Examining the role of content quality in the AI agent boom with Acrolinx

A common theme as new, game-changing technologies emerge is a return to foundational paradigms that have shaped—and continue to shape—knowledge management. In the space of AI Agents, content management plays a key role in its success. Without quality, well-governed content, AI agents can produce inconsistent, inaccurate, or even non-compliant outputs, ultimately damaging customer trust and business reputation.

Experts from Acrolinx joined KMWorld’s webinar, Is Your Knowledge Content Ready for AI-Powered Agents? Key Strategies to Optimize for Large Language Models (LLMs), to discuss how to best prepare your content repositories for the benefits—and risks—of AI agents.

Jean Bezek, senior solutions consultant, Acrolinx, explained that the advantages to LLM-driven AI agents are each critical to their overall value. One piece cannot be lost, and the larger system still retains its utility, noted Bezek; these pieces include:

• Language interaction: Their inherent ability to understand and generate language ensures seamless user interaction.
• Decision-making capabilities: Large language models can reason and make decisions, making them adept at solving complex problems.
• Flexible adaptation: The adaptability of agents ensures they can be shaped for different applications.
• Collaborative interaction: Agents can interact with humans or other agents collaboratively, paving the way for multifaceted interaction.

With these benefits known, why is content quality important?

Chris Carroll, director of product and digital marketing, Acrolinx, emphasized the “garbage in, garbage out,” adage that we’ve all come to know in the AI era. If the content is poor quality, so too will the outputs be. Not only that, content must also be interconnected across departments and functions to deliver the most accurate, useful response to a user query—which introduces a range of challenges.

“The idea is that you should be using content that’s coming from a lot of different places to get that best answer, but oftentimes—looking at formatting or style or tone or any of that—it may be very inconsistent. And as a result, you’re putting inconsistent things into your brain [LLM],” Bezek explained.

Curt Raffi, chief product officer, Acrolinx, further echoed Bezek’s point, referring to a customer case study where siloed, unaligned content and departments were key inhibitors of AI agent adoption.

“What is garbage to one department versus another is something that can introduce massive inconsistencies,” said Raffi. Adding to this complexity, the content “may not be ‘garbage,’ there may be no metadata associated with that content, it might not be aligned with Q&A pairs to power the agent—all of those can introduce challenges that people aren’t thinking about.”

“Garbage is one thing, but it’s also unstructured content that isn’t aligned for the purpose it’s being used for,” Raffi continued.

Content must be clear, using clear terminology and phrasing, and follow a consistent pattern of structure, added Carroll. Guardrails are also crucial components of the greater AI agent structure, ensuring that they are producing the right kind of content for both human- and machine-readable purposes. Carroll identified these four guardrails:

1. Content Creation: Ensure that guidelines are followed at the moment of content creation by existing within those environments. The best place to fix errors is during the drafting processes, not later down the line when it becomes costly to correct.
2. Quality Gate: Put into the publishing process “gates” or stops that validate whether the content meets the desired level of quality. If it does, it proceeds to the next stop; if it doesn’t, return it to the content creator to be corrected.
3. Content Audit: Be able to benchmark existing content to make sure quality levels are consistent across documents. Running a content audit prior to moving the content to the LLM helps ensure that no poor-quality content is missed.
4. Published Content: Make sure content continues to be valuable. If there are any changes in style, terminology, or regulatory changes, be able to persist those changes across your content repositories—and therefore, your LLMs.

Acrolinx helps impart some of those critical guardrails that prevent poor quality content from entering LLM systems. Its enterprise-grade generative AI guardrails offer rapid content review that ensures style guide alignment and regulatory compliance, addressing key challenges in the content management/LLM pipeline.

This is only a snippet of the full webinar discussing strategies for optimizing content in preparation for agentic AI. For the full webinar, featuring in-depth examinations, case studies, and more, you can view an archived version here.