Search boxes look innocent. They sit there politely, waiting for the user to type something useful. In ordinary software, this feels reasonable. In a document repository, a customer support portal, or a product catalogue, the user usually arrives with at least a rough idea: a name, a keyword, a complaint, a document type, a half-remembered phrase. ...
Context is comforting. A large context window gives managers, developers, and product demos the same pleasant illusion: if the model can see enough of the repository, it should stop missing important files. Put the whole codebase into the window. Add retrieval if necessary. Let the agent read, reason, edit, and move on. ...
A lab workflow is a terrible place to discover that your AI agent has been “remembering” chemistry as a conversation. That sounds unkind. It is also the point. In a casual chatbot, losing track of context means an awkward answer. In computational chemistry, losing track of context can mean a wrong molecular geometry, a missing imaginary-frequency check, an invalid charge or multiplicity, or a pKa estimate that looks numerically confident while being scientifically useless. The model did not necessarily become stupid. The workflow around it treated state as text. ...
Links look harmless. One click from one Wikipedia page to another. Then another. Then another. No robotics. No messy browser UI. No customer database. No procurement workflow with three inconsistent Excel files and one person named Mike who “usually knows where that form is.” Just hyperlinks. That is why LLM-WikiRace is useful. It strips agentic AI down to a small, irritating question: when a model knows a lot about the world, can it use that knowledge step by step without getting lost?1 ...
Archive work has a very simple-looking question at its center: who was where, and when? That question looks harmless until a machine has to answer it from a century-old newspaper, after OCR has mangled the spelling, the place names have shifted, the language is not always English, and the text only implies the answer through an event, job title, or institutional affiliation. At that point, “extracting information” becomes less like copying a fact from a sentence and more like making a legally cautious inference from a witness who speaks in fragments. ...
Workflow reuse sounds like a housekeeping problem. It is not. In many companies, workflow automation has already escaped the tidy diagram on the transformation slide. One team builds an n8n flow to process invoices. Another builds a Dify workflow to triage support tickets. A third writes an internal tool chain for compliance checks. Each workflow contains useful logic: API calls, branching rules, exception handling, data validation, reporting steps, and the small ugly details that make automation survive contact with real operations. ...
A knowledge graph is supposed to make AI systems less vague. That is the pitch, at least. Instead of letting a model float around in text, we give it entities, relations, and structure. A person works at a company. A product belongs to a category. A supplier is connected to a shipment, an invoice, a warehouse, and eventually a mildly panicked operations manager. ...
RAG has a public-relations problem. It promises grounded answers, then quietly assumes that “grounded” means “retrieved from somewhere nearby.” That assumption is convenient. It is also the kind of convenience that creates compliance incidents, medical confusion, and internal knowledge assistants that cite the wrong document with absolute confidence. A retrieval-augmented system can answer from evidence and still choose the wrong evidence. It can cite something real and still fail provenance. ...
Retrieval has a breathing problem. Most enterprise RAG systems inhale once, grab the nearest chunks, and then hope the model can make the answer sound less fragile than the evidence actually is. That works tolerably well when the user asks for something sitting neatly inside a document paragraph. It works less well when the answer lives across entities, relations, aliases, product categories, authors, diseases, suppliers, regulations, or customer records. In other words, it works less well in the part of business where knowledge is not a pile of text but a network. ...
Retrieval has a management problem. Not the motivational-poster kind of management problem. The operational kind. A company asks its AI system a question about a contract, a customer dispute, a policy exception, or a technical incident. The answer is not sitting in one paragraph. It is distributed across definitions, transactions, policies, exceptions, and historical context. A flat vector search grabs a few semantically similar chunks and hopes the model can stitch them together. A global summarizer reads widely, compresses aggressively, and occasionally smooths away the exact fact that mattered. A local graph search follows nearby entities and may become very confident inside the wrong neighborhood. ...