Why Intent-Based Systems in Web3 Are More Complex Than They Seem

Most people think intent-based systems are like plug-and-play solutions — connect a few nodes, and the magic happens. But under the hood, things are far more complicated. Intent systems are designed to improve user experience by allowing users to express what they want (an intent), while solvers determine how to fulfill it.

In theory, this abstraction sounds perfect: users no longer worry about transaction routes, gas fees, or liquidity optimization. In practice, achieving optimal solver behavior requires constant rebalancing, coordination, and trade-offs — a complexity rarely discussed. As Web3 systems scale, understanding this hidden machinery becomes critical.

The Basics of Intent-Based Architecture and Solvers

An intent-based system allows users to express desired outcomes without specifying the steps. Solvers are entities (or algorithms) that compete to execute these intents in the most efficient way possible.

How Intent-Based Architecture Work in Web3

Intent systems abstract away gas optimizations, routing, and MEV strategies, providing seamless user experience. For example, a user can specify, “Swap 10 ETH for the best possible price,” and solvers compete to deliver the most optimal result. On the surface, the process seems smooth — but behind the scenes, solvers juggle dynamic conditions and trade-offs to fulfill that request.

Some examples of intent-based architecture include:

• DEX Aggregators (e.g., 1inch, Cow Protocol) use solvers to identify the most cost-efficient trade routes across decentralized exchanges.
• MEV Protection platforms mitigate malicious transaction extraction (e.g., Flashbots).
• Cross-Chain Solutions fulfill intents by coordinating liquidity across multiple chains, abstracting the complexity of bridging.

What Are Solvers?

A solver is a mechanism, entity, or algorithm that interprets and executes a user’s intent. Solvers take on the computational burden of finding the best possible way to fulfill the user’s request. Their role is to optimize for various factors like cost, speed, and fairness. In decentralized systems, multiple solvers often compete to deliver the best result, which drives efficiency but also introduces complexity.

For example:

• In a decentralized exchange (DEX), solvers identify optimal trade routes to fulfill a swap order.
• In cross-chain systems, solvers coordinate liquidity across chains to bridge assets efficiently.

Solvers can be centralized or decentralized:

• Centralized Solvers: Operated by a single entity and often focus on speed and efficiency but may suffer from trust and transparency issues. 
  • Binance Smart Order Routing: Used in Binance’s centralized exchange to optimize order matching.
• Decentralized Solvers: Open to anyone to participate, improving transparency and fairness but introducing coordination challenges. Examples include:
  • Cow Protocol Solvers: A decentralized system where solvers compete to execute trades with MEV protection.
  • Flashbots Auction: Decentralized solvers participating in MEV auctions to execute Ethereum transactions fairly.

The Hidden Work of Rebalancing

While solvers aim to optimize for speed, cost, and fairness, maintaining balance is a continuous challenge as demand grows. Key challenges include:

• Liquidity Imbalances: High-demand liquidity pools often see solvers prioritizing gas efficiency, which can disadvantage less efficient solvers and create resource inefficiencies.
• Adaptation Challenges: Solvers that over-optimize for low costs can congest the system, delaying or disrupting other intents. Constant changes in liquidity, gas fees, and volatility also require solvers to adapt in real time.
• Fairness and MEV (Maximum Extractable Value): MEV strategies, as highlighted by Flashbots can skew fairness. When solvers focus on MEV extraction, it often comes at the expense of user outcomes.
• Incentive Misalignment: Centralized solvers may prioritize profit over fairness, while decentralized solvers often face coordination difficulties.
• Coordination Costs: Like traffic lights, solvers need continuous recalibration to keep intents flowing smoothly. Without it, congestion and inefficiencies arise.

The Importance of Rebalancing Solvers

Solver rebalancing is the invisible machinery that keeps intent-based systems functioning effectively. It harmonizes competition, fairness, and resource allocation, ensuring optimal outcomes for users while preserving system integrity. Without effective rebalancing, several issues can arise:

System Inefficiencies

If solvers are not rebalanced, resources such as liquidity and computational power can become overused or underutilized. This leads to suboptimal outcomes for users and higher costs across the system.

For example, in DEXs, a lack of solver rebalancing can cause liquidity fragmentation, where solvers repeatedly compete for the same liquidity pools, driving up gas fees and reducing efficiency.

Solver Dominance

Without rebalancing, certain solvers can dominate execution opportunities, leading to centralization risks. This undermines the decentralized nature of intent systems and reduces competition, which typically drives better outcomes for users. Solver dominance may prioritize profit-driven behavior over fair and efficient intent fulfillment.

Maintaining Fairness

Solvers must operate under fair conditions to ensure all intents are treated equitably. Effective rebalancing prevents solvers from exploiting system loopholes or prioritizing high-value intents at the expense of smaller transactions.

The UX Dilemma

A poorly balanced solver system can cause frequent transaction failures, longer execution times, and higher costs. This directly impacts user trust and satisfaction.

Imagine a user trying to swap tokens on a DEX during peak network congestion. Without proper rebalancing, the solver handling the intent prioritizes higher-value transactions, leaving smaller trades delayed or failed. This leads to frustration and mistrust from smaller traders.

Users expect seamless, predictable experiences, and a smooth UX demands seamless solver coordination. If they consistently encounter failed transactions or unpredictable costs, they are likely to abandon decentralized platforms in favor of simpler, centralized alternatives.

Addressing solver rebalancing involves collaboration across UX design, development, and system architecture:

1. Dynamic Solver Weighting: Gauntlet Network suggests using adaptive algorithms to dynamically rebalance solver priorities based on market conditions.
2. Incentive Alignment: Platforms like Curve Finance implement reward systems that encourage solvers to prioritize cooperation over competition.
3. UX Transparency: By exposing solver metrics directly to users to improve trust. Platforms like Cow Protocol highlight solver competition as a feature, improving transparency and user confidence

Simplicity for Users, Complexity for Builders

Intent systems solve UX problems by abstracting complexity for end users. But this simplicity comes at a cost: hidden rebalancing work to keep solvers efficient, fair, and reliable. Building user-friendly intent-based systems requires tackling solver rebalancing head-on. For the Web3 ecosystem to scale, developers and designers must address these challenges collaboratively, ensuring that the invisible machinery operates as seamlessly as the user-facing interface.