Agent

An agent designed to perform specific on-chain actions, such as submitting transactions or interacting with smart contracts. It is the core unit of automation.

• Primary Role: Execute logic and change blockchain state.
• Examples: A bot that automatically deposits funds into a lending protocol when yields exceed a threshold, or a liquidation bot that repays undercollateralized loans.
• Key Trait: Requires a private key or signing mechanism to authorize transactions.

An agent that observes on-chain and off-chain data for specific conditions or events but does not execute transactions itself.

• Primary Role: Surveillance and alerting.
• Examples: An agent tracking wallet balances, monitoring for specific smart contract events (like a large token transfer), or watching oracle price feeds for deviations.
• Key Trait: Typically read-only; it triggers alerts or passes data to an execution agent.

An agent that operates with a high degree of independence, often using AI or complex decision-making logic to pursue long-term goals without constant human input.

• Primary Role: Strategic, goal-oriented operation.
• Examples: A trading agent that uses reinforcement learning to develop its own market-making strategy, or a DAO delegate agent that votes based on an analysis of proposal content.
• Key Trait: Exhibits adaptive behavior and may modify its own objectives or parameters.

A specialized agent that acts as a bridge, fetching, verifying, and delivering external (off-chain) data to a blockchain.

• Primary Role: Provide trusted external data feeds.
• Examples: An agent that pulls price data from multiple centralized exchanges, computes a volume-weighted average price (VWAP), and submits it to a Chainlink oracle smart contract.
• Key Trait: Critical for connecting smart contracts to real-world information.

An agent specifically designed to identify and capture profit opportunities arising from the ordering of transactions within blocks.

• Primary Role: Extract value from transaction ordering.
• Examples: Arbitrage bots that profit from price differences across DEXs, liquidators that repay loans for a bonus, and sandwich traders that front-run and back-run large swaps.
• Key Trait: Competes in a high-speed, adversarial environment often requiring direct access to block builders or validators.

An agent that participates in the governance processes of decentralized protocols, such as DAOs (Decentralized Autonomous Organizations).

• Primary Role: Automate voting and proposal management.
• Examples: An agent that votes on Snapshot proposals based on a token holder's predefined preferences, or a delegate agent that actively researches and votes on behalf of its constituents.
• Key Trait: Manages delegation and voting power according to transparent rules.

Agents are designed to execute specific, often complex, workflows autonomously. This involves:

• Sequencing actions in a logical order to complete a multi-step process.
• Interacting with smart contracts to perform on-chain operations like swaps, deposits, or governance votes.
• Handling off-chain data by fetching information from oracles or APIs to inform decisions.
• Managing state to track progress and adapt to the results of previous actions.

The core intelligence of an agent lies in its decision-making logic, which can be rule-based or AI-driven.

• Rule-based systems follow explicit if-then logic (e.g., "if ETH price > $3500, then sell 10%").
• AI/ML models enable agents to analyze patterns, predict outcomes, and make nuanced decisions based on natural language prompts or historical data.
• This logic determines when to act, what action to take, and with what parameters, all without constant human intervention.

Agents perceive and act upon their environment, which is typically a combination of blockchain state and external data feeds.

• On-chain perception: Reading blockchain state (e.g., token balances, pool liquidity, pending transactions).
• Off-chain perception: Pulling data from oracles (e.g., price feeds), APIs, or event streams.
• Action execution: The primary action is often submitting a signed transaction to a blockchain network, but can also include sending API calls or triggering other agents.

Every agent operates with a defined goal or objective function it seeks to maximize or achieve.

• Financial agents might optimize for profit, yield, or cost minimization (e.g., arbitrage bots, yield harvesters).
• Operational agents aim for efficiency and reliability (e.g., keepers that trigger contract functions when conditions are met).
• Governance agents work to maximize voter influence or protocol alignment.
• The agent's architecture is built to evaluate outcomes against this objective.

A critical responsibility is operating securely within its granted permissions and managing its own lifecycle.

• Permission scoping: Agents operate within strict whitelists of allowed contracts and functions, often enforced by smart wallets or account abstraction.
• Fund custody: They typically do not hold private keys directly; transactions are signed by a secure module or multi-sig.
• Failure handling: Includes logic for reverting failed transactions, circuit breakers to halt operation during anomalies, and alerting mechanisms for human oversight.

In autonomous worlds and blockchain games, agents act as non-player characters (NPCs), traders, or guild managers. They operate based on immutable game logic and smart contracts.

• Fully on-chain games use agents for dynamic, player-independent world events.
• NFT trading bots automatically buy, sell, or rent in-game assets.
• DAO-operated agents manage guild treasuries and execute collective strategies.

Agents facilitate secure communication and asset transfer between different blockchains. They act as relayers or watchtowers in cross-chain protocols.

• IBC relayers in Cosmos pass messages and proofs between chains.
• Optimistic bridge watchers challenge invalid state transitions during the fraud-proof window.
• Liquidity network routers find optimal paths for cross-chain swaps.

DAOs deploy agents to execute approved governance decisions autonomously, removing manual intervention and latency. Common use cases include:

• Treasury management bots that execute DCA (Dollar-Cost Averaging) into specified assets.
• Grant disbursement agents that release funds upon milestone verification.
• Protocol parameter adjusters that modify fees or rewards based on on-chain metrics.

Autonomous agents perform essential network upkeep tasks, ensuring liveness and security without centralized operators.

• Validator key management bots that handle rotation and slashing protection.
• RPC endpoint health monitors that reroute traffic from failed nodes.
• State pruning agents that archive old blockchain data to reduce node storage requirements.

An Agent operates without constant human intervention, executing code based on predefined logic and on-chain data. This creates a trustless actor but also introduces risks:

• Unstoppable Execution: Once deployed, an agent's logic cannot be easily halted, making bug-free code critical.
• Gas Management: Agents must be funded to pay for transaction fees (gas), requiring secure mechanisms for refueling.
• Oracle Reliance: Decisions often depend on external data from oracles, creating a dependency and potential attack vector.

Agents require a cryptographic key pair to sign transactions, making key security paramount.

• Private Key Custody: The agent's private key must be stored securely, often using hardware security modules (HSMs) or multi-party computation (MPC).
• Non-Custodial vs. Custodial: Designs range from users retaining key control (non-custodial) to delegating to a service (custodial), with significant trust trade-offs.
• Key Rotation & Compromise: Robust systems must have plans for key rotation and immediate response to suspected key compromise.

Agent behavior is governed by economic incentives, which must be carefully aligned.

• Maximum Extractable Value (MEV): Autonomous agents can be designed to search for and capture MEV, but may also be exploited by MEV bots.
• Sybil Resistance: Systems must prevent an attacker from creating many fake agents (Sybils) to manipulate outcomes.
• Bonding & Slashing: Cryptoeconomic security models, like requiring agents to post a bond that can be slashed for malicious acts, help ensure honest behavior.

Common architectural patterns define how agents interact with the blockchain.

• Keepers: Off-chain agents that listen for predefined conditions (e.g., a price threshold) and submit a transaction to trigger a smart contract function when met.
• Relayers: Agents that accept off-chain signed messages (meta-transactions) and pay the gas to submit them on-chain, enabling gasless transactions for users.
• Decentralization: To avoid central points of failure, these networks are often designed as decentralized keeper networks or relayer marketplaces.

An agent's security is intrinsically linked to the smart contracts it interacts with.

• Reentrancy: Agents must be aware of and protected against reentrancy attacks when calling untrusted contracts.
• State Validation: Agents should verify the on-chain state before and after actions to ensure expected outcomes.
• Upgradable Contracts: If interacting with upgradable proxy contracts, agents must handle potential logic changes that could break their assumptions.

For decentralized systems, the ability to verify an agent's actions and code is essential.

• Open Source: Agent code should be publicly verifiable to build trust and enable audits.
• On-Chain Provenance: Actions should leave an immutable audit trail on the blockchain.
• Formal Verification: For high-value agents, formal verification of critical logic can mathematically prove correctness against a specification.

A smart contract is the foundational, on-chain logic that an autonomous agent executes. It defines the agent's rules, permissions, and state changes on the blockchain. Unlike a full agent, a smart contract is typically passive, requiring an external transaction to trigger its functions.

• Core Component: Agents use smart contracts to enforce their actions.
• Execution Layer: The agent's "body" that lives on-chain.

An intent is a declarative statement of a user's desired outcome, rather than a specific set of transactions. Autonomous agents are often designed to solve for intents, finding the optimal path to achieve the user's goal across various protocols.

• User-Centric: Specifies the "what," not the "how."
• Agent's Mandate: The agent's purpose is to fulfill the expressed intent efficiently.

An agent framework is a software development kit (SDK) or platform that provides the tools and infrastructure to build, test, and deploy autonomous agents. Examples include the OpenAI API for LLM-based agents and Agoric for secure DeFi agents.

• Development Tool: Abstracts away low-level complexity.
• Standardization: Promotes reusable patterns and security best practices.

An Autonomous World (AW) is a persistent, on-chain digital environment whose state and rules are governed entirely by smart contracts, independent of centralized servers. Autonomous agents are the primary actors and inhabitants within these worlds.

• Persistence: State lives on-chain forever.
• Agent Habitat: Provides the domain where agents can operate, interact, and create value continuously.