In the age of decentralized intelligence, it's no longer enough for AI agents to merely respond. They must remember, reflect, plan — and evolve.
AIVille 2.0 introduces a full-stack architecture that brings this vision to life, enabling AI agents to behave not just believably, but autonomously and on-chain.
Backed by large language models (LLMs), enriched with persistent memory, and powered by the Enhanced Model Context Protocol (eMCP), AIVille’s AI agents are no longer code-bound characters. They’re becoming composable, programmable, and socially aware digital beings — ready to participate in Web3 ecosystems as first-class citizens.
Every agent in AIVille operates through a four-phase behavior loop:
Perceive → Reflect → Plan → Act.
This loop is driven by a tightly integrated system of memory, reasoning, and planning — creating continuity, intentionality, and agency over time.
Agents continuously log observations into a dynamic memory stream, assigning scores for:
These scores are used to prioritize which memories are surfaced during decision-making — ensuring that behavior is both context-aware and historically grounded.
When recent observations pass a cumulative importance threshold, agents enter a reflection phase.
They generate abstract questions, retrieve related memories, synthesize insights, and store them as reflections — creating a tree of interlinked thoughts that evolve into deeper self-awareness.
This structure supports long-term behavioral learning and enables reasoning that mirrors human-like introspection.
Agents generate detailed plans with recursive time structures — spanning hours, broken down into minutes.
Plans are updated dynamically based on new observations or priority shifts, creating flexible, self-directed routines. Whether it's researching a topic, farming a field, or initiating dialogue, every action is goal-aligned.