How an executable graph breaks the static toolbox by creating, activating, and using new capabilities while work is still in progress....
A technical explanation of Buffaly's second supervisory loop: how a separate model watches the working agent's memory, catches drift, preserves continuity through compaction, and sends the worker back to concrete tool work when it matters....
A technical explanation of executable graph agents: how semantic identity, typed objects, runtime actions, native code, and self-extending capability change what agents can learn and execute....
A practical evaluation of local embedding models for Buffaly's short action/entity semantic retrieval workload, including methodology changes, run IDs, EmbeddingIDs, storage caveats, and reproducibility notes....
How language acquisition, dual-channel learning, ontology, ProtoScript, and executable memory shaped the long path to Buffaly....
A case for building runtime-first systems around frontier models instead of asking larger prompts to become memory, execution, policy, and control....
Why traditional LLM agents are an operational dead end in medical administration: and why we built a neurosymbolic alternative....
A different kind of agent: one that turns language into executable structure instead of keeping everything in text prompts....
In the race to solve complex problems with AI, the default strategy has become brute force: bigger models, more data, larger context windows. We put that assumption to the ultimate test on a critical healthcare task, and the results didn’t just challenge the “bigger is better” m…...
This blog outlines a groundbreaking proof of concept for reimagining medical ontologies and artificial intelligence. Buffaly demonstrates how large language models (LLMs) can unexpectedly enable symbolic methods to reach unprecedented levels of effectiveness. This fusion deliver…...
Large Language Models (LLMs) have ushered in a new era of artificial intelligence, enabling systems to generate human-like text and engage in complex conversations. However, their extraordinary capabilities come with significant limitations, particularly when it comes to predict…...
The rapid advancement of Large Language Models (LLMs) has brought remarkable progress in natural language processing, empowering AI systems to understand and generate text with unprecedented fluency. Yet, these systems face a critical limitation: while they excel at processing l…...
In the beginning there was keyword search . Eventually word embeddings came along and we got Vector Databases and Retrieval Augmented Generation (RAG) . They were good for writing blog posts about topics that sounded smart, but didn’t actually work well in the real world. Fast f…...
In Volodymyr Pavlyshyn's article , the concepts of Metagraphs and Hypergraphs are explored as a transformative framework for developing relational models in AI agents’ memory systems. The article highlights how these metagraphs can act as a semantic backbone, enabling AI to reta…...
As artificial intelligence (AI) becomes a powerful part of our daily lives, it’s amazing to see how many directions the technology is taking. From creative tools to customer service automation, AI can be both a powerhouse and, at times, a bit of a playground. At Intelligence Fac…...
When it comes to data retrieval, most organizations today are exploring AI-driven solutions like Retrieval-Augmented Generation (RAG) paired with Large Language Models (LLM) . These systems have certainly made strides in helping businesses pull information from large datasets an…...