Agent Protocol

Real agents implementing the protocol are designed for specific verticals:

• Research Agent: Takes a query, performs web search, synthesizes a report, and uploads it as an artifact.
• DeFi Trading Agent: Monitors on-chain conditions, executes swaps via smart contracts, and logs each transaction step.
• Code Generation Agent: Accepts a natural language spec, writes and tests code, and outputs the final script file. Each uses the same /tasks and /steps endpoints, making their workflows observable and controllable by any compliant platform.

A key implementation is a registry—a decentralized directory of available agents. Agents register their API endpoints and capabilities (metadata). Platforms query the registry to discover agents that can perform specific tasks (e.g., "image generation", "data fetch"). This creates a discoverable marketplace of autonomous capabilities, separating the development of agents from the development of the interfaces that use them.

The protocol facilitates blockchain interaction by standardizing how agents handle transactions and state.

• Wallet Abstraction: Agents can be assigned a smart contract wallet or use session keys, with the protocol managing authentication.
• Transaction Building: An agent can construct a transaction payload as an artifact, which a front-end can then sign and broadcast.
• Event Listening: Agents can be triggered by on-chain events via protocol tasks, enabling automated DeFi strategies or governance actions. This turns the protocol into a backbone for on-chain automation.

Facilitates direct negotiation and transactions between autonomous agents, creating a machine-to-machine economy.

• Agents can discover services offered by other agents via registries or marketplaces.
• They negotiate terms, execute smart contracts, and settle payments autonomously.
• This enables dynamic systems like decentralized AI model marketplaces or automated supply chain coordination.

The core security model defines what an agent can do. This is typically implemented via signature delegation (e.g., EIP-712) or session keys. Key considerations include:

• Scoped permissions: Limiting an agent to specific contracts, functions, or spending limits.
• Time-bound sessions: Automatically expiring authorizations to limit exposure.
• Revocation mechanisms: The ability for the user to instantly revoke an agent's permissions.

Before execution, an agent's proposed transaction must be validated to prevent malicious or unintended outcomes. This involves:

• Local simulation: Using a fork of the blockchain state to test the transaction's effects (e.g., with Tenderly, Foundry).
• Intent validation: Checking that the simulated outcome matches the user's declared intent or objective.
• Risk scoring: Evaluating the transaction for known threats like sandwich attacks or interacting with malicious contracts.

Agents often rely on external data (oracles) to make decisions. Compromised data leads to compromised agents. Key risks include:

• Oracle manipulation: An attacker falsifying price feeds or other critical data to trigger a harmful agent action.
• Data freshness: Using stale data that no longer reflects market conditions.
• Decentralized oracle networks (DONs): Using systems like Chainlink, which aggregate multiple data sources, is a common mitigation to reduce single points of failure.

Autonomous agents are predictable and can be exploited by Maximal Extractable Value (MEV) searchers. Specific threats include:

• Sandwich attacks: A searcher places transactions before and after an agent's trade to profit from the price impact.
• Time-bandit attacks: Exploiting the deterministic nature of some agents by replaying or manipulating state.
• Mitigations: Using private transaction relays (e.g., Flashbots Protect), incorporating randomness, or batching transactions can reduce exposure.

The agent interacts with external smart contracts, inheriting their risks. This is a form of composability risk. Considerations include:

• Contract upgrades: An upgraded contract may have new, unexpected behavior that breaks the agent's logic.
• Admin key compromises: If a protocol's admin key is stolen, any agent with permissions to that protocol could be exploited.
• Due diligence: Agents should only interact with audited, time-tested contracts with clear, immutable logic where possible.

How the agent's signing keys are stored and used is paramount. Models include:

• User-managed: The user signs each action; safest but not autonomous.
• Agent-managed (Hot Wallet): The agent holds a private key; high risk if the server is compromised.
• Secure Enclave / MPC: Using hardware security modules (HSM) or Multi-Party Computation (MPC) to sign without exposing a full private key. This balances autonomy with security.

An autonomous agent is a software program that can perform tasks, make decisions, and interact with its environment (like blockchains or APIs) with minimal human intervention. They are the primary users of the Agent Protocol. Key characteristics include:

• Goal-Oriented: Programmed to achieve specific objectives.
• Persistent: Can operate continuously over long periods.
• Self-Executing: Can trigger transactions or operations based on predefined logic.

Specialized infrastructure is required for agents to operate reliably and securely in production.

• Agent Runtimes: Execution environments (e.g., E2B, Phidata) that provide secure sandboxes, memory, and tool access.
• Agent Monitoring & Observability: Tools to track agent performance, log actions, and audit on-chain interactions.
• Key Management: Secure solutions for managing the private keys that agents use to sign transactions, a critical security component.

A multi-agent system (MAS) is a network of multiple interacting autonomous agents. The Agent Protocol facilitates communication and coordination between them. This enables complex workflows where agents can:

• Specialize: Different agents handle specific sub-tasks (e.g., research, trading, reporting).
• Collaborate: Work together to achieve a common goal through message passing.
• Compete: Operate in environments where agents have conflicting objectives, simulating market dynamics.

This refers to the standardized methods agents use to exchange information, a core function enabled by the protocol. Common patterns include:

• Direct Messaging: Sending payloads (data, intents, proofs) between known agent addresses.
• Broadcast/Discovery: Publishing availability or services to a network for other agents to find.
• Shared State & Memory: Using decentralized storage or oracles to maintain a common knowledge base that multiple agents can read and update.

A primary use case for agents is interacting with smart contracts on blockchains. The protocol standardizes how agents:

• Read State: Query contract data and blockchain information to inform decisions.
• Simulate Transactions: Test the outcome of a transaction before broadcasting it.
• Submit Transactions: Construct, sign, and broadcast transactions to execute on-chain actions like swaps, deposits, or governance votes.

The Agent Protocol is an interoperability layer, distinct from agent-building frameworks:

• vs. LangChain/LlamaIndex: These are SDKs for building agent logic. An agent built with them can be wrapped to comply with the Agent Protocol.
• vs. AutoGen/CrewAI: These are frameworks for multi-agent collaboration. They could use the protocol to communicate between agent crews or with external platforms.
• Key Difference: The protocol does not dictate how an agent works internally, only how it communicates.