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 Agent, 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. 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.

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

Cloud or local: where agents operate (and why it matters in Europe)

Not every agent is the same. A key distinction is where these systems do their work. This determines what they can do and how complex their deployment is. It also defines their security risks and geographic availability.

The first variant is cloud-based agents. Examples include ChatGPT Agent or Manus. The big advantage is ease of use: You do not have to install any software, you simply give the agent its task via the browser. After that, it can work asynchronously. You can close your laptop while the system might spend hours searching the internet or preparing data in the background.

The obvious disadvantage is data protection: All information and documents you provide to the agent end up on external servers. For content professionals in Europe, this is a major hurdle. Because of strict GDPR data-residency rules and the EU AI Act, many cloud agents are currently restricted to the US. One current example is Google’s Gemini Spark.

The second variant is local agents. Programs like Claude Cowork or Perplexity Personal Computer run directly on your computer. These systems can use existing local tools, read files you give access to, and perform tasks directly in the operating system. Because they run locally, they bypass many of the current EU regulatory delays, making them readily usable in Europe today. This is an advantage regardless of your region’s privacy laws, because you also do not have to worry about external servers leaking your information.

However, local agents bring a different kind of security risk: They might need deep access to your hard drive and local files to be really useful. If they make a mistake, they could alter or delete important data on your computer. Some local systems also require powerful hardware to run smoothly.

Top AI agents for content professionals

The differences between agentic AI tools don’t stop here. One reason: Many providers use this term 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)

These AI tools 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 being added, but with a reduced featureset. 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. 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: Recently, 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 safely organize messy folders, synthesize interview transcripts, or batch-rename files, provided you monitor it closely. It also connects directly to platforms like Google Workspace and Canva. Fair warning: Complex tasks involving large 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.

OpenAI Codex (Knowledge Work Edition): Codex started as a command-line tool for software developers. OpenAI repositioned it in 2026 to include general knowledge workers. It functions as a local command center. To interact with it, you can invoke specific “skills” (such as create-spreadsheet or create-docx) to output heavily formatted deliverables. You can also use its “Goal mode” to automate recurring tasks, commanding the agent to monitor a competitor’s output every Friday and generate a summary report by Monday morning. As an aside: Skills and Scheduled Tasks are also available with Claude Cowork. Codex can also run multiple agents in parallel: One agent can research a topic online while another updates a spreadsheet in the background. It does this without hijacking your active screen cursor.

While powerful for parallel execution, Codex retains a technical feel. For example, it lacks a built-in text editor. If you spot an error in a generated document, you have to open an external program to fix it. This introduces some friction into the writing process.

Update: OpenAI introduced ChatGPT Work in early July. We will exchange Codex for it in a future version of this overview.

Apple Intelligence (Siri AI): Apple takes a different approach by weaving its agent directly into the operating system. Siri AI is supposed to use on-screen awareness to see and comprehend what is currently displayed on your screen. You can then highlight a data table in a PDF and command the agent to summarize it and email it to a colleague. It will also be able to search across your personal data like past emails and messages to retrieve specific details.

The main limitations are hardware and geography. It requires recent Apple chips to run the local models. It also faces ongoing regulatory delays in regions like the European Union. Furthermore, it will only work with English at first and is expected to come out “later this year”.

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 (Windows is coming). 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)

These systems 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.

ChatGPT Agent (OpenAI): This agent operates entirely in the cloud. It uses a visual browser to navigate websites, click buttons, and extract data. You can ask it to analyze several competitor websites for example and it will then run independently. In the end, it compiles the data into a structured document or presentation. It is useful for deep research.

However, it has strict usage limits: The standard ChatGPT Plus subscription ($20/month) is severely capped at just 40 agent messages per month. This is because agentic tasks “think” in loops, consuming 50 to 100 times more data quotas than a normal chat. A few poorly formulated requests can rapidly exhaust your allowance.

It can also get stuck on complex website layouts or authentication screens. When it hits a login gate, you must manually activate its “Take over browser” mode, because the agent intentionally disables its screenshots during password entry to protect your privacy.

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.

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 highly 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.

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. Like the Microsoft offering, it is geared strictly towards enterprise budgets. Access requires a Google AI Ultra subscription, which places it in the $100 to $200 per month tier, far beyond the standard $20 AI tool budget. It is currently also restricted to a limited beta for users in the United States. We will see if, when, and how Google opens up Spark to a wider audience.

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 Agent 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 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. This is great for me, because I don’t have to start from scratch.

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.

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. Use ChatGPT Agent for research-heavy browser tasks or formatting slide decks. Use Perplexity’s Comet Assistant for basic web summaries. These are low-risk ways 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. 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 and the expensive $100+/month Google Ultra tier for Gemini Spark. Hold off on these until credit pricing stabilizes or they become bundled into standard subscriptions.

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.