https://aiforhumanexpertise.com/tags/knowledge-representation/Recent content in Knowledge-Representation on AI For Human ExpertiseHugoen-gbMon, 22 Jun 2026 00:00:00 +0000https://aiforhumanexpertise.com/blog/a-five-role-diagnostic-for-any-ai-agent/Mon, 22 Jun 2026 00:00:00 +0000https://aiforhumanexpertise.com/blog/a-five-role-diagnostic-for-any-ai-agent/<h2 id="most-agent-problems-have-a-diagnostic">Most Agent Problems Have A Diagnostic</h2> <p>When an AI agent fails in production, the conversation almost always turns to the model. The team waits for a bigger model, a better-tuned variant, a different vendor, a new context window. Sometimes the team turns to prompts instead: more careful instructions, more examples, a sharper system message. Most failures are neither. They are failures of structure, and the structure question is a thirty-year-old framework most engineers have never read.</p>https://aiforhumanexpertise.com/blog/the-harness-is-where-symbolic-ai-returned/Mon, 15 Jun 2026 00:00:00 +0000https://aiforhumanexpertise.com/blog/the-harness-is-where-symbolic-ai-returned/<h2 id="the-standard-story-about-ai-agents-misses-half-the-system">The Standard Story About AI Agents Misses Half The System</h2> <p>The dominant story about building AI agents is straightforward: take a language model, prompt it well, give it tools, and let it loop. The model is doing the work. Everything else is plumbing.</p> <p>This story is incomplete in a way that has practical consequences. The plumbing is not plumbing. It is a body of design decisions that determines whether the agent works in production, fails silently, or quietly degrades over time. And some of it, the parts that earn the name, is doing what symbolic AI has always tried to do: making reasoning explicit, checkable, and tied to a model of the world.</p>