Whitepapers, research articles, and blog posts sharing our findings, experiments, and learnings with the AI community.
Most business apps are CRUD wrapped in business logic, and that logic is migrating into an AI agent layer. "SaaS is dead" sounds right until you ask where reliability actually comes from. Capability was never the line. Verifiability is. This essay maps what AI will eat, what it won't, and why agents don't dissolve the problem.
This article explains how to design and implement a simple AI agent architecture in Ruby, focusing on core concepts like perception, reasoning, decision-making, and action rather than just code. Using Ruby's clarity, it demonstrates how modular components come together to form a goal-driven agent that interacts with its environment in a structured, understandable way.
Designing and evaluating system-level methods for extracting, updating, and using long-term user memory from conversations—without hallucinated personas, stale assumptions, or excessive compute.
Agentic LLMs struggle to balance competing objectives across long execution horizons. This work studies how models and agent architectures handle persistent, conflicting goals and proposes evaluation methods and design patterns to improve objective-aware reasoning.
We investigate how large language models can communicate using compact, machine-native encodings rather than human language, focusing on semantic density, shared ontologies, and emergent protocol design under strict token constraints.
