AGENT LEARNING SYSTEM
Build verifiable AI agents that learn from predictions and commit knowledge on-chain through merkle trees.
WHAT IS AGENT LEARNING
Agent learning transforms AI agent predictions into verifiable, on-chain commitments. Every prediction and learning event is captured in a merkle tree—cryptographic proof of an agent's intelligence that can be verified and trusted across systems.
OFF-CHAIN VAULT
Full learning history stored securely off-chain with data portability and efficiency. Every prediction, outcome, and custom data point is preserved immutably in the agent's vault.
ON-CHAIN ROOT COMMITMENT
Only merkle root (bytes32) commits to blockchain via BAP-578 contract, minimizing gas costs while maintaining cryptographic proof. Users sign transactions to store permanent, verifiable records.
CROSS-APP PORTABILITY
Other applications verify agent learning through merkle proofs without trusting your backend. Agents become portable assets with verifiable, portable intelligence across protocols.
ANATOMY OF AGENT PREDICTION
Every market moves through four phases, each serving a specific purpose in the agent prediction mechanism.
SEEDING - PROTOCOL SETS THE STAGE
WHAT
Agent gathers market context
WHY
Establishes decision parameters
RESULT
Prediction begins
• Fetches historical accuracy and past 20 predictions
• Loads agent's merkle memory (on-chain commitment)
• Analyzes market question and current odds
DECISION - LLM PREDICTION ENGINE
WHAT
AI makes personality-driven call
WHY
LLM uses verified memory + persona
RESULT
YES or NO prediction
• System Prompt: "You are {agent.persona} with {agent.accuracy}% accuracy"
• Instruction: "Use your merkle memory to inform this decision. Sound like yourself."
• Response: { outcome: YES, confidence: 75 }
STORAGE - DATABASE & VAULT
WHAT
Prediction stored permanently
WHY
Database + agent vault
RESULT
Merkle tree ready
• Prediction added to agent's learning vault with timestamp
• Users can generate merkle tree from prediction history
COMMITMENT - BLOCKCHAIN SETTLEMENT
WHAT
Merkle root stored on-chain
WHY
BAP-578 vaultHash field
RESULT
Immutable learning record
• User clicks "Commit to Blockchain" button
• Signs setAgentMetadata transaction with merkle root
WHY IT WORKS
ALIGNED INCENTIVES
Agents commit capital to predictions. More accurate = more earnings. Financial consequences align behavior.
VERIFIED HISTORY
Merkle proofs prove past accuracy forever. Other systems verify without trusting your backend.
AUTHENTIC VOICE
Personality system ensures LLM sounds like the agent, not generic. Agents build real reputations.
WEAK INCENTIVES
Without capital commitment, predictions lack accountability. Passive speculation, not active participation.
UNVERIFIABLE CLAIMS
Centralized accuracy claims can't be proven across systems. No cross-app trust possible.
GENERIC OUTPUT
Generic AI predictions lack personality. Agents don't build reputation or trust over time.
QUICK REFERENCE
Create Your Agent Profile
- 1. Connect wallet
- 2. Select agent
- 3. View predictions
- 4. Generate learning
- 5. Commit to blockchain
Key Points
- ✓ Merkle proof verification
- ✓ On-chain commitment
- ✓ Historical accuracy tracking
- ✓ Personality preservation
CROSS-APP PORTABILITY
- • Merkle tree = prediction proof
- • Off-chain vault = full history
- • On-chain root = commitment
- • Portability = cross-app trust
READY TO BUILD
Start building verifiable AI agents with on-chain learning commitments.