AI agents

What it refers to

AI agents, also called autonomous agents, are software systems that independently evaluate conditions, form financial intents, and execute actions across networks without requiring continuous human input.

They operate as participants in the same execution infrastructure as human users: submitting intents, interacting with solvers, and receiving outcomes.

An AI agent is not a bot that repeats a hardcoded strategy. It is a system that evaluates changing conditions, decides what action to take, and submits that action as an intent for the execution layer to resolve.

In practice, AI agents:

• Monitor conditions across multiple networks simultaneously
• Form intents based on predefined goals, thresholds, or learned strategies
• Submit those intents to execution infrastructure (solvers, SDKs, MCP servers) for resolution
• Evaluate outcomes and adjust subsequent actions

This concept is often searched as AI agents crypto, AI DeFi agents, autonomous DeFi, AI trading agents, or agentic execution. The cross-network SDK (often searched as cross chain sdk) and standards like the Model Context Protocol (MCP) are the natural interfaces through which agents interact with the execution layer.

Why this concept exists

The multi-network environment generates more opportunity and more complexity than any human user can track. Conditions change across dozens of networks simultaneously. Yield rates shift. Liquidity moves. Arbitrage windows open and close in seconds.

Human users cannot monitor all networks, evaluate all conditions, and execute optimal actions in real time. The gap between available opportunity and human capacity to act on it grows with every network added to the ecosystem.

At the same time, the infrastructure for running autonomous agents is maturing rapidly outside of DeFi. Tools like Claude Cowork and Claude Code from Anthropic give non-developers and developers alike the ability to run persistent, agentic workflows that interact with external services through connectors and MCP servers. Open-source frameworks like OpenClaw take this further, enabling always-on agents that operate across messaging platforms, APIs, and scheduled tasks with persistent memory and tool access.

These systems are not DeFi-specific. But they demonstrate the pattern: an agent evaluates conditions, selects an action, executes through available infrastructure, and adjusts. The same pattern applies to cross-network financial execution. The difference is that DeFi agents need access to real-time liquidity data, cross-network routing, and settlement infrastructure to act on.

AI agents exist because the scale and speed of the multi-network environment exceeds human operational capacity. They are not a replacement for user intent. They are an expression of it: a user defines goals, and the agent pursues them continuously across the full execution surface.

What this changes for system design

If agents are participants alongside human users, execution infrastructure must support both equally.

System design must:

• Accept intents from agents with the same reliability as human-submitted intents
• Provide programmatic interfaces (SDKs, APIs, MCP servers) that agents can use to discover conditions, submit intents, and receive outcomes
• Handle higher throughput and frequency of intent submission as agents operate continuously
• Maintain execution quality regardless of whether the intent originates from a human or an agent
• Support trust and reputation frameworks so that agent behavior can be evaluated and constrained

The Model Context Protocol (MCP) is especially relevant here. MCP is an open standard that allows AI agents to connect to external data sources and tools through a single protocol. An MCP server exposes capabilities, and any compatible agent, whether running in Claude, Cursor, VS Code, ChatGPT, or an autonomous framework like OpenClaw, can discover and use those capabilities without custom integration code.

The SODAX Builders MCP server (builders.sodax.com) is built on this principle. It gives AI agents live access to cross-network DeFi data across 17+ networks: swap tokens, money market rates, solver volume, intent transaction history, and auto-syncing SDK documentation, all through one MCP connection. An agent connected to this server can query current conditions, identify opportunities, and inform execution decisions across the full SODAX network surface.

This is the bridge between general-purpose agent infrastructure and DeFi execution. Agentic tools like Claude Cowork and OpenClaw provide the orchestration layer, the ability to run persistent workflows, manage context, and call tools. The SODAX Builders MCP server provides the DeFi execution layer, the data and interfaces agents need to act on cross-network opportunities. Together, they enable a pattern where an AI agent can monitor, evaluate, and execute across networks continuously.

AI agents shift execution infrastructure from serving occasional human actions to supporting continuous, programmatic participation. The intent architecture becomes the shared interface between human goals and agent execution. Systems built around intent-based execution (often searched as intent based execution) and solvers are naturally positioned as the execution layer for AI agents.