes it as user prompts or external content altering a model's behavior in unintended ways. An agent that reads web pages, emails or documents can meet hidden instructions in them. OWASP's mitigations translate directly into marketing setups: give the agent the least access it needs, require human approval for high-risk actions such as sending emails, spending budget or changing records, and keep untrusted external content clearly separated.

The rules on marketing content still apply when an agent writes it. The FTC's rule on fake reviews and testimonials, announced in August 2024, covers reviews that misrepresent that they come from someone who doesn't exist, such as AI-generated fake reviews. Don't let an agent post reviews, testimonials or social proof on anyone's behalf, and keep a person responsible for what goes out under your brand.

How to start with one agent

Pick one task that is repetitive, bounded and easy to check. Good first candidates are answering common support questions from an approved help center, researching accounts before a sales call, summarizing weekly campaign results, or drafting first versions of routine content for an editor to review.

Measure the task before you automate it: how long it takes now, how often it's done and what a good result looks like. Run the agent in a test environment or on a small share of real volume, with a person reviewing every output at first. Track accuracy, time saved and cost per task for a few weeks, then widen access one permission at a time.

Write down what the agent may and may not do, who reviews its work and how to switch it off. Those three lines of policy are worth more than any feature comparison.

How we list AI agent tools

Our AI marketing tools category includes an AI agents subcategory next to AI social media and AI advertising tools, ordered by our Overall Score, half Editor Rating and half Market Signal, the 0-100 score we build from Ahrefs and Semrush data. Across the whole category, Writesonic has the highest Market Signal on the data through September 2026, followed by The Brief and Positive Surfer. Agent features also appear in tools we list under customer support, automation and sales.

Market Signal measures each brand's footprint in search. It says nothing about how well an agent handles your data, your edge cases or your approval rules, which only a small, supervised test can show.