Character, Actor and Performance: Why a Fictional Person Is More Than Canon
Canon defines a fictional person, but an actor changes which expressions, transitions and interpretations become convincingly reachable in performance.
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Agent Memory Architecture: Working, Episodic, Semantic and Procedural Memory
Agent memory requires typed records, controlled writes, scoped retrieval, consolidation, provenance and deletion—not just a vector database.
AI Agent Architecture: Models, Memory, Tools, State and Control
Learn the components of a production AI agent and where reasoning, memory, state, tools and approval should live.
AI Coordinator Architecture: Preserve Process Sovereignty
The coordinator owns the run lifecycle and accepted transitions, allowing organizations to use probabilistic models without surrendering process control.
Character, Actor and Performance: Why a Fictional Person Is More Than Canon
Canon defines a fictional person, but an actor changes which expressions, transitions and interpretations become convincingly reachable in performance.
From AMR to Execution Graphs for Agentic Workflows
AMR captures who did what to whom; execution graphs define state, permitted transitions, tools and validation for agentic workflows.
Graph RAG vs. Agent Memory vs. World State
Choose Graph RAG for connected retrieval, agent memory for reusable experience and world state for authoritative current truth.
Editor's Picks
AI Transparency Is a System Property
A model explanation is not enough. AI transparency must be designed into the surrounding knowledge and decision system so outputs can be traced, challenged and corrected.
Falsification, Not Determinism, Is the Central AI Problem
Determinism supports reproducibility, but reproducibility is not truth. Knowledge-centric AI requires claims that can be tested, falsified and corrected.
Intelligence Navigates by Invariants
Invariants provide orientation across changing situations, while entropy, falsification and incompleteness explain why no useful map should be mistaken for the whole territory.
LLMs Expand Possibilities—Knowledge Eliminates Errors
LLMs widen the search space through rapid candidate generation. Knowledge-centric systems need an equally explicit process for rejecting false paths.
The Entropy Budget of Intelligence
Entropy connects generative abundance to physical infrastructure: variation expands the candidate space, while selection must pay to turn uncertainty into usable structure.
The Two Engines of Discovery
LLMs have made candidate generation abundant. Reliable progress now depends on the quality of the selection system around them.