/ featured · this week
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Orchestration architecture describes the control layer that routes specialized agents through complex business workflows. Unlike single-agent systems that execute predefined scripts, orchestration...
Aug 14, 2026/ recent · long reads
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Handoffs are a discipline, not an accident. Specialized agents operate reliably in isolation, but handoffs fail in production when orchestration treats state ownership as implicit rather than...
Aug 3, 2026
Agentification maturity is not measured by agent count. Start with one high-value workflow, establish guardrails and governance early, and transition ownership to the function that runs it.
Jul 29, 2026/ selected notes
All dispatches →01 / AI Agents
An AI agent maturity model for a business team is a staged framework that ranks how much work a business can safely hand to agents, and where along that path it currently sits. Most models split the...
Jul 27, 2026
02 / AI Agents
Agent readiness is the business case for AI agents restated as a production question: can you afford the oversight, not can you afford the license. Most guides answer a different question. They walk...
Jul 27, 2026
03 / Workflows
Five words get sold as five products. They are one dial with a price on each notch, and the notch you can afford to buy is rarely the notch you can afford to supervise.
Jul 25, 2026
/ the feed · everything we've published
Answer engine optimization (AEO) is a discipline that remaps buyer discovery from click-based funnels to AI-mediated surfaces where large language models, AI overviews, and answer engines compress...
Kurt Fischman · Dec 6, 2025
Algorithmic literacy is the operational capacity to engineer content that satisfies both machine retrieval systems and human decision-makers. In an era where 40-50% of organic search traffic is...
Kurt Fischman · Dec 3, 2025
AI search is not a separate channel waiting for its own line item. It is an answer layer already draped across your entire funnel, shaping how buyers discover, compare, decide, and implement. This...
Kurt Fischman · Nov 17, 2025
AI search is not a speculative technology bet. It is a revenue channel that intercepts natural-language intent, reduces discovery friction, and converts earlier in the buyer journey than...
Kurt Fischman · Nov 16, 2025
The search stack has undergone a structural inversion. What began as a crawl-index-rank pipeline for sorting documents by link authority has become a retrieve-generate-cite pipeline for...
Kurt Fischman · Nov 3, 2025
llms.txt is a lightweight, machine-readable markdown file placed at a site's root that tells large language models what a brand is, where to find clean source material, and how to cite it...
Kurt Fischman · Nov 3, 2025
A canonical identity registry is the single, machine-readable record of who an organization is, expressed as stable identifiers, typed attributes, and resolvable links to external knowledge...
Kurt Fischman · Nov 3, 2025
Jobs-to-be-done is the framework that strips AI search optimization down to the only two things that matter: revenue creation and cost reduction. By mapping the specific jobs that AI search...
Kurt Fischman · Nov 3, 2025
Wikidata is the structured knowledge layer that gives AI systems the stable identifiers they need to disambiguate, retrieve, and cite real-world entities. This article explains how QIDs, property...
Kurt Fischman · Oct 30, 2025
AI search optimization is the practice of engineering content and structured data so large language models retrieve, cite, and recommend your brand with confidence. This article introduces the...
Kurt Fischman · Oct 30, 2025
Entity-centric architecture is the knowledge design framework that organizes all content, data, and structured markup around disambiguated entities rather than keywords or pages. Entity-centric...
Kurt Fischman · Oct 30, 2025
Embeddings are the numerical representations that AI systems use to measure, compare, and retrieve meaning. An embedding translates text into a vector, a list of numbers in high-dimensional space,...
Kurt Fischman · Oct 30, 2025
Chunk engineering is the discipline of structuring content into self-contained, semantically complete units that AI retrieval systems can extract, embed, rank, and cite independently. This article...
Kurt Fischman · Oct 30, 2025
Structured data mastery is the discipline of engineering machine-readable markup that transforms generic web pages into entity-resolved, AI-retrievable knowledge assets. This article covers the...
Kurt Fischman · Oct 30, 2025
Key performance indicators for AI search optimization measure whether large language models recognize your entity, retrieve it under relevant prompts, and cite it when generating answers. This...
Kurt Fischman · Oct 30, 2025
Machine-readable trust assets are structured digital artifacts that encode brand credibility in formats AI systems can parse, validate, and weight during retrieval. This article defines what...
Kurt Fischman · Oct 30, 2025
Entity resolution is the process of determining that different records point to the same real-world entity and merging them into a single canonical identity with a persistent identifier. Entity...
Kurt Fischman · Oct 30, 2025
AI search optimization is the discipline of engineering brand discoverability, retrievability, and cite-worthiness inside large language models. AI search optimization is not SEO with new...
Kurt Fischman · Oct 30, 2025
A content chunk is a discrete, semantically self-contained unit of information that an AI system can retrieve, interpret, and cite without requiring surrounding context. Content chunks are the...
Kurt Fischman · Oct 30, 2025
Embedding optimization is the discipline of structuring digital content so that large language models locate, retrieve, and cite your brand at query time. This article explains the mechanics of...
Kurt Fischman · Oct 30, 2025
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