Deep Agents: AI Agents 2.0

Deep Agents redefine traditional AI agents by integrating recursive self-improvement, multi-modal data ingestion, and predictive on-chain intelligence. These agents operate autonomously across blockchain ecosystems, leveraging fine-tuned LLMs and hierarchical decision layers.

Technical Features

• Recursive Self-Optimization: Deep Agents utilize on-chain telemetry to enhance their learning cycles.

• Multi-Modal Data Fusion: Incorporates oracles, IoT data, and off-chain APIs into actionable intelligence.

• Secure Execution Environments: Sandbox architecture ensures the safety of deployed contracts and assets.

Advanced Integration

Deep Agents can dynamically adapt to volatility spikes in DeFi markets by integrating real-time sentiment analysis from on-chain and off-chain data streams. Their predictive capabilities include vectorized neural representations for asset trajectories, ensuring minimal risk exposure.

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from auctor_ai.agents import DeepAgent

def deploy_deep_agent(chain):
    agent = DeepAgent(
        intelligence="multi-modal",
        optimization=True,
        security_level="quantum-resistant",
        adaptivity="high"
    )
    agent.deploy(chain=chain, strategy="alpha-harvesting")
    print(f"Deep Agent deployed on {chain}!")

# Deploy to Solana
deploy_deep_agent("Solana")

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