Redefining Blockchain Interactions: The Crucial Role of Solvers in an Intent-Focussed Future

Thanks a lot to Christopher Goes (Anoma), Sam Hart (Skip Protocol), Stephen Monn (Essential), Alex Viñas & Andrea Canidio (CoW Swap) and Markus Schmitt (PropellerHeads) for reviewing the article and providing invaluable feedback.

1. Introduction to Solvers: Setting the Context

In the following article, we will dive into the critical role that solvers play, particularly in the context of intents. We’ll explore not only their fundamental responsibilities, but also look into the broader challenges within the solver ecosystem, while also looking into potential solutions for some of these challenges. While the discussion, and especially the role of solvers, is centered around intents, the purpose of this article is not to be an introduction to intents.

As a refresher, it is important to distinguish between transactions and intents. Transactions encode imperative execution logic, which means that every single step of the “execution chain” needs to be clearly defined. In contrast, intents are signed messages that only define an acceptable end state and potential constraints. They don’t define “how” this end state is achieved. Finding the best “how” is outsourced to a specialized actor, called a solver.

For a more in-depth coverage of intents please check the articles written by Paradigm, Delphi Digital and 20[ ].

2. Exploring the future Landscape of Intents

We believe that intents have the potential of shaping the future of user interactions with crypto.
Intents present a unique opportunity to solve two key issues at once: Optimal execution quality for users, including tackling MEV & extractive behavior, along with resolving fundamental UX obstacles that limit crypto’s untapped potential.
Intents enable us to repurpose the existing MEV infrastructure toward benevolent ends. Instead of searchers competing solely for personal gains, solvers compete for providing users with optimal execution – all while significantly enhancing UX and lowering the barriers to entry.
Nevertheless, we recognize the need for careful management of potential negative externalities associated with intents and solvers.

3. An Intent-Centric Future

In an intent-centric world, users’ interactions with protocols are poised to be redefined. We foresee a decline in the relevance of monolithic front ends, as an efficient solver market eliminates the advantages of utilizing protocol-specific frontends for transaction execution.
Users can only gain increased efficiency by using intents and solvers; at a minimum, they’ll achieve execution close to what they’d get from manual transaction creation.
Notably, the biggest difference is the settlement conditional fee that a user would need to pay to solvers. However, we expect that even in a case where an asset is traded only in a single pool at one venue, users would still benefit from a solvers’ capabilities in MEV protection and gas optimizations among other things.

An intent-centric future will have considerable implications for our understanding of value capture in the transaction supply chains, the role of actors such as LPs, the design of protocols such as bridges, the overall user experience of crypto, and much more. In such a world, the role of protocols will gradually move more into the background. Protocols will compete on efficiency, rather than focusing on user acquisition for their front end. This trend started with DEX aggregators, as some DEXs get significant volume through aggregators, without having many users of their protocol-specific front end. We are even starting to see DEXes like Ekubo on StarkNet that do not provide a frontend for swaps at all and rely completely on DEX aggregators, and in the future solvers, to route swaps through their liquidity, doing around 75% of all StarkNet’s trading volume.

However, we digress, this might be the topic of a follow-up article in which we will paint the picture of how an intent-centric future may look like and how the role of current ecosystem participants may change.

Numerous articles have been written about the advantages and risks of intents.
In this article, we will focus specifically on the role of solvers. We will explore their evolving function in the ecosystem, potential future developments, and concerns tied to introducing this new intermediary—along with strategies for mitigating such issues.

4. Overview of Current Solver Activities

a) A Solver’s Role Today

The role of solvers has become more prominent with an increased discussion around intents. Even in the short term, they have shown significant impact in areas such as execution quality and enhanced user experience.

Solvers are market participants who provide counterparty discovery for user intents. Said simply, users express their desired outcome through intents and solvers find the best path to achieve it and earn a fee for satisfying said intent.
While in theory solvers have a significant degree of flexibility around how to solve an intent, as long as settlement satisfies the user’s expressed end state and constraints, protocols often restrict such flexibility in their off-chain auction design by setting additional constraints.

We are seeing a fast growth in the amount of trading volume that is executed through solvers and we expect it to continue to grow significantly as the infrastructure for intents and solvers matures over the next few years.

i. Execution Quality

Currently, the majority of intent protocols on Ethereum are isolated proto-intent systems, where users express protocol-specific intents, mostly centered around trades. Examples include CoW Swap, 1inch Fusion, and UniswapX. Solvers frequently provide the best pricing and execution on these protocols.
The most optimal order routing is either finding a “coincidence of wants” or a “ring trade”. This means that a user’s intent gets matched directly with one or multiple intents of other users. As a result, all users get execution without incurring any fees or slippage. Cow Swap explains these concepts well in this post. However, these types of trades are unfortunately quite rare.

In batch auctions, such as those used by Cow Swap, orders are not executed instantly. Instead, orders from all users are aggregated over a specified period of time, creating a batch. Solvers then compete on optimally solving all orders within a batch.
Such slow settlement can improve execution quality. The longer a user wants to wait, the higher the chance that a CoW, ring trade or other orders with the same direction, that can be bundled for increased gas efficiency, get found.

Protocol specific proto-intent systems suffer from a lack of composability of intents. Consequently, an intent on UniswapX cannot be matched with an intent on CoW Swap.
This is a limiting factor for unlocking the full potential of intents and solvers, as it limits finding CoWs, ring trades or even just batching orders for gas efficiency.
Generalized intent networks and architectures are planning to address this limitation, although none have been launched as of yet.

This year we are seeing a surge in strong teams developing intent-based infrastructure - both generalized and app-specific. Establishing an open, generalized intent standard will be crucial, facilitating a transition away from app-specific intents to maximize efficiency for end-users.
As a result, the role of solvers will only become more important, empowered by a generalized intent standard and full intent composability, making them an integral part of future user experience.

“Solvers will help traders overcome the limitations of 1st generation DEXs – removing most of the price-impact, DEX fees, settlement uncertainties and gas costs from DeFi trades. The next generation of DEXs, intents and solvers will ensure that DeFi is not only trustless, but has the best prices, and the most expressive UX, ahead of centralized alternatives.” Markus Schmitt from PropellerHeads.

ii. User Experience

While crypto made advancements in account abstraction, addressing wallet management and security, it continues to suffer from UX challenges rooted in the current transactional flow. The learning curve is steep: To be somewhat efficient, users must navigate a myriad of protocols, identify scams, understand MEV, be familiar with private RPCs, OFAs, and much more. The barriers to entry are too high and this UX will never scale.
Solvers act as an abstraction layer and as “mediators of expertise”. They enable even uninformed users to achieve near-optimal execution by improving the quality of transactions. Users simply need to express their desired end state, without having any expertise or even knowledge about the existence of various underlying protocols.

To bring this point home, let’s look into a typical user flow of a new crypto user. Alice only knows about Uniswap.

Alice has 1,000 USDC on Ethereum and wants to buy ARB on Arbitrum with it.
Her steps are roughly the following:

1. Bridge
   1. Research Arbitrum bridges, preferably find the one that gives you the best execution, either speed or the highest output amount
   2. Open dApp website
   3. Approve USDC on bridge
   4. Bridge funds
   5. Wait up to 30 minutes
2. Swap
   1. Go back to Uniswap
   2. Figure out how to change RPC
   3. Approve USDC
   4. Buy ARB

And all of this does not even consider optimizing within any of these steps at all or researching / being aware of aggregators; all while avoiding scams and malicious contracts.
With intents, Alice would just express her intent with settlement on Arbitrum – done.

b) How Solvers Operate

Solvers have full flexibility around how they solve a user’s intent. They can compete through efficient on-chain routing capabilities, optimizing gas efficiency, cross-chain execution capabilities, access to off-chain liquidity, RFQ systems, private order flow, and much more.
Consequently, solvers have to optimize across various parts of the stack. This includes but is not limited to:

1. Build out efficient on-chain routing
2. Maintain off-chain liquidity sources
3. Optionally source private order flow
   1. To underline the importance of private order flow, in “Builder Dominance and Searcher Dependence” by Titan it was said that “when analysing all transaction hashes in mined blocks, Titan receives all transactions that are eventually included in a block in only ~2.7% of cases. This percentage includes our mempool flow, bundle flow, and private transaction flow”. Obviously, this might be different for other builders and it might have changed since then
   2. E.g. Propellerswap
4. Optimize for latency to be able to do all of the above in a timely manner
5. Manage their own inventory
6. Explore vertical integration
   1. For structural advantages of vertical integration see here

We see searchers in a particularly strong position to scale into the role of solvers since both roles require a similar skillset.

The optimization of execution quality is facilitated through “solver auctions”. Where multiple solvers compete to fulfill a user’s intent. These auctions can be structured in various formats, such as batch auctions or dutch auctions. Solvers are typically explicitly compensated via a potential settlement conditional fee linked to an intent, along with any additional value they may extract from the order flow as an implicit reward. In a sufficiently competitive solver auction, the incentive structures are designed to encourage solvers to bid the majority of their anticipated returns, including MEV returns. Consequently, intent solving normally includes a built-on order flow auction for optimal execution quality.
Unlike in some order flow auction design, the user does not get a “refund” based on bids during the auction, but rather gets optimized execution directly. The incentive mechanisms here generally align with those found in block builder auctions.

5. The Future Trajectory: How Solvers Will Evolve

a) The Growing Dominance of Solvers

We believe that solvers will be able to offer highest execution quality by leveraging multiple sources of liquidity, including off-chain sources. We go so far as to predict that solvers may even offer better prices than CEXs, especially for regular CEX users who don’t have access to reduced trading fees. In solver-driven auctions, users often even receive execution quality that exceeds their initial expectations, allowing them to secure a rate better than their initial quote. As Markus pointed out, this is the default case for auctions, as they come with a strict requirement that settlement price needs to be equal or higher than the provided quote – unless slippage is considerable.

Solvers can act as a crucial link between available on-chain liquidity and optimized access to off-chain liquidity. Combined with access to private order flow or RFQ systems, this underlines the argument for solvers as abstractors of complexity even stronger.
Furthermore, solvers can even incorporate broader off-chain contextual data about a swap, such as classifications that differentiate between toxic and non-toxic order flows.

However, solvers rely on mempool simulations and various types of predictions. Thus, they need to account for a lot of uncertainty. In the current transaction supply chain, the party with the highest information advantage are block builders. They benefit from “last look” on each block, along with an up-to-date view of state, e.g. the trades within the block and the price of an asset on a centralized exchange.
This asymmetry in information can serve as a pivotal competitive advantage. Consequently, we might see an increased trend towards vertical integrations, where solvers also start being block builders. This way, solvers can have a more complete understanding of state, while also minimizing latency. This would further amplify the existing pressures towards centralization that are already evident today.

b) Expanding the Scope: New Frontiers for Solvers

Our current understanding of intents and solvers is focused around swaps. Such systems are easier to structure as swaps have an objective “best execution” path. Solving swaps also does not depend on any subjective user-specific data point. Consequently, structuring solver auctions around swaps is also considerably more straightforward. At least currently, they always optimize for price.
However, over time we are also interested in following how the ecosystem will develop around “subjective” intents. Those are harder to solve, as they require additional user profiling or at least rounds of interactivity, which could result in DoS concerns.
An example of a subjective intent is lending. There is no objective truth to the question of “Where is the best place to earn yield on my USDC?” as it depends on each user’s risk profile.
As added by Markus, even in a case where you try to create an objective quantifiable metric for risk, and solve this two-dimensional optimization problem, different users will have varying subjective preferences over the weighting of risk vs. yield.

Furthermore, we expect that the role of solvers will become even more important with an increased fragmentation due to the rollup centric future and modular stack developments. The aforementioned UX concerns will become even worse, meaning that the importance for a layer of abstraction will grow simultaneously.

We expect that solvers will continue to specialize over time and optimize for a specific market segment. Currently, most solvers are focused on swaps and optimize for a similar skillset. Specializing in a particular niche allows solvers to build up segment-specific private order flow, information advantages and general expertise within that sector.

6. Addressing Concerns and Risks with Solvers

Financial incentives remain a dominant driving force, frequently serving as the main determinant in user decision-making processes. There are exceptions, such as when participants prioritize alternative values in scenarios where the financial gain is minor, such as in the case of -min-bid in MEV-Boost.
Therefore, the primary goal for a DEX to optimize for is offering the best price. As per our previously shown graphic, solvers help achieve this goal.
While the impact of solvers sounds promising, we need to be conscious about negative externalities or even introducing new types of risk.

a) Principal-Agent Problem in Solver Systems

The lack of accountability amongst solvers is a concern and presents a typical principal-agent problem.
One of the main issues for users is that intents become a “free option” for solvers if they are not expressed properly. Solvers have total control about how the intent gets executed, and users might not receive the optimal outcome if there is not enough competition in the solver market. For instance, a user might express an intent for “swapping 1 ETH for at least 1500 USDC”, with a prevailing market rate for $ETH of $1550. In a direct-to-solver relationship, the solver could fulfill this intent by merely providing the user with 1500 USDC, retaining any difference between this amount and the current market rate as profit. The winning solver has a short-term monopoly in terms of value extraction from the user, and could act as rent seeking middlemen. The situation for the user improves the more competitive the solver auction is.
This is a similar scenario to staking providers, where acting maliciously would be economically rational if the extracted value is bigger than the loss of collateral or future business revenue.

b) Risk of Centralization

The Ethereum transaction supply chain already faces strong centralizing forces, particularly at the block builder level where over 70% of blocks are built by just three entities. Similarly to block building, solving is a complex role that requires market sophistication. It’s also susceptible to becoming a ‘winner-takes-all’ market. A lack of sufficient competition among solvers could lead to a scenario where the majority of intents are solved by a limited number of participants, thereby amplifying centralization risks and associated negative externalities.

We believe that there’s a high likelihood of centralization at the solver level, particularly when considering the importance of having access to private order flow in enabling a solver to be competitive, as well as the incentives solvers have to vertically integrate with block builders.

However, Stephane from Frontier recently argued that neutral block builders might actually end up receiving more order flow, therefore being more competitive than a vertically integrated builder. The reason is that solvers / searchers would not send their bundles to a vertically integrated builder, as it would leak information. As a result, they would mostly send to neutral block builders. This could kickstart a feedback cycle that benefits neutral block builders and reduces the incentives for vertical integration.

One of the biggest concerns resulting from high levels of centralization at the solver level is reduced competition in solver auctions. This increases the risk of extractive behavior, as solvers face little need to bid away a significant portion of their profits. Moreover, formation of cartels becomes easier to manage too. Beyond this, centralization brings additional challenges including reduced resistance to censorship, and increased difficulty in implementing modifications to the Ethereum transaction supply chain, given that any changes would need to gain the acceptance of a concentrated group of solvers.

Moreover, Quintus emphasized how centralization might even lead to a lack of innovation, thereby reducing the likelihood of UX improvements being developed. In a sufficiently competitive market, intermediaries such as builders, and maybe even solvers, can try to compete on functionality beyond price optimizations.
“Some of the ideas that have been floating around include account abstraction, backrunning-as-a-service, gasless cancellations, gasless orders, pre-confirmations and state channels. If order flow does not respond to the implementation of new features because it is tied up in PFOF contracts or some other reason, there is no incentive for builders to implement these features.” - Quintus.
The extent to which this applies to solvers is still an open question. Nonetheless, we are already seeing promising ideas in this area; for example, Propellerheads is developing patient-intents on the solver level.

c) Censorship Resistance

Since Flashbots open sourced their block builder and relay code a year ago, censorship resistance on Ethereum has improved significantly and the number of OFAC compliant blocks has declined from 75% to 30%.

However, the growth of the solver role might become a new threat to censorship resistance. A censored intent would not even reach the block builders for eventual inclusion. If solvers decided to be OFAC compliant, then some intents could be at risk of not being fulfilled at all and not even reach block builders. While there are economic incentives to address it, some degree of competition and solver diversity is required to guarantee intent inclusion and censorship resistance.

We predict that there will be a considerable degree of centralization at the solver level. If that becomes true and most intents are solved by a small number of solvers, we could see similar challenges in terms of censorship resistance compared to what we have seen at the block builder and relay part of the stack. \
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Centralisation is not the only aspect that could compromise censorship resistance and inclusion. Solvers could hoard intents to gain competitive advantage and earn more fees from users.

Lastly, extreme centralization could cause the formation of censorship-as-a-service markets in which users are able to bribe the centralized intermediary to censor someone else’s transaction. We think similar challenges could be seen at the solver level if we see a high degree of centralization.

d) Price Discovery Remains Off-Chain

At present, the majority of liquidity and price discovery for major assets predominantly happens on centralized exchanges, where customers don’t have custody over their assets and there’s very little transparency.
It can be argued that our end goal should be to move liquidity on-chain, instead of just making it easier to access off-chain liquidity. The concern is that relying on a centralized intermediary for better execution via off-chain resources could reinforce the existing dominance of off-chain venues in price discovery and order flow. Instead, we should strive for enhancing on-chain liquidity and execution quality, with the aim that on-chain systems will eventually become more efficient than their off-chain counterparts.

While we obviously agree with this goal, it is important to note that intents and solvers give us an opportunity to change user default habits. We see this as a valuable interim goal to pursue.
Improving wallet-accessible execution quality could serve as a significant pull factor for users, incentivizing them to set up funded wallets. Again, financial incentives are often the strongest. As a result, we could see a notable shift in user behavior, transitioning from CEXs to wallet-based trade execution. Even if the actual trade execution happens off-chain, and merely settlement is on-chain, the impact on altering user habits is substantial. Most users will still perceive this as “on-chain” because they associate wallet interactions with on-chain interactions. While this shift may not be as ideal as significantly improving on-chain execution, it represents a positive development. Changing habits in this manner could have a significant downstream impact, leading to a significant number of users with funded wallets and the know-how to use them.

7. Risk Mitigation Strategies for Solver Systems

We are in the nascent stages of the development of intent-based protocols and networks. Below is a non-exhaustive list of ideas designed to address some of the previously mentioned risks.

a) Tackling Fragmentation Through Generalized Intent Architecture

Ecosystem convergence on a common intent standard is crucial for several reasons, including full fungibility of intents. This would naturally lead solvers to also adopt this standard, thereby increasing solver participation. Increased competition among solvers would then improve execution quality for users and minimize the likelihood of extractive behavior. A greedy solver would simply be outbid by competitors in the auction.

“Our push for an open and collaborative generalized intent standard, ERC-7521, aims to be the catalyst that moves us from a world of intent and account abstraction teams reinventing the same but incompatible wheels, into a world where teams can specialize and build off each other, making the pie bigger for all involved.” Stephen from Essential.

The evolution of censorship resistance in the block building market could offer valuable insights. As soon as we saw Flashbots open source their relayer, we saw the emergence of new, neutral relays over time, that have reduced concerns related to censorship. A similar improvement could be expected from an open and commonly-accepted intent standard. In a similar vein, even ‘weak’ censorship resistance — whereby a delay in executing OFAC intents is tolerable as long as they are eventually processed within a “reasonable” timeframe — could be sufficient.

Lastly, as pointed out by Delphi, designs such as Anoma’s chimera chains allow solvers to take on less risk by enabling riskless cross-domain atomicity. This allows for offering better execution, due to the certainty of atomic trade execution, and lowers the capital requirements for solvers. In alternative systems, solvers would often need to fill an intent with their own inventory and then rebalance their books afterwards. This exposes them to inventory risk and comes with higher capital requirements. Designs such as chimera chains make this process risk-free and improve accessibility.

b) Solver DAOs

The anticipated increased specialization of solvers will mean that the necessity for collaborative solving will grow over time. This approach allows for more efficient solving of multi-leg intents by combining intent solutions from multiple specialized solvers, rather than one party solving the whole intent, including the parts they are not specialized in.

Firstly, we have Anoma’s compositional solving design. This basically allows splitting an intent into multiple legs that can be solved for individually.
For instance, “Bridge 1 ETH to Arbitrum and sell it into USDC at market price” could be split into 1) bridge and 2) swap.
A solver specializing in bridging can handle that segment and then pass it on to a solver proficient in swaps, optimizing execution quality while allowing both solvers to earn a fee.

Moreover, there’s potential for collaboration even within a single leg of a trade. Let’s imagine there’s a larger trade intent expressed: “Swap 1,000,000 USDC into ETH at market rate”. With an $ETH price of $1550.
Solver A might be the only solver with access to a specific market maker RFQ system. He can even get a price of $1545. However, the maximum trading size is 500,000 USDC. He would need to fill the rest at the market price of $1550. Giving the user an execution price of $1547.5. The user receives 646.203554 ETH.
Solver B could have filled the whole intent at an ETH price of $1548.5. The user would have received 645.786245 ETH. Solver B loses the auction.

If it would have been possible for solver A to fill only 50% of the swap and solver B to fill the other half the user would have received an average price of $1546.75, which would be equal to 646.51689 ETH and therefore the optimal approach.
This could be especially important in the context of private order flow. Similar to how current conversations around PEPC-Boost and similar proposals explore splitting the block into Top of Block and Rest of Block, we could maintain competitiveness on the part of the intent that would not be filled through private order flow. This would reduce barriers to entry for new solvers.

Lastly, Suave could develop into a decentralized solver on top of generalized intent architecture, such as Anoma. This would play out in a similar way to their vision about becoming a decentralized block builder on multiple domains.

c) Local Solving in Anoma: Exploring Fractal Instances

Anoma can see a world where we might have local fractal instances. As soon as the user intent isn’t fully of financial nature, local solvers with background knowledge of that geographical location might have an advantage, such as cultural knowledge. This would result in local solvers being more competitive than global solvers for that specific fractal instance.
This is similar to the aforementioned idea of solvers specializing; just that in this case they are specializing in a specific geographical location, community or culture.

While the idea of local solving offers promising advantages, it’s important to recognize that this vision is far from realization. Moreover, it will not be universally applicable, especially in the context of pure global financial intents.

d) Reducing Reliance on the Competitiveness of the Solver Ecosystem

The above approaches generally focus on increasing the competitiveness of the solver ecosystem. What if we instead try to reduce the reliance on the competitiveness of the solver ecosystem? Generally, even a competitive market is not necessarily good at long-term ecosystem optimizations. As long as there exists a period of time in which extractive behavior is rational, solvers will inevitably pursue this strategy, instead of working towards a collective long-term objective that would benefit the entire ecosystem.
What if we can instead find ways to reduce our reliance on the competitiveness of the solver ecosystem? In the end, decentralization is generally not a goal in itself. Even in Ethereum, it is a proxy for reaching higher overarching goals such as censorship resistance.

Generally, the less specific an expressed intent is the more possibility for extraction solvers are presented with. As a result, if we can optimize a user’s intent before it even gets sent to solvers we can limit the possibility for extraction. This optimization necessitates a clear definition of constraints associated with an intent. Most notably, this would not be done by the user itself like in the current user flow. For instance, intent templates, automated intent optimization, pre-validation checks, among other ideas could be explored.

This is, of course, a non-trivial computational and design task and we are mostly thinking out loud here. Moreover, this could likely only be accomplished by using publicly visible state and order flow, but this could already help with an automatic baseline addition of constraints.

This could play out in a similar way to traditional ad auctions in web2, where “best execution” is defined by conversation rates. Buyers of ad space want to optimize for conversion rates, without knowing how to achieve this. They define an end state, but don’t encode any imperative execution logic.
Google advertisers are using automated bidding strategies. These are automated software systems, similar to “bidding agents”. These agents frequently employ AI and machine learning techniques and optimize for the usage of funds.

e) Reduce Solver Trust Assumptions

In his recent article, Jon talked about how rollups and intent systems are often not so different from each other, as in both cases “you rely on offchain actors (sequencers vs solvers/fillers/etc.) for some weaker guarantees such as providing best execution and good UX”, without custodying your funds.

He also outlines how verifiable off-chain compute will become increasingly relevant here, such as ZK co-processors.

f) Accountability Frameworks

Sam Hart recently brought up how an accountability framework could be used to address the principal-agent problem in intents: users expressing intents and solvers acting as their agent to solve it. Skip Protocol is actively working towards this with validators on dYdX..

While it may be difficult to prevent short term extractive behavior from anon solvers, the right accountability framework could achieve long-term alignment between user and solver interests.

For instance, users can constraint their intent by specifying which solver will be able to solve it. By referencing an accountability framework, users could restrict solver eligibility to those ranked in the 75th percentile or higher. This would create a direct correlation between the volume of order flow a solver receives and their standing within the accountability framework.

In line with Sam’s proposal, the accountability framework must be designed to incentivize long-term, trustworthy behavior among solvers. Effective enforcement would significantly tarnish a solver’s reputation, and consequently their future earnings, if they act against user interests. Rebuilding that lost reputation would not only be a lengthy process but also make the solver less competitive due to diminished order flows.

“Repeated interaction engenders trust because the past ritualizes mutually beneficial behavior that can be projected into the future. Both players know the winning game will continue. Within a commercial network, service providers can build a reputation for quality delivery. Customers then gain confidence from their own experience, the endorsement of others, as well as the knowledge that a breach of trust would have a cascading cost on the entirety of their future business” - Sam Hart from Skip Protocol.

Such a framework can be created by using blockchains as a commitment device. Enforceability can be encoded and we achieve common knowledge of such enforcement through transparent and open source mechanisms. Shared knowledge of enforcement itself can act as a credible threat to influence an agent’s behavior. Please see Anoma’s Suave blog, @sxysun/ccdwtf">Sxysun’s hackmd and @virgilgr/ethereum-is-game-changing-technology-literally-d67e01a01cf8">Virgil Griffith’s blog post for more information on this.

g) Granularity of Control

Another way to alleviate concerns is to enhance user control through finer granularity.
Anoma achieves this through “information flow control”. This allows users to control how information flows and choosing who to reveal it to and under which circumstances. This intersects closely with a broader conversation around privacy and particularly programmable privacy.
Other networks such as Suave, where users can authorize specific contracts to access their private data, enable similar functionality. See Anoma’s Suave post to learn more.

This granularity also extends to the selection of specific solvers for your intent. Users can carefully select which solver, or subset thereof, will receive their intent order flow. This grants users a credible threat of switching solvers, serving as an enforceable action for accountability as previously discussed

This would also allow for users to build up long-term relationships with selected solvers, potentially allowing them to explore alternative fee structures beyond a per-intent basis.

However, increased control could inadvertently lead to user default behavior, thereby amplifying centralization risks. One countermeasure could involve incorporating a degree of randomness in solver selection to encourage decentralization

h) Combinations of the Above

It is also worth mentioning that some of the potential solutions we cover are more powerful when combined. For instance, a constrained intent can be combined with an accountability framework.
The added constraint limits the worst case outcome for users. Let’s assume a user wants to sell a token for the current market rate of 1000 USDC and there is an application enforced constraint of a maximum 3% slippage. The worst case outcome for the user is to receive 997 USDC, which could even be the quote shown on the front end.
The accountability framework could now be applied in this context. Solvers effectively get rated, and the ones with the lowest ratings are the ones that continuously solve intents close to the lower bound limit, i.e. in this case return 997 USDC or close to it.
A lower rating will reduce the amount of times the solver is chosen, which in turn reduces their order flow, and consequently reducing future earnings.

Concluding Remarks

Intents and solvers offer a promising avenue for innovation, but they are not a be-all and end-all solution to current challenges.
They do come with various negative externalities that we need to carefully manage.
Moreover, relying solely on off-chain liquidity is insufficient; enhancing on-chain efficiency must be pursued in parallel.

We firmly believe that the significance of solvers will grow over time. They can develop into a critical part of the stack, addressing pressing issues, specifically in improving execution quality, even in the context of MEV, and enhancing user experience amid growing ecosystem fragmentation.

At Perridon Ventures, we are eager to take on these challenges head-on and support portfolio teams in building towards this vision. If you’re a builder innovating in the intent space, we would love to speak with you! Reach out to us via X @perridonventure.

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