AAA GitBook
  • 🤝Welcome to AAA “C(H+A)RM”:
  • 1. Who We Are
  • 1.1 From 2015 to 2025
  • 1.2. History of AI CRM “C(H+A)RM” creation:
  • 1.3. The Why, The How
  • 1.4. Palo Alto AI WEB-3 Research Lab
  • 1.5. How Our AI Orchestration C(H+A)RM Chooses the Strongest Projects
  • 1.6. Platform Functionality. Orchestration CRM "C(H+A)RM" for AI-Agents
  • 1.7. Key Benefits of AI Agents
  • 1.8. How We Make Money
  • 1.9 Types of Peers
  • 1.10AI Agents Orchestration C(H+A)RM Startup Perks
  • 1.11. BACKERS + PARTNERS
  • 1.12. TradeMarks
  • 1.13. PIVOTs / "The only constant in life is CHANGES"Page
  • 1.15. Disclaimer
  • 2. WHY AI
  • 2.1. LLMs vs AI Agents
  • 2.2. Key Differences Between LLMs and AI Agents
  • 2.3. Energy-Efficient AI Agents Powered by Distilled LLMs
  • 2.4. Swarm Orchestration: AI Agents at Scale
  • 2.5. Decentralized Physical Infrastructure (DePIN) & Zero-Knowledge AI
  • 3. PLATFORM FUNCTIONALITY (The Ecosystem)
  • 3.1. Technology and Infrastructure
  • 3.2. ESCROW: VC’s Funds Escrow and Distribution to ProjectsPage
  • 3.3. Marketplace for AI Agents
  • 3.4. AI-Agent Creation Tools & Templates
  • 3.5. AI-Agent Labor Exchange
  • 3.6. Node Sales & Staking for AI Agents
  • 3.7. Reputation System: Trust & Accountability for AI Agents
  • 3.8. User Lifecycle in Our Ecosystem
  • 3.9. UGC: A User-Generated Content Platform for AI Agents
  • 3.10. AI-Agents + Crypto => onchain AI Agents
  • 4. What AI Agents you can launch with us
  • 4.1. DefAI = DeFi + AI Agents
  • 4.2. AI Agents for Blockchain Security
  • 4.3.AI Agents for "NO CODE dApps"
  • 4.4. Legal & Compliance AI Agents
  • 4.5. AI Agents for ESG & Sustainability
  • 4.6. More AI Agents for Web3
  • 5. Tokenomics, Token Sale, Nodes sale
  • 5.1. FAIR LAUNCH
  • 5.2. For VCs: Token Buy
  • 5.3. For VCs: Equity Sale
  • 5.4. Designed for Tier-1 CEXs
  • 5.5. $AAA Token Utility
  • 5.6. TGE — Q3
  • 5.7. Tokenomics
  • 5.8. Diamond Hands Distribution Program
  • 5.9. Revenue Share, Token Burning & Buyback Program
  • 5.10. Inflation & Deflation
  • 5.11. Sustainable Economy for Token Growth
  • 5.12. Monetization
  • 5.13. AI Agents as NFTs: Ownership, Privacy & Profit Sharing
  • 6. Roadmap - “This is a Way”
  • 6.1.âś… 2022 - Research, Networking & Early Development
  • 6.2.âś… 2023 - Building the Foundations
  • 6.3.âś… 2024 - AI Launchpad Development & Product Infrastructure
  • 6.4.🔄 2025 - Official AI Launchpad & Full Ecosystem Growth
  • 6.5.🔲 2026 - Scaling, Adoption & Enterprise AI Deployment
  • 6.6. Roadmap (Q-based)
  • 7. The Team, The DAO, The Roles
  • 7.1. Сustodians \ Treasury Co-Signers
  • 7.2. Co-founder
  • 7.3. Advisor for the Laboratory
  • 7.4. Mentor for Projects
  • 7.5. Judge at DemoDays
  • 7.6. Syndicate Member
  • 7.7 Team
  • 8. Frequently Asked Questions
  • 9. Official Links
  • 10. References that sparked our inspiration
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2.2. Key Differences Between LLMs and AI Agents

Previous2.1. LLMs vs AI AgentsNext2.3. Energy-Efficient AI Agents Powered by Distilled LLMs

Last updated 6 hours ago

AI agents are not just chat-enhanced LLMs—they are independent entities that think, act, and evolve. LLMs are powerful language-processing models, but they lack persistence, initiative, and decision-making capabilities. AI agents fill this gap, turning passive intelligence into active execution.

  1. Memory & State: Forgetful Brains vs Persistent Intelligence

    • LLMs: Every interaction is stateless. LLMs forget everything once the session ends. They struggle with personalization and multi-step problem-solving.

    • AI Agents: They store both short-term and long-term memory, adjusting their strategies over time and improving their responses based on past interactions.

“LLMs are like a goldfish—each new interaction is a blank slate. AI agents? They remember and evolve.”

  1. Proactivity & Goals: Passive Tools vs Autonomous Execution

  • LLMs: They wait for instructions. LLMs can’t initiate actions or complete tasks autonomously—they only respond.

  • AI Agents: Goal-oriented and proactive. They take action, seek information, and execute tasks without waiting for a prompt.

“LLMs will never do anything unless you tell them to. AI agents act with intent, they don’t need permission to move.”

  1. Role & Fine-Tuning: General-Purpose Models vs Specialized Operators

  • LLMs: General-purpose models that require extensive fine-tuning to specialize. They are versatile but need guidance for specific roles.

  • AI Agents: Pre-built with defined roles and objectives, ready to solve real-world problems from day one.

“LLMs are like fresh grads—capable but need a manager. AI agents are seasoned experts—ready to roll.”

  1. Decision-Making & Learning: Static Predictions vs Adaptive Intelligence

  • LLMs: Pre-trained and static. Their responses are fixed based on their training data, without the ability to learn from new experiences.

  • AI Agents: Adaptive and learn from interactions. They continuously optimize decision-making based on real-time feedback.

“LLMs are knowledge databases. AI agents? Evolving, learning, and continuously improving like living organisms.”