The Smart Way to Use Agentic AI for Content

Key takeaways 

• Agentic AI works best as your quality-control partner. It's excellent at catching technical errors, narrative gaps, and oversights you might miss when you're racing to publish.

• Use agentic AI to audit your existing content and see how it stacks up. Use AI to check existing content and docs to see if you're offering something unique, using terms correctly for your audience segment, and covering everything your audience needs.

• Build in checks for AI output and give yourself time to refine. You’re becoming familiar with your agentic AI tool and need to double-check its output to ensure your content truly serves your readers.

Introduction

If you’re like most documentarians, you constantly feel pressure to use AI and churn out documentation faster, but end up with mixed results. You get time saved summarizing research, but more work checking AI for hallucinations. 

Agentic AI doesn't reduce work—it intensifies it. But there's a smart way to apply it that avoids churning out content just to move metrics.

AI actually shines as your quality-control partner. Here's how I use it to catch technical errors, uncover narrative gaps, and ensure completeness.

What’s agentic AI?

According to IBM, agentic AI describes systems that can accomplish a specific goal with limited supervision and mimic human decision-making. These agents work with a complex objective that has multiple steps. 

For example, an agentic AI system might take your documentation from multiple sources and draft a key takeaways section. It processes the information, "understands" it, decides what steps to take, and determines what information to return—all without you micromanaging each step.

This is different from a simple grammar check (which just follows rules) or asking ChatGPT to write a section (where you're still directing each step). With agentic AI, you should be able to walk away and let it complete the entire task. 

How do you use agentic AI to get quality content?

I use agentic AI as part of my writing process to check my content. It’s a hybrid approach that saves me time and helps me translate complex concepts more effectively. Here are three specific ways I deploy agentic AI as my quality auditor.

Checking for unique positioning

When my technical and business audiences want advice on data governance, they get flooded with content. Data governance is a very crowded space. 

So, I need to engage my audience and address their problems in a way that’s not repeating what is already out there. I could search through the internet, determine what’s there, and then rate my topic. Or I can have my agentic AI tool do it for me.

Based on my draft and source material, my agentic AI checks for unique positioning. It autonomously searches, compares, identifies gaps, and reports on its findings. I guide my content edits and any further research based on this information.

Unique positioning example

Recently, I had to write a synopsis of a data governance challenge event, including why two teams won and their approaches. I wanted to check that my content provided readers something new and actionable, given my source material.

I asked my agentic AI tool to determine how my content compares with the available data governance documentation and identify gaps. The tool found tons of documentation on what data governance is and why it matters, but fewer pieces on the practical skills of translating data governance concepts into business language and applying them within organizational realities. I refocused my content to show how teams applied data governance knowledge under time constraints, speaking to what the readers needed.

Validating technical correctness

Beyond positioning checks, I also use agentic AI to validate technical correctness, especially when I’m including closely related concepts. Given my drafted document, writing goals, audience, and style guide, agentic AI checks the web. It compares concepts and reports on whether I’m using terms correctly. This assistance helps me update and correct my draft.

Technical correctness example

When I wrote a detailed piece on a knowledge graph, I needed to get that concept right, especially as architects use it to train AI. I started out describing it as a type of property graph.

I asked the AI agent to compare a property graph and a knowledge graph. The agent’s summary helped me understand that a property graph can depict many different relationships, but is constrained by the type of business operation, like the items selected for an online shopping cart. 

Knowledge graphs don’t have that limitation. Since some sources were mixing up these concepts, I carefully clarified the terminology in my content and made sure my explanations were accurate.

Ensuring completeness for your audience

Readers don't just want accurate content; they want all their answers in one place. While it’s easy to research and write a doc on what you know, you might miss a few key points your readers need.

I ask my agentic AI to check for completeness using my drafted content and source material. The system autonomously reads my materials, searches the internet, identifies patterns, finds gaps, and reports back. Using its report, I do more research and update my content. 

Completeness Example

I wrote an article covering the top data management trends to follow in 2026. I wanted to make sure I covered the major trends, so I tasked my agentic AI to check existing content and compare it with my draft.

It aligned with the broader data management trends and stood out in its coverage of content and AI architecture. However, the AI tool highlighted the need to cover cloud and hybrid architectures more deeply. I went back and fleshed that out in the “Steps to Improve Data Governance” and “Steps to Improve AI Governance” sections.

Why human oversight is essential

Agentic AI saves time by independently checking documentation quality; human involvement is needed. Take my technical correctness example: agentic AI saved me time by summarizing information about property graphs and highlighting how I could integrate it into my article.

However, the agentic AI system presented information about the property graph that was more technical than my reader needed. I wanted a definition that would resonate with businesspeople, so I connected my property graph’s explanation to corporate operations. The agentic AI still independently decided where to source information and what to include, but I guided the final wording.

Your next steps

Ready to try agentic AI as your quality-control partner? Start with these practical steps:

• Start with technical validation: Begin with technical correctness checks since they're the most concrete. Ask your agentic AI to compare key concepts with authoritative sources and flag any discrepancies.

• Test one validation method at a time: Don't overwhelm yourself. Pick either unique positioning, technical correctness, or completeness checks.

• Track what works: Keep notes on which AI feedback proves useful and which doesn't. This helps you refine your prompts and build confidence in the process.

• Iterate: Start with a well-defined use case and expand from there. Once you've seen the time savings and quality improvements, you can apply these methods to more of your content.