DPSN+MPC: Bridging Real-Time Data for AI Agents
Last updated
Last updated
Real-time and reliable data are becoming highly critical for AI Agents. Two powerful technologies—Model Context Protocol (MPC) and Decentralized Pub/Sub Network (DPSN) integrate together to make this happen. While they have different strengths, their combination enables AI agents to access decentralized, real-time data streams seamlessly. This blog explains how MPC and DPSN collaborate, using simple language and examples to ensure everyone can follow along.
What Are MPC and DPSN?
Let’s start with a quick overview of these two technologies.
MPC (Model Context Protocol)
MPC acts as a universal translator for AI agents, providing a standardized way to access data and tools from various sources—databases, APIs, or decentralized networks. While MPC is not just pull-based—it supports push-based updates through Server-Sent Events (SSE) for continuous data delivery—its major adoption comes from a pull-based model. In this model, AI agents request data as needed, which aligns with how most agents currently use MPC. However, the SSE capability ensures MPC can also handle real-time, push-based scenarios when required.
DPSN (Decentralized Pub/Sub Network)
DPSN is a decentralized platform built for real-time data streaming, ideal for Web3 applications like decentralized finance (DeFi) or the Internet of Things (IoT). It follows a publish-subscribe (pub-sub) model:
Publishers send updates—like crypto prices or sensor data—to specific topics.
Subscribers receive those updates instantly. Its decentralized nature means no single point of failure, ensuring reliability and resilience.
The Challenge: Different Data Delivery Styles
MPC and DPSN approach data delivery differently:
DPSN is push-based, streaming data to subscribers as soon as it’s available.
MPC supports both pull-based (where agents request data) and push-based (via SSE) methods, though its primary use today is pull-based due to widespread agent adoption patterns.
For AI agents using MPC in a pull-based way, tapping into DPSN’s constant push-based stream requires a clever solution. Even though MPC can handle push updates with SSE, many agents are designed for or prefer pull-based interactions. This is where integration gets interesting.
The Solution: An MPC Server as a Flexible Bridge
To connect MPC and DPSN effectively, an MPC server acts as a bridge, catering to both pull-based and push-based needs. Here’s how it works:
Subscription: The MPC server subscribes to DPSN topics—like a price feed or IoT sensor updates—and receives the data stream.
Buffering: Incoming data is stored in a buffer, which might hold the latest update or a short history, depending on the use case.
Flexible Delivery:
For pull-based agents (the majority today), the server makes buffered data available via MPC tools, letting agents query it on demand.
For push-based agents, MPC’s SSE streams the data continuously, bypassing the need for requests.
This dual approach ensures AI agents can access data in the way that suits them best, reflecting MPC’s versatility and its dominant pull-based adoption.
A Real-World Example: Trading in DeFi
Let’s explore this with a practical example: an AI agent in a DeFi app tracking cryptocurrency prices for trading decisions. Here’s how it plays out:
Subscription: The MPC server subscribes to a DPSN topic, “price_feed,” for ETH/USD prices.
Buffering: As new prices arrive (e.g., every few seconds), the server updates its buffer with the latest value.
Agent Interaction:
Pull-based (most common): The AI agent, using MPC, calls a tool like get_latest_price("price_feed") to fetch the current price—say, “ETH/USD = $2,500”—when it’s ready to decide.
Push-based (optional): If designed for real-time updates, the agent uses MPC’s SSE to receive a continuous stream of price changes without asking.
Outcome: The agent acts on the data—buying or selling—based on its preferred method of access.
This example highlights MPC’s flexibility: while pull-based access dominates due to agent preferences, SSE enables push-based updates for more dynamic needs.
Why This Matters
Pairing MPC and DPSN offers significant benefits:
Tailored Data Access: Agents can pull data on demand (the most adopted approach) or receive push updates via SSE, matching their design.
Real-Time Power: DPSN’s live, decentralized streams keep AI agents informed with up-to-date info, like price feeds or sensor readings.
Decentralized Reliability: DPSN’s no-central-point design ensures data keeps flowing, even during disruptions.
Versatility: The MPC server supports the pull-based majority while SSE accommodates push-based scenarios, making this integration widely applicable.
This setup shines in time-sensitive fields like DeFi or IoT, where AI needs either periodic checks or constant updates.
How It Works Behind the Scenes
Here’s a peek at the technical magic (kept simple):
Freshness: The MPC server updates its buffer instantly with DPSN data, ensuring pull-based agents get current info.
Storage: The buffer keeps only what’s needed—like the latest price or a small history—to stay efficient.
Focus: The server subscribes only to relevant topics, minimizing waste.
SSE Push: For push-capable agents, MPC’s SSE delivers data in real time, no polling required.
These details make the system fast, lean, and adaptable to how most agents use MPC today.
A Future-Ready Protocol
By combining MPC and DPSN with a flexible MPC server, we’ve created a solution that bridges real-time data to AI agents effectively. While MPC’s major adoption is pull-based—reflecting how agents typically query data—its SSE support ensures push-based updates are also possible. This adaptability lets AI tap into DPSN’s decentralized streams in the way that works best for them.
This integration unlocks huge potential for Web3, from lightning-fast crypto trades to responsive IoT systems. As decentralized tech grows, MPC and DPSN together will empower AI to make smarter, faster decisions—meeting diverse needs in an ever-changing landscape.
