Hierarchy, Not Hype: Why Domain Logic Beats Agent Chaos
A practical reading of HTAM and EarthAgent, showing why domain-structured agent hierarchies can outperform generic agent workflows in specialized planning tasks.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Mind Over Matter: How a BDI Ontology Gives AI Agents an Actual Inner Life
A mechanism-first reading of how formal BDI ontology can make AI agents more auditable, interoperable, and explainable without pretending that ontology magically solves reliability.
Probe and Error: Why Off‑Policy Training Warps LLM Behaviour Detectors
A practical reading of why LLM behaviour probes can look accurate in synthetic tests yet fail when the monitored behaviour depends on hidden generative context.
When Curiosity Becomes Contagious: Mutual Intrinsic Rewards in Multi-Agent RL
A mechanism-first reading of Mutual Intrinsic Reward, a simple reward-coupling idea for helping cooperative reinforcement-learning agents explore actions that change each other’s worlds.
CLOZE Encounters: When LLMs Start Editing Medical Ontologies
A mechanism-first reading of CLOZE, a zero-shot pipeline for turning clinical notes into candidate Disease Ontology extensions without pretending that LLMs have become unsupervised medical librarians.
Concurrency, But Make It Fashion: Why Trustworthy AI Needs an Agentic Lakehouse
A mechanism-first reading of why trustworthy AI agents need lakehouse infrastructure built around branches, sandboxed compute, declarative I/O, and atomic merge.
Drift Happens: Why AI Needs a Memory for People, Not Just Patterns
PersonaDrift shows why long-term AI monitoring needs personalised baselines, anomaly-specific detectors, and calibration before it can credibly notice human change.
ESG in the Age of AI: When Reports Stop Being Read and Start Being Parsed
Pharos-ESG shows why useful ESG intelligence starts with evidence architecture, not simply larger multimodal models.
Mind the Gap: Why Digital Consciousness Isn’t One Debate, but Forty-Two
A mechanism-first reading of why AI consciousness arguments need taxonomy before they need louder opinions.
Mind the Model: When Generative AI Teaches Neuroscience New Tricks
A mechanism-first reading of why generative AI should be treated less as a brain metaphor and more as a source of testable conjectures for cognition, learning, attention, scaling, and representation.