DOWHAT DEV
INFRA
DoWhat - Infrastructure
The humanized, execution-driven cognitive model developed by DOWHAT.ME will bring groundbreaking impacts to AI applications, driving transformative advancements across the field.
Personal Digital Clone Protocol (PDCP)
The Personal Digital Clone Protocol (PDCP) is a standardized protocol designed to enable individuals to own and control their personal digital clones. It serves as the foundational framework for human-like AI automation, data sovereignty, and cross-platform intelligent services. PDCP facilitates the creation, management, and orchestration of personal digital clones, endowing them with “human-like reasoning and execution capabilities” while ensuring secure cross-platform functionality spanning both Web2 and Web3 ecosystems.
1. Primary Objectives of PDCP
Provide each user with a highly personalized and intelligent digital clone/digital proxy.
Enable the digital clone to autonomously handle a wide range of tasks, such as social interactions, scheduling, payments, on-chain operations, and data management, based on user intent.
Empower users to control their data, permissions, and the behavior of their digital clones through PDCP.
2. Key Features of PDCP
Chain of Thought (CoT): A core logical framework ensuring the digital clone operates with human-like “thinking-reasoning-decision-execution” workflows.
Unified Identity and Permissions: Enables the digital clone to manage multiple identities, wallets, and accounts with secure authorization.
Cross-Platform Capability: Allows seamless invocation of Web2 and Web3 services (e.g., social media, shopping, wallets, smart contracts).
Data Sovereignty: Users maintain full control over their personal data and the actions of their digital clone.
Programmability and Extensibility: The protocol supports third-party developers in creating new capabilities for the digital clone ecosystem.
3. Role of PDCP in DOWHAT.ME
PDCP functions as the operating system-level protocol for DOWHAT.ME, forming the backbone of its architecture. It enables each user's digital clone to emulate human-like decision-making, perform autonomous execution, and securely empower cross-platform services.
Chain of Thought (CoT)
The Chain of Thought (CoT) is a critical component of PDCP and serves as the intelligent reasoning framework of DOWHAT.ME. CoT refers to the AI’s ability to solve problems or execute tasks by breaking them into a series of logical steps, where each step is guided by structured reasoning, judgment, and actionable insights. In PDCP, CoT goes beyond basic reasoning, embodying an integrated “thinking-decision-action” process that mirrors human cognition.
Key Components of CoT
A. Perception and Input Understanding
Processes user instructions, environmental data, and historical records to generate structured perceptual representations.
B. Intent Parsing and Goal Setting
Extracts the user’s deep intent and establishes clear objectives or sub-goals.
C. Task Decomposition and Step Planning
Deconstructs complex goals into multi-step executable tasks. Combines historical preferences and context to identify the best pathway for each step. Supports various chain structures such as parallel, serial, and conditional branching.
D. Reasoning and Decision-Making
For each task, integrates personal habits, real-time environmental factors, and external variables to make human-like judgments and choices. Dynamically adjusts steps, priorities, and even allows for “skipping” or “rollback” actions as necessary.
E. Multi-Agent Collaboration
Automatically coordinates multiple agents and platforms when tasks span diverse ecosystems, ensuring synchronized state management.
F. Action and Execution
Invokes MCP services to automate operations across Web2 and Web3 platforms, such as placing orders, making payments, and sending notifications.
G. Feedback and Self-Correction
Continuously refines the CoT workflow based on execution outcomes, user feedback, and external changes, ensuring task completion.
H. Memory and Growth
Records every execution, its outcomes, and feedback to optimize future decision-making and refine task workflows.
Why CoT is Ideal for AI Execution？
A. Human-Like Decision-Making for Enhanced Intelligence
Human decision-making involves multi-step reasoning and dynamic adjustments. CoT equips AI with similar “think-judge-act-correct” capabilities for comprehensive intelligence.
B. Task Decomposition and Traceability for Complex Scenarios
Complex tasks (e.g., travel planning, cross-chain payments, automated workflows) require multi-step coordination. CoT naturally supports task decomposition, state tracking, and error recovery.
C. Adaptability and Robustness for Real-World Variability
The real world is full of uncertainties. CoT dynamically adjusts execution paths based on real-time feedback, significantly enhancing adaptability and resilience.
D. Cross-Platform and Multi-Agent Collaboration
CoT inherently facilitates the distribution of complex tasks across multiple platforms and agents, maximizing resource utilization across Web2 and Web3 ecosystems.
E. Personalization and Growth
CoT deeply integrates user preferences and historical behaviors, creating a highly personalized AI execution style. Continuous learning allows the system to evolve over time.
CoT's Role in Enhancing AI Execution
The CoT framework enables AI to perform dynamic reasoning and decision-making akin to human cognition, significantly enhancing its ability to handle complex tasks, adapt to changing environments, and evolve personalized execution styles. It serves as the ideal foundation for AI execution and is the central intelligence framework of DOWHAT.ME’s ERE-Bob.
