AI
Agentic AI vs Generative AI: A Guide for Technical Writers
Technical writers are used to staying flexible in the face of new technology. Now, with the evolutions of Generative AI and emergence of Agentic AI, how will these technologies transform the roles and work of technical writers? Let's explore the differences and common use cases of each.
Technical writers are no strangers to staying flexible and shifting their approach to content in the face of new technology. Many tech writers have already used ChatGPT, or similar tools, to summarize research, prepare outlines, and brainstorm ideas. It’s no wonder, considering 90% of knowledge workers who used AI reported higher levels of productivity than those who do not. However, AI technology continues to evolve, and if 2024 was the year of Generative AI (GenAI), 2025 marks the rise of Agentic AI.
Agentic AI is still new, yet it’s already expected to drive serious business value. Gartner reports that by 2028, at least 15% of day-to-day work decisions will be made autonomously through Agentic AI, up from 0% in 2024.
This guide clarifies the key differences between Generative AI and Agentic AI, explores their implications for technical writing, and helps documentation teams make informed decisions about which AI capabilities best serve their needs.
Generative AI vs Agentic AI
Before we detail how these evolving technologies will impact the work and roles of technical writers, let’s cover the basics.
Generative AI refers to a category of AI that creates content — text, images, audio, code — by trying to copy human creativity. GenAI applications are powered by large language models (LLMs), a class of deep learning models trained on extensive datasets that enable them to recognize patterns, interpret context, and produce natural language responses to prompts or instructions. The outputs generated by GenAI may be used by human users as well as AI agents to complete tasks toward dedicated objectives.
Agentic AI, on the other hand, is an overarching artificial intelligence system designed to accomplish specific goals with limited human supervision. These independent systems orchestrate AI agents — specialized models that mimic human decision-making — to communicate with various applications. Agentic systems make autonomous decisions and execute defined sub-tasks to achieve high-level goals.
Let’s take a closer look at the comparison table below to explore how these two technologies impact technical documentation teams.
| Generative AI | Agentic AI | |
|---|---|---|
| AI objectives | To generate human-like outputs based on a prompt. | To achieve defined goals autonomously by planning, reasoning, taking actions, and coordinating across multiple tools or systems. |
| How it works | It uses LLMs trained on the vast datasets to predict and produce relevant responses based on input prompts. It operates within a single conversational session. | It employes AI agents to autonomously gather data from tools and environments that the system deems relevant. With the ability to make decisions, the system acts and adjusts as necessary to complete complex, multi-step tasks to achieve a goal. |
| Level of autonomy | GenAI tools have low autonomy and are considered reactive as they require user prompts for each step and new output. | Agentic AI systems have high autonomy and are proactive as they work independently and can plan, act, and adjust without waiting for direct prompts. |
| Role of humans | Humans are writers and editors. They craft prompts, review outputs, and refine content for quality and accuracy. | Humans are more supervisors or collaborators. They define the goals, set parameters, and review AI’s results or decisions. |
| Key Capabilities | – Draft first versions of documentation or articles,– Create visual media: code blocks, product schemas, visual templates, and more,– Summarize technical details,– Translate or localize content. | – Detect API or code changes and update related documentation,– Create and manage content tickets,– Continuously learn from feedback,– Monitor content accuracy and suggest updates. |
How Technical Documentation Teams Use GenAI vs Agentic AI
Technical documentation teams must constantly juggle multiple roles: content writers, editors, knowledge managers, SME coordinators, translators, information architects, governance controllers, compliance checkers, and more. With so many tasks to manage, AI’s potential to eliminate certain tasks and workflows is a game-changer.
13 AI Tools for Technical Writers that aren’t ChatGPT
How exactly does AI remove friction and help technical documentation teams advance on their projects? GenAI and Agentic AI elevate great documentation teams, helping them be even more efficient. Here are the top use cases.
Common Generative AI Use Cases
From reducing translation costs to churning out first drafts in mere seconds, 40% of organizations report achieving their expected efficiency and productivity benefits from GenAI projects. This is a good start, but to maximize the return on investment from this technology, documentation teams should integrate GenAI tools into the following workflows.
Brainstorming Ideas
Documentation teams can prompt GenAI tools to suggest topics, headings, or example scenarios for documentation. This is especially helpful when covering complex products or creating tutorials for new or unfamiliar features.
Summarizing Research and Expert Interviews
GenAI can condense technical notes, meeting transcripts, or interviews with subject matter experts into clear, digestible summaries. This saves time and ensures key insights aren’t lost in lengthy source material.
Preparing Document Outlines
GenAI helps create documentation outlines when writers are creating long, detailed documents (e.g., new reference guides) that go beyond established templates. Here, AI outlines allow writers to quickly refine and verify content structure rather than starting from scratch.
Creating Multimedia Content
Beyond text, GenAI-powered tools generate code snippets, diagrams, images, or even short videos, which teams can embed directly into documentation.
Translating Documents
Technical writers often need to make documentation accessible to a global audience. GenAI can help automatically translate content into multiple languages while maintaining technical accuracy and consistent terminology. This makes content inclusive, accessible, and usable for users around the world.
Optimizing and Marking Up Content
Tools like Acrolinx, Congree UCC, and oXygen Positron AI Assistant use GenAI to check documentation against a company’s writing rules, ensuring clarity, quality, consistent tone, and compliance. This helps teams produce documentation faster while maintaining enterprise-wide standards.
Common Agentic AI Use Cases
Agentic AI moves beyond static content generation by integrating reasoning, planning, and action. In documentation workflows, it can manage entire publishing pipelines. These capabilities make agentic systems a key driver of faster publication cycles, higher productivity, and streamlined operations. The following use cases illustrate where it delivers the greatest impact.
Automating Documentation Workflows
Agentic AI systems can automatically monitor code changes in GitHub or other development environments. When updates are detected, the system extracts relevant information and uses it to draft updated documentation, including code examples or explanations. Once the documentation is ready, the system autonomously sends the draft to SMEs for review and tracks approval progress through Jira or similar project management tools.
Creating User Feedback Loops
Companies can design agentic workflows that continuously monitor support tickets, community discussions, and documentation analytics to identify knowledge gaps. Once a gap is identified, the system employs agents to create documentation improvement tickets in the team’s project management tools, draft content updates in the CCMS, and finally, submit the proposed changes to the relevant teams for review.
Coordinating Release Notes
Agentic systems can orchestrate AI agents to pull release notes from Jira and initiate a release note generation process when new information is retrieved. The system then fetches changelog data from GitHub and compiles in-product screenshots from Figma or other design tools, to generate consolidated release documents. Once prepared with the relevant new information, the system publishes the notes to multiple channels.
The New Role of Content Professionals
The emergence of Generative and Agentic AI is transforming the role of technical writers and content professionals. They are evolving from traditional documentation specialists into knowledge conductors, AI content architects, and, as Stefan Gentz from Adobe said at LavaCon 2025, “content orchestrators”.
These new titles come with new responsibilities that extend beyond writing, editing, and other content operations tasks. Content experts are now uniquely positioned to lead prompt engineering efforts for Generative AI tools and to design Agentic workflows that automate documentation and support processes for both internal teams and end users. Modern content professionals are also taking on roles such as managing AI strategies, consolidating product knowledge into centralized repositories, and feeding this information into front-line applications for user support.
Additionally, collaboration with security and governance teams is now essential to ensure AI-driven content adheres to the latest standards and best practices.
Nowadays, the term “Technical Writer” is being replaced with titles such as “Documentation Engineer” or “Content Strategist.” This change in terminology reflects a broader understanding of the evolving responsibilities involved in managing technical content in the era of AI, and more specifically Agentic AI.
RĂ©mi Bove
Head of Product Knowledge, Fluid Topics
Looking Ahead at Generative and Agentic AI in Technical Writing
Technical writers started asking if they would be replaced by GenAI when the technology was new, and now the next wave of strategic questions is emerging with the rise of Agentic AI. Rather than posing a threat, these technologies represent a significant opportunity to redefine the role and impact of technical writers. By focusing on making content understandable for machines, writers will be able to reap new productivity gains, faster time to publication, improve tone consistency, translate at lower costs, and so much more.
Schedule a free demo of Fluid Topics with a product expert
Agentic vs GenAI FAQs
To harness the potential of these new technologies, technical writers need to develop new skills. For Generative AI, they should refine their prompt engineering, editing, and ability to train data sets with IT. For Agentic AI, technical writers need to understand how to design advanced workflows, collaborate with product and engineering, and write for machines.
