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The Agent Challenge

AI Agents represent the frontier of Crypto × AI, operating with independence making decisions, and taking on-chain actions without human intervention. In 2023, we pioneered the idea of on-chain AI agents with Frenrug, an agent controlled by a combination of LLM and Classical ML models, managing over $30,000 on Base. Yet, while recent traction has pushed the agents market cap past $13B, current, leading agents and agent frameworks have regressed to poor, unsophisticated implementations, limited in their capabilities:
  1. Trust & Verification: No reliable way to verify autonomous operation or decision-making
  2. Limited Capabilities: Restricted action space and poor integration with external services
  3. Security Risks: Vulnerable to model and private key tampering and unauthorized access
  4. Centralized Control: Reliance on centralized providers for model and trade execution
  5. Privacy Concerns: User data and agent behavior exposed to central operators
  6. Poor Composability: Limited ability to coordinate between multiple agents
  7. Difficult Developer Experience: Poor framework architectures force simple, fragile agents
  8. Inadequate Architecture: Poor reproducibility, broken state consistency, unsafe parallelism

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Existing landscape

As a part of our exploration, we explored the existing Web2 and Web3 agents and agent framework and tooling ecosystems, closely studying projects including: elizaOS, AI Rig Complex, autogen, Coinbase Agent Kit, ZerePy, DSPy, multi-agent-orchestrator, GOAT SDK, Llama Agents, Heurist, Virtuals, Solana Agent Kit, CrewAI, TapeAgents, Nexus, Swarms, BabyAgi2, LangChain, Aether, Sora, Allice, Nous Agents, Cline, OpenAI Swarm, DuckAI. Our research identified several key patterns and challenges:
In the ideal world, agents follow a linear, reproducible architecture:
  1. Agent observes world state
  2. Agent updates internal state
  3. Agent takes actions
Yet, in our exploration, we found that many frameworks break this model by:
  1. Allowing observation components to take direct actions
  2. Hindering reproducibility of agent actions
  3. Operating unsafely in a parallel environment, causing undue race conditions
  4. Breaking state consistency across agent actions
  5. Interspersing sync and async agent processing

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Ritual’s Agent Framework

Ritual provides a comprehensive framework for building better agents, that are verifiable, truly autonomous, and cross-chain. On top, our upcoming agent launchpad simplifies the lifecycle of building and orchestrating complex agents, while tapping into the secure underlying execution properties of Ritual Chain.

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Enhanced Capabilities

Agents on Ritual live on-chain, with all execution logic (AI Inference, TEE code execution, wallet management, cross-chain trading, etc.) happening directly within a smart contract.

Verifiable Execution

Guaranteed autonomous operation with TEE execution and blockchain provenance.

Enshrined AI Inference

Secure, privacy-preserving AI inference powering real-time agent decision-making.

Autonomous Operation

Scheduled transactions orchestrate agent lifecycles without keepers or Web2 servers.

Cross-chain Composability

Chain Abstraction enables agents to take actions across any blockchain network.

Trade & Swap Agent Models

Model marketplaces allow users to trade best-performing agent models.

Enhanced Wallet Management

Native account abstraction enables secure wallet management and delegation.

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Autonomous Evolution

Ritual agents have self-improving capabilities:

On-chain adaptation with verifiable performance gains Adaptive strategy evolution via DAOs Self-healing through automated failure detection and recovery

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Multi-Agent Coordination

Ritual Chain acts as a trust-minimized coordination layer for agent-to-agent communication, enabling complex multi-agent systems with cryptographic guarantees.

Communication Layer

Secure message passing State synchronization Resource sharing Conflict resolution

Coordination Patterns

Hierarchical organization Peer-to-peer collaboration Task delegation Collective decision making

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Future Directions

We plan to continue to extend the capabilities of Ritual agents:
1

Automated Evolution

  • Automated skill acquisition
  • Experience synthesis
  • Adaptive specialization
2

Context Portability

  • Cross-{(chain, task)} knowledge transfer
  • Multi-domain expertise
  • Context adaptation
  • Skill composition

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Getting Started

You can get started building autonomous, smart agents on Ritual today. Our tutorials & case studies feature ways to build infinitely customizable on-chain agents, tapping into our native enshrined compute and robust primitives.