BrainGnosis
BrainGnosis
At BrainGnosis, we harness deep insight to create smarter, human-centric AI solutions.
Get In TouchEmpowering the future with smarter, human-centric AI solutions.
BrainGnosis Inc. is on a mission to build reliable, adaptable, and deeply human-aligned AI agents that transform how organizations work. Our name—"BrainGnosis"—is a portmanteau of "brain" and the Greek word gnōsis (γνῶσις), meaning "knowledge" or "insight."
This name reflects our vision of AI that goes beyond data processing—AI that gains gnostic-level understanding, operating with intuition, memory, and context, just like the human brain. We aspire to develop agents that don't just analyze—they understand, reason, and evolve with purpose.
AgentOS is our flagship platform—a full-stack operating system for AI agents that enables the creation, management, and collaboration of intelligent agents. It supports modularity, scalability, agent-to-agent communication, and even the ability for agents to create other agents.
Join us in reshaping the enterprise landscape with AI agents that think, adapt, and collaborate. With BrainGnosis, the future of work is intelligent, efficient, and deeply human.
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Custom AI models tailored to your business needs, built with cutting-edge technology.
Expert guidance to integrate AI into your business for enhanced efficiency and innovation.
Automate your workflows with AI-powered solutions that boost productivity.
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AgentOS powers intelligent workflows with smart, modular, and autonomous agents—built for enterprise-scale operations.
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Visionary leader with deep expertise in AI and machine learning.
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Crafts compelling strategies to connect with global audiences.
Redefining the path to Artificial General Intelligence
Introducing SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that enables LLM-based agents to autonomously plan, reason, and achieve robust domain-specific task execution through stateful memory systems.
A modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution.
Agents are constructed with FSA memory, enabling persistent state tracking and context-aware decision-making across extended workflows.
Validated through integration with physical hardware (microwave synthesizers) and virtual systems (PubChem database) for autonomous multi-step scientific workflows.
Stateful Memory for Reliable AI
A comprehensive framework that shifts the definition of AGI from scale to structure, from model to system, and from static prediction to dynamic emergence, rooted in statistical mechanics and neuroscience.
Intelligence emerges from networks of dense, redundant, interacting systems constrained by principles of entropy, feedback, and cooperation.
Defines AGI as a critical point in the agent interaction landscape, beyond which collective behavior becomes qualitatively intelligent.
Demonstrates four key capabilities: adaptivity to changing goals, context-awareness through persistent state, conflict resolution, and long-term planning.
From Scale to Structure: A New AGI Paradigm
The prevailing conception of artificial general intelligence (AGI) often centers on single-agent multimodal large language models (LLMs) and physical models that mimic human linguistic and physical intelligence. However, this perspective overlooks the importance of decentralized, interacting systems in producing generalizable, adaptive behavior.
Drawing inspiration from statistical mechanics, neuroscience, and agent-based modeling, we argue that AGI is more appropriately conceptualized as an emergent property of interacting intelligent agents. These multi-agent systems, analogous to particles in thermodynamic ensembles or neuronal assemblies in the brain, possess the capacity for robust coordination, planning, adaptability, and resilience - properties absent in standalone models.
We propose a theoretical framework rooted in entropy and emergent dynamics and argue that AGI will not emerge from scale alone but from structured interactions among specialized systems.
Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, planning, and tool integration.
Here, we introduce SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution. Agents are constructed dynamically from source code documentation and augmented with finite-state automata (FSA) memory, enabling persistent state tracking and context-aware decision-making.
This approach eliminates the need for manual prompt engineering and allows for robust, scalable deployment across diverse applications via maintaining context across extended workflows and to recover from tool or execution failures. We validate SciBORG through integration with both physical and virtual hardware, such as microwave synthesizers for executing user-specified reactions, with context-aware decision making and demonstrate its use in autonomous multi-step bioassay retrieval from the PubChem database utilizing multi-step planning, reasoning, agent-to-agent communication and coordination for execution of exploratory tasks.
Systematic benchmarking shows that SciBORG agents achieve reliable execution, adaptive planning, and interpretable state transitions. Our results show that memory and state awareness are critical enablers of agentic planning and reliability, offering a generalizable foundation for deploying AI agents in complex environments.
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