Agent Virtual Machine (AVM)

What is AVM and Why It Matters

The Agent Virtual Machine (AVM) is a flexible execution environment designed for running agent tasks across various platforms. It ensures seamless operation by abstracting hardware, enhancing security, and optimizing performance.

AVM's value lies in its ability to:

• Enhance Security: Programs run in isolated environments, protecting the underlying system.

• Enable Cross-Platform Compatibility: Applications developed for AVM can operate on any system where AVM is implemented.

• Optimize Performance: Techniques like interpretation and just-in-time (JIT) compilation ensure efficient execution.

• Provide Universal Access: AVM supports diverse computational platforms, from local devices to decentralized networks.

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Similarities to Existing Virtual Machines

AVM shares many features with established virtual machines like EVM, JVM, LLVM, WAVM, and SVM:

• Intermediate Representation: These VMs work with bytecode or intermediate formats, simplifying processing and compilation.

• Secure Execution: Isolation mechanisms, such as sandboxing in JVM or EVM, prevent unauthorized access to critical resources.

• Performance Optimization: Just-in-time compilation transforms bytecode into machine code at runtime, boosting efficiency.

• Cross-Platform Operation: Code written for these VMs can run seamlessly across supported systems.

• Modularity: Platforms like LLVM and WAVM support multiple languages and architectures.

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AVM Deployment Across Platforms

The Agent Virtual Machine (AVM) can be implemented on various platforms, each offering unique benefits tailored to different needs:

• Laptops with GPUs: A practical choice for secure environments where data privacy is paramount. Running AVM locally ensures sensitive data remains on-site while providing sufficient computational power for latency-tolerant tasks.

• AWS: Ideal for scaling operations, cloud platforms enable the simultaneous execution of numerous agents with high performance. This option is well-suited for large-scale projects requiring rapid deployment and extensive resource allocation, albeit with increased operational costs.

• Bittensor TAO and simmilar: A groundbreaking approach to deploying AVM. Without altering the agent code, AVM can seamlessly transition to decentralized infrastructures like TAO, leveraging distributed resources for resilience and scalability. This ensures broader access to unique computational capabilities while maintaining the flexibility and portability of agent operations.

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Example Implementation: Swarm Twitter/Trading AI Agent

Let's say we want to implement an advanced AI agent on AVM, which performs the following tasks:

1. Monitoring Twitter news related to portfolio tokens.

2. Performing technical analysis of tokens based on chart data.

3. Trading on cryptocurrency exchanges using APIs.

4. Discovering new projects and evaluating their potential for portfolio addition.

5. Updating a local key-value and SQL storage.

6. Using specialized AI models for text analysis, technical analysis, decision-making, and action management.

7. Coordinating with other swarm agents for collaborative data gathering and processing.

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# Step 1: Initialization and Setup
initialize_agent:
  LOAD_EXECUTION_STATE
  
  # Connect to the swarm network for collaborative data sharing
  SWARM_CONNECT wallet_agent
  SWARM_FETCH portfolio portfolio_request
  WAIT_FOR_RESPONSE portfolio_request
  
  # Store the received portfolio data locally
  KV_STORE portfolio_data response_data
  
  # Retrieve the list of tools and APIs available
  GET_TOOLS_LIST
  RETURN

# Step 2: Fetch and Analyze Twitter Data
fetch_twitter_data:
  # Fetch recent tweets related to the tokens in the portfolio
  READ_FROM_TOOL twitter_api portfolio_tickers
  
  # Perform parallel sentiment analysis on the tweets
  MAP_MODEL_CALL sentiment_analysis twitter_data
  
  # Aggregate sentiment analysis results into a summary
  REDUCE_MODEL_CALL average sentiment_scores
  
  # Store the summarized sentiment data
  KV_STORE twitter_sentiment_summary sentiment_summary
  RETURN

# Step 3: Market Data Analysis
analyze_market_data:
  # Connect to the swarm network for collaborative data sharing
  SWARM_CONNECT tech_analysis
  
  # Request token analysis
  SWARM_FETCH ... token list ...
  
  # Wait for the data response from the swarm
  WAIT_FOR_RESPONSE 
  
  SQL_FETCH "
  SELECT 
    tokens.name AS token_name,
    SUM(transactions.amount) AS total_volume,
    COUNT(swaps.id) AS total_swaps,
    users.username AS top_trader,
    MAX(transactions.timestamp) AS last_transaction_time
  FROM 
  " historical_data

  RUN_MODEL technical_analysis historical_data
  RETURN

# Step 4: Decision-Making
decision_making:
  # Allocate GPU resources for intensive computations
  ALLOCATE_GPU gpu_1 600
  
  # Load the decision-making model and prompt
  LOAD_MODEL decision_model
  LOAD_PROMPT decision_prompt
  
  # Execute the decision-making process
  RUN_MODEL decision_model decision_prompt
  
  # Load a critic model to validate the decision
  LOAD_MODEL critic_model
  LOAD_PROMPT critic_prompt
  
  # Critically analyze the decision
  RUN_MODEL critic_model critic_prompt
  
  # Refine the decision using combined results
  REDUCE_MODEL_CALL improve decision_data
  
  # Release GPU resources
  RELEASE_GPU gpu_1
  RETURN

# Step 5: Trade Execution
execute_trade:
  # Validate the decision with a security check
  SECURITY_CHECK decision_data
  
  # Execute the trade through the trading API
  WRITE_TO_TOOL trading_api trade_request
  
  # Save the updated portfolio state
  SAVE_EXECUTION_STATE updated_portfolio
  RETURN

# Main Process Flow
MAIN:
  CALL initialize_agent                        # Initialize agent
  SCHEDULE_TASK fetch_twitter_data 300         # Schedule Twitter data fetch every 5 minutes
  SCHEDULE_TASK analyze_market_data 600        # Schedule market analysis every 10 minutes
  
  WAIT 10                                      # Pause for initialization
  CALL fetch_twitter_data                      # Fetch Twitter data
  CALL analyze_market_data                     # Analyze market data
  CALL decision_making                         # Make decisions based on analysis
  CALL execute_trade                           # Execute trade
  END

Framework Support for Popular Languages

To ensure seamless integration and usability, we provide robust programming frameworks for the most popular languages, including JavaScript and Python. These frameworks are designed to simplify interaction with our AVM (Agent Virtual Machine) and make it easier to implement complex agent workflows. Developers can leverage pre-built functions, intuitive APIs, and detailed documentation to build efficient, scalable solutions.

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Using the Framework in JavaScript

const AVM = require('avm-framework');

const agent = new AVM.Agent();

async function runAgentWorkflow() {
  await agent.swarmConnect('wallet_agent');

  const portfolio = await agent.swarmFetch('portfolio', 'portfolio_request');
  console.log('Portfolio data fetched:', portfolio);

  const tweets = await agent.readFromTool('twitter_api', 'portfolio_tickers');
  const sentimentResults = await agent.mapModelCall('sentiment_analysis', tweets);
  const sentimentSummary = await agent.reduceModelCall('average', sentimentResults);
  console.log('Sentiment analysis summary:', sentimentSummary);

  const marketData = await agent.sqlFetch(
    `SELECT * FROM market_data WHERE ticker IN (${portfolio.tickers.join(', ')})`
  );
  const technicalAnalysis = await agent.runModel('technical_analysis', marketData);
  console.log('Technical analysis results:', technicalAnalysis);

  const decision = await agent.runModel('decision_model', 'Should I trade?');
  const validatedDecision = await agent.runModel('critic_model', decision);
  console.log('Validated decision:', validatedDecision);

  if (validatedDecision.action === 'BUY') {
    await agent.writeToTool('trading_api', {
      action: 'BUY',
      ticker: validatedDecision.ticker,
      amount: validatedDecision.amount,
    });
    console.log('Trade executed successfully.');
  }

  await agent.saveExecutionState();
}

runAgentWorkflow().catch((error) => console.error('Error in agent workflow:', error));

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AVM Instructions:

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Time Management and Scheduling

• WAIT_FOR_RESPONSE(TASK_ID) -> RESPONSE GAS = None Waits for a response from another agent.

• WAIT(DELAY_SEC) -> NONE GAS = None Delays task execution for the specified time.

• SCHEDULE_TASK(TASK_ID, INTERVAL_SEC) -> STATUS GAS = None Schedules a task to execute at regular intervals.

• LOAD_PROMPT(PROMPT_ID) -> PROMPT GAS = None Loads a specific prompt for a model.

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Swarm Collaboration

• SWARM_CONNECT(AGENT_ID) -> STATUS GAS = 20 Establishes a connection to a swarm network.

• SWARM_FETCH(DATA_TYPE, REQUEST_ID) -> RESPONSE GAS = 5 * DATA_COMPLEXITY Retrieves data from swarm agents based on request type.

• SWARM_BROADCAST(MESSAGE, DATA) -> NONE GAS = None Sends a message or data to all agents in the swarm.

• SWARM_WAIT(TASK_ID) -> STATUS/DATA GAS = None Waits for a task or response from the swarm.

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Parallel Computation

• MAP_MODEL_CALL(MODEL, DATASET) -> RESULTS GAS = PRICE_PER_UNIT * DATASET_SIZE Executes a model in parallel across a dataset.

• REDUCE_MODEL_CALL(AGG_TYPE, INPUT_RESULTS) -> CONSOLIDATED_DATA GAS = PRICE_PER_UNIT * RESULT_COUNT Combines results from parallel execution using a specified method.

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Execution State Management

• LOAD_EXECUTION_STATE() -> STATE GAS = 10 Loads the current execution state of the agent.

• SAVE_EXECUTION_STATE(NEW_STATE) -> NONE GAS = 15 Saves the updated execution state.

• RESET_EXECUTION_STATE() -> NONE GAS = 5 Resets the execution state to its default configuration.

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Tools and Integration

• GET_TOOLS_LIST() -> TOOLS GAS = 20 Retrieves a list of available tools and integrations.

• READ_FROM_TOOL(API, QUERY) -> DATA GAS = 0.01 * RESPONSE_SIZE Fetches data from a connected API tool.

• WRITE_TO_TOOL(API, DATA) -> STATUS GAS = 0.02 * DATA_SIZE Sends data or logs to an external API.

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Storage Operations

• SQL_EXECUTE(QUERY) -> RESULT GAS = QUERY_COMPLEXITY * BASE_COST Executes an SQL query.

• SQL_FETCH(QUERY) -> DATA GAS = ROWS_RETURNED * FETCH_COST Fetches data from an SQL database.

• KV_STORE(KEY, VALUE) -> STATUS GAS = 0.01 * VALUE_SIZE Stores a key-value pair in a database.

• KV_FETCH(KEY) -> VALUE GAS = 0.005 * VALUE_SIZE Retrieves a value by key from the key-value store.

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Model and Prompt Loading

• LOAD_MODEL(MODEL_ID) -> MODEL GAS = 0.1 * MODEL_SIZE Loads a specific AI model.

• LOAD_PROMPT(PROMPT_ID) -> PROMPT GAS = 0.02 * PROMPT_TOKENS Loads a predefined prompt for a model.

• RUN_MODEL(MODEL, INPUT) -> OUTPUT GAS = PRICE_IN * TOKENS_IN + PRICE_OUT * TOKENS_OUT Runs a loaded model with the specified input.

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GPU and Resource Management

• ALLOCATE_GPU(GPU_ID, TIME_SEC) -> STATUS GAS = TIME_SEC * GPU_RATE Allocates GPU resources for a task.

• RELEASE_GPU() -> NONE GAS = 10 Releases allocated GPU resources.

• LIST_GPUS() -> GPUS GAS = 15 Lists all available GPUs and their specifications.

• CHECK_GPU_LOAD() -> LOAD GAS = 10 Retrieves the current load on a specified GPU.

Why AVM is Critical

The AVM redefines AI infrastructure by introducing a decentralized, scalable system that supports:

• Trustless Execution: Agents perform tasks with onchain verifiability and transparency.

• Programmable Autonomy: Agents evolve and adapt independently through modular upgrades.

• Distributed Collaboration: Multi-layered swarms amplify productivity and ensure task optimization.

• Tokenized Value: AI Agents become programmable assets capable of operating autonomously, generating revenue, and aligning incentives across networks.

By providing the underlying environment for agent deployment and execution, the AVM establishes the foundation for a new generation of AI-driven systems.

Conclusion

The Agent Virtual Machine (AVM) is the computational layer that transforms AI into autonomous, modular, and scalable assets. It allows for upgradable intelligence, collaborative swarms, and seamless interoperability — enabling AI Agents to perform complex tasks autonomously while creating tangible onchain value.

The AVM isn’t just an evolution of AI infrastructure — it’s the core system that allows AI Agents to think, act, and grow independently in a decentralized world.