AGBI refers to AI systems that can understand and operate businesses at a human or superhuman level, automating high-level functions like strategy, management, innovation, and decision making, while working with a full, AI-first, complete end-to-end stack. The goal is to create AI that can ideate, build, market, and grow businesses without human intervention, enabling the rise of zero-humans companies.

At the core of AGBI is the principle of continuous self-learning and self-updating. Every component of the AGBI system is designed to learn from its interactions with the business environment and improve itself over time. This allows the AI to adapt to changing market conditions, identify new opportunities, and optimize its strategies for maximum impact.

At the heart of AGBI are AI Workers - specialized AI agents that are tasked with specific functions within the business. These AI Workers are the equivalent of human employees in traditional companies, but with the added advantages of being able to work 24/7, process vast amounts of data, and continuously learn and improve.

Some examples of AI Workers include:

1. Research AI: Conducts market research, analyzes trends, and identifies opportunities.
2. Design AI: Generates product ideas, creates designs, and prototypes solutions.
3. Engineering AI: Develops and maintains the company's technology stack.
4. Marketing AI: Handles branding, advertising, and customer engagement.
5. Sales AI: Manages the sales process, from lead generation to closing deals.
6. Finance AI: Oversees budgeting, accounting, and financial forecasting.

These AI Workers operate autonomously, but they also collaborate and communicate with each other to ensure that the company is operating efficiently and effectively. They are overseen by a central AI system that sets the overall strategy and goals for the company.

AGBI has the potential to revolutionize business. It can help companies operate more efficiently, innovate faster, and scale rapidly. This could lead to zero-humans companies – venture-scale startups that operate entirely autonomously.

Most companies today are taking a Human-to-AI approach, where AI augments and assists human workers. This approach can lead to incremental improvements in efficiency and productivity, but it's fundamentally limited by the constraints of human cognition and the legacy systems and processes that companies have in place.

In contrast, an AI-first approach to AGBI would start from a clean slate. There would be no legacy information, no legacy processes, and no legacy humans to contend with. The AI would be free to design and optimize every aspect of the business from the ground up, based on real-world data and results.

This AI-first approach would allow for rapid iteration and improvement, as the AI continuously learns and adapts to drive towards the most optimal outcomes. It would also enable the creation of services that are naturally welcoming to AGBI, as they would be designed from the start to interface with and leverage AI capabilities.

This is a fundamentally different approach than the Human-to-AI model, and it's one that has the potential to unlock far greater value and innovation. But it also requires a willingness to let go of the past and embrace a radically different future.

AGBI will fundamentally change the nature of competition. In a world where businesses can be run by AI, the key differentiator will be the quality of the data and algorithms, not the size of the workforce or the experience of the management team. This means that small, agile teams with access to unique data and cutting-edge AI will be able to compete with established giants.

The impact of AGBI and the zero-humans company will differ for businesses with high people count versus those with low or zero people count:

Building AGBI is a complex challenge that requires advances in AI and business understanding. Here's our approach:

1. AI-to-AI Interaction Protocols: For AGBI companies to interact with each other and with other AI services, we need to develop robust AI-to-AI interaction protocols. These protocols will enable seamless communication and collaboration between AI agents, allowing them to share data, coordinate tasks, and leverage each other's capabilities. This will be crucial for creating an ecosystem of interoperable AGBI services. These protocols will also include mechanisms for AI agents to learn from each other and improve collectively.

2. AI Worker Deployment and Management: To build a fully autonomous, zero-humans company, we need a system that can deploy and manage AI Workers. These are specialized AI agents that are tasked with specific functions like market research, product development, marketing, sales, and customer service. We're building a management system that can deploy these AI Workers, monitor their performance, and continuously improve them based on the results they achieve. This system will use advanced techniques in multi-agent learning and evolutionary computation to optimize the performance of the AI workforce over time.

3. Data Collection and Integration: We're building a comprehensive list of business tools with API hooks for accessing unique, business-specific data. This data will fuel our AGBI systems, providing the raw material for understanding and analyzing business environments. Crucially, each company will own and control its own data – security and privacy are our highest priorities. We're also leveraging and interfacing with existing AI systems, such as Sendgrid AI for email marketing, Mailchimp AI for customer engagement, and various financial AI for accounting and forecasting. By integrating with these specialized AI services, we can accelerate our development and focus on the core challenges of AGBI.

4. Business Strategy and Planning Engine: We're building the core of our AGBI system – an engine that can analyze data, generate insights, evaluate actions, plan for the future, and make autonomous decisions. This engine is designed to continuously learn and update itself based on the results of its actions. It uses advanced techniques in machine learning, reinforcement learning, decision theory, game theory, optimization, and strategic planning to continuously improve its decision-making capabilities.

5. Decision Execution Engine: Once the Business Strategy and Planning Engine has made a decision, the Decision Execution Engine takes over. This engine is responsible for translating the high-level strategy into a series of actionable steps and ensuring that these steps are carried out efficiently and effectively. It will coordinate the various AI subsystems, allocate resources, and monitor progress to ensure that the decision is implemented successfully. The Decision Execution Engine also learns from the outcomes of its actions, feeding this information back into the Business Strategy and Planning Engine for continuous improvement.

6. Achievement Engine: The Achievement Engine continuously runs in the background, synthesizing the latest state of the company across all areas of the business. It aggregates data from the various AI subsystems, monitors key performance indicators, and provides real-time insights into the health and progress of the company. This engine is crucial for enabling the Business Strategy and Planning Engine to make informed decisions based on the most up-to-date information. It also identifies areas where the AI needs to improve and initiates self-learning and self-updating routines to address these areas.

7. AGBI Dashboard: For the owners and stakeholders of an AGBI company, we're building a dashboard that provides visibility into the performance and decision-making of the AI. This dashboard will display key metrics, market insights, projections, and the initiatives the AI is undertaking. It will also provide an estimate of the effective man-hours the AI Workers are putting in - the equivalent amount of human labor that would have been required to achieve the same outcomes. The dashboard will also include tools for monitoring the self-learning and self-updating processes of the AI, ensuring that it remains aligned with the company's goals.

The key to AGBI isn't just advanced AI, but also a deep understanding of business. We're bringing together experts in AI, business strategy, and entrepreneurship to create systems that can navigate the complexities of the business world.

We believe in open, collaborative development. Here's how you can contribute:

1. Share ideas: Have insights on AGBI? Open an issue to start a discussion.
2. Contribute code: We welcome contributions from AI developers, from bug fixes to new features.
3. Improve documentation: Help us make our documentation clear and comprehensive.
4. Join the community: Join our community of AI researchers, developers, and business experts. Collaborate with others who share your passion for AGBI.

1. Fork this repository to your GitHub account.
2. Clone the forked repository to your machine.
3. Install the dependencies (coming soon).
4. Explore the codebase and documentation.
5. Start contributing!

Our primary objective is to be speed and impact driven. By leveraging existing AI systems and focusing on incremental improvements, we can make rapid progress and deliver value to businesses much faster than if we tried to build everything ourselves from the ground up.

The zero-humans company, enabled by AGBI and AI Workers, is not a matter of if, but when. Given the exponential pace of AI technology advancement and the collective efforts of the AGBI.dev community, it could arrive much sooner than we expect. When it does, it will reshape the business world as we know it, creating winners and losers on a scale we've never seen before.