Introduction to AI agents for content professionals: tools, use cases, first steps

The AI landscape is going through a big shift: New tools like Claude Cowork, ChatGPT Work, and others promise to take over everyday work. They go well beyond just generating text or images: They are designed as digital assistants that actively support you and handle complex tasks independently. At least, this is their promise.

But how do they work, what can they do today, and how can they be helpful for content professionals? These are some of the questions I want to answer with the overview on this page.

Additionally, I look behind the hype surrounding this topic. Many AI providers see agents as the next big step. However, reality often collides with their promises.

What are AI agents?

At its core, an AI agent is a system that pursues a given goal largely independently. This sets it apart from the chatbots you already know, because they specialize in answering direct requests. That means they usually do not become active without a specific prompt, don’t do more than one task at a time, and won’t come up with a plan on their own containing several steps to reach a given goal.

Agents on the other hand use their artificial intelligence to create plans, make decisions, and adjust their work steps. They can also run in the background over a longer period or wait for results.

Another important factor is their access to external tools: AI agents can search the internet, read local documents, control computer programs, or communicate with other software via interfaces. They do not remain isolated in a chat window. They perform concrete actions in your digital workspace. And while some chatbots also have some of these capabilities, they are still limited to reacting to one input at a time. And that makes a big difference.

In a nutshell: While a chatbot primarily reacts, an agent is designed to act.

Cloud or local: where agents work

Not all agents are alike. One key difference is where these systems operate. It determines what they can do and how much effort it takes to use them.

The first type is pure cloud agents. Examples include Manus and Google’s Gemini Spark. The big advantage is ease of use: you don’t have to install any software, you simply give the agent its task in your browser. After that, it can work asynchronously. You can close your laptop while the system searches the web for hours or prepares data in the background.

The second type is local agents. Programs like Claude Cowork, ChatGPT Work or Perplexity Personal Computer run directly on your device. These systems can use the tools you already have installed, read files you give them access to, and carry out tasks directly in the operating system.

Local agents do come with a different kind of security risk, though: to be truly useful, they often need access to your hard drive and local files. If they make a mistake, they can change or delete important data on your computer.

Tip: Learn how to avoid nine typical risks when working with AI agents.

For data protection, by the way, this makes no difference. Even if you use ChatGPT, Claude, Gemini and others through a locally installed app, all your information is still sent to the cloud and processed there. It’s only different for tools that use an AI running directly on your computer or on a server you manage yourself.

The line between the two types is blurring. Claude Cowork, for example, started out as a local agent but can now also run in the cloud (on the web and in the mobile app). ChatGPT Work is the same: in the desktop app, it can access your files and programs, while on the web and on your phone it runs in the cloud. In this overview, I sort agents by what makes them special. For these two, that’s the ability to work on your own computer, because it opens up uses that a pure cloud agent can’t cover. That they can also work in the cloud is more of a bonus.

Top AI agents for content professionals

Fair warning: Many providers use terms like “AI agent” or “agentic AI” very generously for their products (to put it mildly). To make it easier to understand, I divide these services into three categories based on where and how they operate. In addition, I’ll give you concrete examples for each.

1. The desktop manipulators (local & OS agents)

“Desktop manipulators” operate a computer similar to how you do it: They navigate through user interfaces, organize folders, and process local files. They are useful when tasks require moving data between different local programs.

They are ideal for professionals when dealing with downloaded assets, raw transcripts, disjointed local drafts, and other similar use cases.

Claude Cowork (Anthropic): Claude Cowork is a desktop application for macOS and Windows. Access via mobile and web is now available in beta, but with a reduced featureset: The desktop app remains the fullest version.

Anthropic designed Cowork to bring agent capabilities to non-technical knowledge workers. The system operates directly where your work already happens. It accesses local files and everyday applications.

It also has its own built-in browser: Cowork can open websites, read pages, click controls, and enter text, for example to fill out web forms or pull figures from dashboards that don’t offer an API. This browser is separate from your everyday one, so Claude cannot see your open tabs, bookmarks, or saved passwords. You manage its access through three levels of permissions: It always asks for your approval, it only asks for risky tasks, it never asks.

This risk is real: Venture capitalist Nick Davidov posted about his attempt to use Claude Cowork to organize his wife’s messy computer desktop. The agent misinterpreted the vague prompt, executed a harsh deletion script, and accidentally erased 15 years of family photos. While any code it writes to process data runs in an isolated virtual machine to protect your core operating system, it can still wreak havoc on the folders you give it access to.

As a content professional, you can use it to organize messy folders, synthesize interview transcripts, or batch-rename files, provided you monitor it closely. It also connects to platforms like Google Workspace and Canva. One caveat: Complex tasks involving many local files can quickly use up the limits of the standard $20/month Pro subscription tier.

In another article, I explain Claude Cowork in more detail, talk about its features and capabilities, as well as some practical use cases. If you are wondering when to use Cowork and when to stick with a regular chat, this comparison of Chat and Cowork will help.

ChatGPT Work (OpenAI): ChatGPT Work is an agent built directly into ChatGPT, on the web as well as in the desktop app for Mac and Windows. Like Claude Cowork, it has its own version of Skills, Connectors, and Scheduled Tasks. These recurring tasks can also be triggered by events, such as a new Gmail or Slack message.

What makes Work interesting in this category is the desktop app: There, it can also use the files and programs on your computer, as long as you allow it, and it comes with a built-in browser. On the web and on mobile, it runs in the cloud instead. How Work compares to Cowork is something I look at in a separate article.

Work builds on Codex, which had already won many users outside of software development. Codex itself remains OpenAI’s separate tool for actual coding, in the same desktop app.

Keep an eye on your usage: Work draws from the same allowance as Codex, and how quickly it is used up depends on the task.

Newcomers to this market follow the same recipe. Kimi Work, for example, is a desktop app for macOS and Windows that combines local folder access, browser automation, and scheduled tasks.

Apple Intelligence (Siri AI): Apple takes a different approach by weaving its agent directly into the operating system. Siri AI uses on-screen awareness to see and understand what is currently displayed on your screen, and it can answer questions about it or act on it. Apple’s own example: While you look at a sports team’s website, you ask Siri to find all upcoming home games and add them to your calendar. It can also search across your personal data like past emails and messages to retrieve specific details.

Siri AI is now live: Apple released it on September 14 as a beta, together with iOS 27, macOS 27, and the other new versions of its operating systems. The main limitations are hardware, language, and geography. You need a device that supports Apple Intelligence, which means an iPhone 15 Pro or newer or a Mac with an M1 chip or newer. For now, Siri AI only works in English. French, Japanese, Korean, Portuguese, and Spanish are coming in October, German is not on that list. In the EU, Apple does not offer Siri AI on iPhone, iPad, and Apple Watch for the time being. The Mac is not affected, provided the system is set to English. Apple also warns about daily usage limits and plans to charge for expanded access later.

Perplexity Personal Computer: This tool is a hybrid: It acts as a bridge between the cloud and your local machine. It runs as a background application on your Mac or Windows PC. This gives it access to your local files and emails. However, it sends complex tasks to cloud models for processing. This makes it a desktop manipulator, but it relies heavily on external servers. You can use it to organize local folders or synthesize research directly on your device.

2. The cloud researchers (virtual computers)

“Cloud researchers” specialize in gathering and structuring information in the cloud. You provide a complex topic, and the agent independently searches the internet, evaluates sources, reads extensive documents, and summarizes the results in a detailed report. These tools are ideal when you need to familiarize yourself with a new field for example.

Manus: Manus is designed for open-ended web tasks. You give it a goal and it breaks the goal down into subtasks and opens a virtual browser to find the answers. It works well for producing an initial research brief. It offers a free tier of 300 daily credits, making it a good starting point to test AI agents without financial risk. However, you have to watch your usage: a single complex research task can consume between 500 and 900 credits. Because of this, its credit-based pricing can become highly unpredictable and expensive if you rely on it heavily. It also struggles with highly complex tasks and can occasionally lose track of its objective.

Perplexity Computer: This is the cloud-based orchestrator from Perplexity. Its biggest strength is factual accuracy, because it cites the source of every answer and figure. It is also integrated natively into Microsoft 365 applications: You can open Microsoft Word and instruct the agent to draft a document based on live web research. It will write the text and add the citations directly into your file.

ChatGPT Agent: OpenAI has retired its former cloud agent, ChatGPT Agent, in favor of ChatGPT Work (see above).

Tip: If you just want a research agent, you can find such “Deep Research” features in all major platforms, including Claude, Gemini, ChatGPT, and Perplexity. Learn more about Deep Research and its usefulness in Marketing in this article. I’ve also published a comparison of the major Deep Research offerings.

3. The ecosystem integrators (suite agents)

Large software corporations integrate agents directly into their existing office suites. This gives them access to internal company data and communication channels. As an individual content creator, you will rarely purchase these tools yourself, but you might encounter them if your clients use these large platforms.

Notion Agents: For content teams that already run their operations in Notion, this is a very accessible option. Notion recently introduced autonomous agents that can run 24/7 on schedules or triggers. You can instruct them to build a content calendar, draft social media posts, or run multi-step work across hundreds of Notion pages. It is included in their Business tier ($20/user/month), though custom agents require additional credit purchases.

Microsoft Copilot Cowork: This tool executes multi-step workflows directly within the Microsoft 365 environment. It can analyze meeting notes in Teams, send emails through Outlook, and draft a matching presentation in PowerPoint. It is well-suited for corporate environments. However, the pricing model is a major hurdle for independent freelancers: You first need a base Microsoft 365 Copilot license (approx. $30/user/month). After that, usage is billed dynamically on a consumption basis using “Copilot Credits” priced at $0.01 per credit. While a light email draft might cost $1.25, a heavy, multi-tool research task can cost upwards of $25.00 per run. This lack of cost predictability makes it largely impractical without enterprise backing. One more reason why costs are hard to predict: Benchmark scores alone say little about what an agent really costs, because failed attempts and token budgets count as well.

Gemini Spark (Google): Google takes an always-on approach with this agent. Spark is meant to run continuously in the background of Google Workspace. You can set it up to monitor your Gmail inbox, extract client information from new emails, and log the data in a Google Sheet automatically. There is also a Gemini app for Mac in which Spark can work with local files (beta), for example by sorting PDFs from your Downloads folder into designated locations. Spark started out as a US-only product for the expensive Google AI Ultra tier (up to $200 per month). In the meantime, Google has opened it up: It is now part of the standard Google AI Pro plan at about $20 per month and available in most countries. Not in Europe, though: The EEA, Switzerland, and the United Kingdom are still excluded, even if you pay for Ultra.

HubSpot Agent Hub: Marketing platforms are building their own agents, too. HubSpot has opened Agent Hub and Agent Builder in public beta for Professional and Enterprise customers. With Agent Builder, you describe a task in plain language and get a custom agent that can use the customer data HubSpot already holds, such as contact records and deal history. Agent Hub is the dashboard that shows what your agents are doing across marketing, sales, and customer service. If your clients run their marketing in HubSpot, it is worth knowing about.

Optimizely Virtual Teammates: Optimizely has launched Virtual Teammates: agents with a defined role, scoped permissions, and access to your organization’s context that take over recurring marketing work. They run on schedules or events, and you decide when a human has to approve their work. Five roles are available at launch, among them an SEO and AI Search Analyst and a Marketing Analyst.

Claude Tag (Anthropic): Claude Tag puts Claude into Slack as a permanent member of the team. Anyone in a channel can tag @Claude to hand over a task. Claude remembers what was discussed in the channel and can work on projects for hours or days. After an update in August, it reads the full context of a conversation and decides by itself whether to chip in. It launched in beta for Claude Team and Enterprise plans, so you are more likely to meet it in a team setting than to buy it yourself.

What agents can achieve in Content Marketing today

The promises of software providers are enticing. But in the end, only everyday usefulness counts. If we put the hype to the side, four areas in content marketing are currently emerging where AI agents can actually be useful.

In-depth research and briefings: When you need to familiarize yourself with a new topic, it usually takes a lot of time. A cloud research agent like Manus or ChatGPT Work can read dozens of professional articles, studies, and websites. It extracts facts, compares sources, and creates a structured briefing for you. You no longer start your writing work with a blank page. You begin with a solid information foundation. If you use a tool like Perplexity Computer, the agent will also cite its sources. This allows you to verify the numbers, facts, and statements within the briefing directly. As mentioned above: Similar, albeit simpler and less capable tools are Gemini Deep Research, Claude Research, and others.

Content repurposing on autopilot: Ideally, a good piece of content should be used multiple times. AI agents can speed up such repurposing workflows. You can hand over an extensive professional article to the system and the agent independently creates matching posts for LinkedIn, a teaser for the newsletter, and a script for a short video. With the appropriate permissions, an automation agent can deposit these drafts directly into your editorial plan or content management system.

Handling digital drudgery: Compressing and renaming hundreds of images for the web, filling large tables with SEO metadata, or searching old blog posts for dead links are time-consuming tasks. Desktop manipulators like Claude Cowork or ChatGPT Work are a good fit for this kind of repetitive work. They navigate through local folders and programs to work through their list. You gain time for more important things.

Automating recurring reporting: At the end of the month, numbers often have to be gathered from different systems. You need to know how many page views there were, the interaction rate on social media, and how many newsletter subscribers joined. Agents can independently read these data sources. They pull the numbers, calculate changes from the previous month, and bring everything into one document.

Two examples from my own usage:

1. I have a recurring task in Claude Cowork every Monday checking certain tags here on Smart Content Report for new articles. Claude lists these for me, and I can decide whether to turn them into a newsletter. If I give the green light, Claude produces the complete HTML of the email based on a template. I just need to paste this HTML into the backend, adjust the contents if needed, and schedule the newsletter.
2. Another such task runs every Friday. It looks at what we’ve published on UPLOAD Magazin and Smart Content Report that week and what is planned for the upcoming week, then develops content recommendations for our LinkedIn page and other social media profiles.

The reality: limits, risks, and why projects fail

This all sounds great, but AI agents are not flawless workers. When projects involving AI agents fail, it is rarely due to a lack of artificial intelligence. It is mostly due to the environment in which they operate.

One problem is bad data. Agents need high-quality information to function. If you give an agent access to a chaotic folder structure with outdated documents and contradictory information, it will produce chaotic results. An agent cannot magically clean up a fundamental organizational mess.

That means: Before you can successfully deploy an agent to summarize your company knowledge or analyze client data, you must ensure your data is clean, well-structured, and up to date.

Furthermore, new technology also brings new security risks. When AI agents get access to your local files or company systems, this becomes a major concern. The biggest emerging threat is indirect prompt injection: This happens when malicious instructions are hidden inside a seemingly harmless document, email, or website. If your agent reads this document during its research, it might interpret the hidden text as a command. Because the agent has the authority to use tools, it could unknowingly leak sensitive data or alter files based on that hidden command.

This risk takes unique forms depending on the agent. For example, security researchers recently identified vulnerabilities termed “CometJacking” within browser-based agents like Perplexity Comet. Because the agent maintains context across all open tabs, malicious instructions embedded in an untrusted webpage could theoretically hijack the agent to read and exfiltrate sensitive data from an adjacent authenticated tab (like your open CRM or email inbox).

This risk is amplified by “shadow IT.” Individuals often deploy agents on their own to speed up their work without informing their security or IT teams. But if you use agents that connect to your email or file system, you must tightly control what they can access.

Last but not least: The term “autonomous agent” is misleading. In professional environments, full autonomy is rarely the goal. The standard approach is a “Human in the Loop” setup. This means the agent does the time-consuming groundwork, but a human must approve critical actions before they are executed.

You should not let an agent publish a post, send an email to a client, or delete files without a final check. The agent acts like an assistant preparing a draft. You remain the editor who reviews the work, catches hallucinations, and gives the final approval. Human intervention is a necessary part of the workflow.

Tip: Learn how to avoid nine typical risks when working with AI agents.

Jump in now or wait? A practical action plan

With all the promises and current limitations in mind, what is the best approach for content professionals? My recommendation is to take a staged approach based on your current comfort level and subscriptions.

Stage 1: Start with what you have. If you already pay for a $20/month subscription, start there. For example, use ChatGPT Work for research-heavy browser tasks or formatting slide decks. This is a low-risk way to build an intuition for prompting agents without spending extra money.

Stage 2: Commit to a local agent. If you are a freelancer based in the EU dealing with massive amounts of transcripts, messy drafts, and downloaded assets, standardize on a local agent. Claude Cowork is currently the clearest, no-code option available in Europe, with ChatGPT Work as a close alternative. Build out “Skills” for your recurring tasks (like turning a podcast transcript into show notes) and run it on a strictly isolated folder to manage security risks. I personally have come to like it despite some of its current limitations or some trial and error.

Stage 3: Wait on the heavy enterprise tools. Unless you are backed by a corporate IT budget, avoid the complex pricing of Microsoft Copilot Cowork. Hold off on it until credit pricing stabilizes or it becomes bundled into standard subscriptions. Gemini Spark, on the other hand, is no longer a luxury product: It is included in the $20/month Google AI Pro plan. But it is not available in the EEA, Switzerland, and the UK, so it is a non-starter for many of my readers.

My conclusion: AI agents are meant to fulfill the promise of a full-fledged “AI assistant” and they deliver on that today for some tasks and under certain circumstances.

You should not expect to delegate all your drudgery to your artificial assistant just yet. But anyone who learns to safely use these tools today — understanding their quota limits, security risks, and prompt nuances — will have an operational advantage in a few years.