The Source of Knowledge

an Artificial Intelligence Laboratory

The Laboratory

We are a research lab pioneering the future of autonomous organizations—companies where AI agents handle day-to-day operations while humans focus on strategic oversight, guardrails, and policy control. Our mission is to develop the foundational technologies, protocols, and frameworks that enable organizations to operate autonomously at scale.

This vision requires reimagining organizational architecture from the ground up. At its core is a fundamental shift in how work is distributed between human oversight and autonomous execution.

Architecture of an autonomous organization with AI agents and human oversight

Autonomous organization architecture with AI agents and human oversight

Through our research in multi-agent systems, we're building the infrastructure for AI-driven automation across operations, finance, growth, and specialized domains. We advance open standards like MCP (Model Context Protocol) and A2A (Agent2Agent) to enable seamless agent coordination and interoperability.

Our approach redefines the human-AI relationship: rather than replacing humans, we create systems where humans establish policies, define guardrails, and oversee strategic decisions while AI agents execute operational tasks. This paradigm shift allows organizations to scale efficiently while maintaining human values and accountability at their core.

Central to this is our research in agentic UI/UX—designing interfaces that enable humans to effectively monitor, guide, and control autonomous agent systems.

The Team

Meet the researchers and engineers building the future of autonomous organizations.

Andrii Tsok

Leadership, Research and Development

Ihor Zahnybida

Technical Staff, Software Engineering

Nataliia Tsok

Experience Staff, Agentic UI/UX

Interested in our research or want to join our mission? We're looking for like-minded individuals passionate about AI and the future of autonomous organizations. Reach out to us at team@tsok.org

Research Papers

Our latest research advancing the field of AI agents and human-AI collaboration.

2024

Bridging Human-AI Collaboration

Exploring methods for improving transparency and trust in AI agent interactions through structured communication protocols and real-time feedback mechanisms.

arXiv Code

2024

Multi-Agent Systems: Coordination Patterns for Complex Task Execution

A comprehensive study on coordination strategies for multiple AI agents working together to solve complex problems with minimal human intervention.

arXiv Code

2023

Adaptive Context Management in Long-Running AI Agent Conversations

Novel approaches to managing context windows and memory in extended AI agent interactions, enabling more coherent and contextually aware responses over time.

arXiv Code

Projects

Production-ready tools and platforms for building and deploying AI agent systems.

In Development

AgentFlow

A drag-and-drop interface for designing, testing, and deploying complex AI agent workflows. Supports multiple LLM providers, custom tools, and real-time monitoring of agent activities.

Website GitHub

Beta

CollabHub

A comprehensive platform that enables seamless collaboration between human teams and AI agents, featuring shared workspaces, task assignment, and integrated communication channels.

Website GitHub

Active

AgentBench

Standardized testing framework for evaluating AI agent capabilities across various tasks, including reasoning, tool use, collaboration, and long-term planning.

Website GitHub

Open Source

Community-driven tools and libraries to accelerate AI agent development.

PythonMIT License

agent-toolkit

A collection of reusable components and utilities for building robust AI agents, including context management, tool integration, error handling, and logging.

GitHub Docs

TypeScriptApache 2.0

mcp-inspector

Developer tool for inspecting, debugging, and testing MCP servers and clients. Features real-time message inspection, request/response logging, and performance monitoring.

GitHub Demo

MarkdownCC BY 4.0

conversation-patterns

A curated collection of conversation design patterns, best practices, and examples for building engaging and effective human-AI interactions.

GitHub Website

We are committed to building truly open source solutions that benefit the entire AI community. For those who wish to use our tools for commercial purposes or require enterprise licensing, please contact us at team@tsok.org.

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