Constructing intelligent search experiences with Straive and Shelf

The core value of agentic AI to create faster, smarter search experiences hinges on quality, accessibility, and accuracy. AI-powered search delivers the best results when it understands user intent, context, and nuance.

Human involvement ensures trusted, hi-fidelity search outcomes. In turn, this acceleration of discovery leads to higher productivity, more satisfied users, and better search results.

KMWorld recently held a webinar, Agentic AI at the Core: Building Faster, Smarter Search Experiences, with Olga Tchivikova, VP, client engagement at Straive and Jan Štihec, director, data and AI at Shelf, who discussed how to get agentic AI and the underlying knowledge right in this agentic era.

According to Tchivikova, the real bottleneck isn’t the model, it’s the knowledge behind it. AI does not just need data. It needs meaning.

Great AI helps people ask better questions. The next frontier of enterprise search is intent clarification, not keyword matching, she explained. The smartest AI is not the one that reads everything, it is the one that knows what not to read.

Agentic AI is only as strong as the knowledge foundation beneath it. Trusted knowledge is what makes Agentic AI useful, reliable, and efficient.

Straive offers capabilities that include:

• Content: Transform fragmented information into AI-ready assets.
• Knowledge: Add metadata, taxonomy, relationships, context, and governance.
• Search: Enable trusted retrieval, cited answers, and intelligent workflows.

Enterprise AI is not just a model problem. It is a knowledge problem. Organizations that build trusted knowledge foundations will build trusted AI, Tchivikova concluded.

There are three pillars of AI agent readiness, Štihec said. Agentic AI searches, interacts with, and applies knowledge very differently than people do. Three obstacles decide whether it is truly ready.

These pillars include:

Poor quality: Redundant, obsolete, trivial, inaccurate, outdated, incomplete, and contradictory content accumulated over years of manual upkeep. Painful for people—and dangerous for AI, which reproduces those errors at scale.

Unclear structure: Long prose, complex tables, embedded exceptions, inconsistent headings, logic split across files. Agents need knowledge recast into explicit rules, definitions, relationships, permissions, dependencies, and decision points.

Missing context: LLMs never learned your terminology, product ties, service tiers, segments, policies, or governance. Working from raw chunks, business meaning is lost, business logic goes unenforced, and controls break down.

Deploying traditional RAG is not enough, Štihec said. Every answer is assembled through a chain of probabilistic steps. Relevance is scored by semantic similarity, retrieval is often incomplete, and generation is not fully grounded. The result is mixed, unpredictable answers.

However, Shelf offers deterministic and accurate agentic answers, Štihec explained. A deterministic knowledge layer sits between your content and the answer. It processes, retrieves, remembers, applies your business logic, and reasons—so answers are grounded in your own knowledge, he said.

For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.