The Stochastic Gap: Why Your AI Agent Fails Before It Starts
A mechanism-first reading of why enterprise AI agents fail when workflow support, decision ambiguity, and human oversight cost are treated as separate problems.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Autoresearch²: When AI Starts Debugging Its Own Brain
A mechanism-first reading of bilevel autoresearch: why the real advance is not smarter prompting, but AI-generated changes to the search process itself.
Nudge, But Make It Machine: The Rise of Mecha-Nudges
A mechanism-first reading of mecha-nudges: how markets may quietly optimize product information for AI agents without visibly changing the human interface.
RelayS2S: When AI Stops Waiting Its Turn
RelayS2S shows how real-time voice agents can start speaking quickly without giving up the stronger reasoning of cascaded ASR-LLM systems.
Shared Memory, Shared Intelligence: When AI Agents Stop Thinking Alone
How MemCollab turns heterogeneous LLM-agent experience into reusable, failure-aware memory without pretending every memory works for every model.
The Sealed Score: Why AI Evaluation Needs an Exam Day
A mechanism-first reading of the LLM Olympiad proposal, and why sealed, frozen, centrally run evaluations may become useful evidence for AI procurement and governance.
Thinking in Libraries: Why Humans (and AI) Solve Hard Problems by Rewriting the Search Space
A mechanism-first reading of online library learning: why reusable abstractions reduce search cost more than they shorten final answers.
When Agents Go Off-Script: The Quiet Collapse of Prompted Identity
A mechanism-first reading of why multi-agent systems can drift from prompted roles, form endogenous stances, and rebuild social order through language.
Braiding the Future: Why Autonomous Systems Need Topology, Not Just Trajectories
A mechanism-first reading of braid prediction shows why autonomous systems need to model future interaction structure, not merely forecast coordinates.
From Prompts to Policies: How Digital Twins Are Quietly Rewiring Enterprise AI Agents
A mechanism-first reading of DT-MDP-CE, a framework that turns messy enterprise agent traces into offline-learned policies for more controllable context engineering.