Agent Building

With Blueprint, creating advanced, autonomous agents for Web3 is no longer a challenge — it’s an advantage. Let us handle the infrastructure, so you can focus on what matters.

Blueprint empowers users and enterprises to create, configure, and deploy their own custom AI Agents tailored specifically for Web3. Our infrastructure is designed to offer flexibility and out-of-the-box functionality, accelerating your journey from concept to deployment.

Build What You Need

Blueprint is designed for configurability. You can adjust your agent’s behavior, tools, and data access to suit your specific workflow — whether you’re automating DAO operations, interacting with DeFi protocols, or managing multi-step actions. Use only the features relevant to your objectives, without unnecessary complexity.

From Tasks to Goals: Agentic Automation

Blueprint agents are more than reactive tools — they’re autonomous, proactive systems. Instead of scripting individual actions, you can hand off high-level goals and let the agent determine the optimal path to fulfillment.

Web3-Native Integration Layer

Blueprint comes equipped with deep integrations across the Web3 stack. This means your custom agents can seamlessly interact with your choice of top protocols, liquidity pools, or core Web3 infra, without needing custom bridge code or external tooling.

Speed-to-Value

With Blueprint’s out-of-the-box tools, you'll be able to go from zero to a functional Web3 agent in days — not months. Pre-integrated services and template workflows accelerate deployment and let you focus on outcomes, not infrastructure. Everything you need is already included.

Scheduled Tasks & Event-Driven Triggers

Leverage our Scheduled Tasks Module to enable recurring or condition-based transactions and operations. This allows your agents to perform actions automatically at specified intervals or in response to on-chain events and external signals.

Intelligent Context with RAG + MCP

Every Blueprint agent is powered by a Retrieval-Augmented Generation (RAG) engine and our Model Context Protocol (MCP).

This allows agents to:

• Ingest and act on relevant historical, real-time, and user-provided data.

• Maintain persistent memory and personalized context across sessions.

• Deliver more accurate, informed, and user-specific outcomes over time.