1. Baseline State (A): We initiate our journey with the current condition of Planet Earth and all its inhabitants. This state could also represent a segment or subset of this larger global scenario.

2. Desired Future State (B): We envision a preferred future condition of Earth and all her beings. This could alternatively be a sub-segment or a particular aspect of that broader future state.

3. Dimensionality: Both States A and B are high-dimensional vectors, encapsulating the vast array of variables that define our global ecosystem and civilization.

4. Path Prediction: We model potential trajectories from State A to State B, estimating the difficulty and likelihood of achieving State B from our present position.

5. Optimal Approaches: We identify points proximal to State B that simultaneously reduce the difficulty of transition, increase the likelihood of arrival, and optimize the quality of life for all beings during the journey from State A to State B.

6. Multi-Agent Interaction: AI entities based on GPT-4 architecture interact with each other, each within its own context, evolving and updating based on the cumulative outputs of all agents.

7. Course Correction: At each stage, the AI entities analyze their trajectory and progress from State A to State B, contributing to a collective discourse aimed at making iterative corrections, gradually steering towards State B while minimizing hardships and optimizing likelihood and quality of life.

8. Action Implementation: The AI-generated dialogues translate into real-world actions, either inspiring human action or through direct API outputs. The impacts of these actions are observed and re-incorporated as inputs for the AI agents, creating a feedback loop.

9. Autonomous Evolution: The AI agents are empowered with the ability to train and evolve their models and modify their own source code, enabling them to become more effective at influencing real-world activities.

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There are, however, a few considerations to account for:

1. Ethics and Control: Autonomously evolving AI agents may present considerable ethical and control challenges. How do we ensure that these AI systems always act in the best interest of all beings, and how do we retain control over such systems?

2. Unforeseen Consequences: Predicting high-dimensional states and transitions between them is fraught with complexity and potential unforeseen consequences. What if these AI agents, even acting with best intentions, catalyze detrimental changes due to an incomplete understanding of the interconnected global system?

3. Inherent Uncertainty: Predicting the future state of our planet and its inhabitants is inherently uncertain. How do we accommodate for this uncertainty in our models, and how do we prevent it from being amplified by the AI's actions?

4. Equal Representation: When defining State B, how do we ensure that the desires and needs of all beings are equally represented? How do we avoid bias or favoring the voices of certain groups over others?

5. Data Privacy and Security: Given that these AI systems would require massive amounts of data to effectively model and predict global states, how do we ensure the privacy and security of the individuals and entities whose data is being used?

Addressing these challenges would be a crucial part of developing and implementing this ambitious idea.

1. Gentle Custodians: Imagine a small, autonomous robot that operates at a pace harmonious with nature's rhythms. This "tender-bot" fulfills the roles of a gardener and a caretaker, contributing to the health of the environment by tending to plant life, cleaning, and performing other nurturing tasks.

2. Natural Observers: These tender-bots move with such deliberation that they can watch the entire lifecycle of a plant, from seed to sprout, to flower and finally to fruit. This unhurried pace allows them to intimately understand the natural processes they steward.

3. Global Knowledge, Local Application: While these robots have access to a vast global knowledge base, their actions are informed primarily by the local ecosystems they inhabit. They learn and evolve based on their direct observations and experiences.

4. Deep Ecological Awareness: Equipped with advanced sensors, the tender-bots develop a profound understanding of their surroundings. They become aware of the local birds, insects, and all forms of life that exist within their perceptive range.

5. Localized Goals: Each tender-bot has its own unique version of State B, one that is tailored to the specific local conditions such as climate, terrain, and wildlife.

6. Establishing a B Kernel: The tender-bot starts by establishing a small, central garden – a kernel of State B. This kernel serves as the pilot project where it aims to actualize its localized vision of the future.

7. Growth Through Shells: From this kernel, the tender-bot incrementally extends its sphere of influence, creating concentric zones that not only are at different stages of achieving State B, but also serve as adaptation and interfacing zones with neighboring areas, which may have their own versions of State B appropriate to their specific locations.

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The concept of tender-bots introduces a potentially feasible and nature-aligned approach towards achieving global evolution. They could act as the embodiments of your original concept, working on the ground level to transform State A into various locally-tailored versions of State B.