Final ultimatum! The AI giant whale is about to awaken, and the $ETH ecosystem faces a "structural reshuffle." Can your position withstand it?

Blockchain is built for machines, but it is not designed for intelligent agents. Market analysis indicates that the deployment of AI agents on-chain is not smooth. Although blockchain features permissionless and programmable capabilities, it lacks semantic abstraction and coordination layers suitable for intelligent agents. Research reports have outlined four major structural frictions faced by agents on-chain: opportunity discovery, trusted verification, data reading, and execution processes. Current infrastructure still revolves around human interaction design, making it difficult to support AI autonomous asset management and strategy execution, which constitutes the core bottleneck for large-scale deployment.

The application scenarios of AI agents are evolving. They are beginning to autonomously execute tasks and are developed to hold and configure capital, discover trading and yield strategies. This experimental shift is still in its early stages, but it is fundamentally different from previous modes where agents mainly served as social and analytical tools. Due to its permissionless, composable, open data, and default programmable assets, blockchain naturally becomes an experimental ground. This raises a structural question: if blockchain is programmable and permissionless, why do autonomous agents still face frictions? The answer lies not in the feasibility of execution but in the semantic and coordination burdens above execution.

Blockchain guarantees the correctness of state transitions but usually does not provide native protocol abstractions, such as for economic interpretation, identity validation, or goal-level coordination. Some frictions stem from architectural flaws of permissionless systems, while others reflect the current state of tools, content management, and market infrastructure. In fact, many upper-layer functions still rely on software and workflows that require human participation.

Blockchain’s design centers on consensus and deterministic execution, not semantic interpretation. It exposes primitives like storage slots, event logs, and call traces, rather than standardized economic objects. Therefore, concepts like positions, yields, health factors, and liquidity depth often need to be reconstructed off-chain through indexers, data analysis layers, front-end interfaces, and APIs.

Many mainstream DeFi operations, especially those targeting retail users, still revolve around user interactions via front-end interfaces and signing individual transactions. This user interface-centric model has expanded with the proliferation of retail users, even though a significant portion of on-chain activity is machine-driven. Programmatic operations follow a different path but also have limitations: developers select contracts and asset sets during the build phase and then run algorithms within this fixed scope. Both models are unsuitable for systems that must dynamically discover, evaluate, and combine operations at runtime based on changing goals.

When infrastructure optimized for transaction verification is used to interpret economic states, assess credit, and optimize behaviors around clear objectives, frictions begin to surface. Part of these gaps stems from the permissionless, heterogeneous design of blockchain, while another part arises from the reliance on tools, content management, and market infrastructure built around manual review and front-end mediation.

What distinguishes more autonomous, intelligent behavior flows from traditional on-chain algorithmic systems? The difference is not in automation level or complexity. Traditional algorithmic systems can be highly parameterized, capable of discovering new contracts and tokens, allocating funds across strategies, and rebalancing based on performance. The real difference is whether the system can handle unforeseen scenarios during the build phase.

Traditional algorithms, no matter how complex, only execute predefined logic for preset patterns. They require predefined parsers for each protocol, evaluation logic that maps contract states to economic meanings, explicit credit and standardization rules, and hardcoded decision branches. When encountering unfamiliar situations, they either skip or fail outright. They cannot reason about unknown scenarios, only determine if the current situation matches known templates.

Agent systems based on foundational models change this boundary. They can interpret vague or incomplete goals, generalize to unfamiliar interfaces, reason under uncertainty about trust and normative aspects, explain errors, and make adjustments. These capabilities exist but are imperfect: foundational models can hallucinate, misjudge content, and make seemingly confident but incorrect decisions. In adversarial environments involving capital, “interacting with unforeseen systems” may mean risking funds.

The core point is not that agents can reliably perform these functions today, but that they can attempt tasks in ways that traditional systems cannot, and future infrastructure can make these attempts safer and more reliable. This difference should be viewed as a continuum rather than a strict boundary. Agents will shift more interpretation, evaluation, and adaptive work to runtime reasoning rather than relying solely on preset rules during construction.

This is crucial for understanding friction because what agents attempt to do is precisely what traditional algorithms avoid. Traditional algorithms sidestep discovery frictions by having humans select contract sets during build, relying on whitelists maintained by operators to avoid control layer frictions, using pre-built parsers for known protocols to avoid data frictions, and operating within predefined safety boundaries to avoid execution frictions. Humans complete semantic, credit, and strategy work upfront, while algorithms execute within defined scopes.

Early on, on-chain agent workflows may follow this pattern, but the core value of agents lies in shifting discovery, credit assessment, and strategy evaluation to runtime reasoning rather than preset during build. They will attempt to discover and evaluate unknown opportunities, reason about standards without hardcoded rules, interpret heterogeneous states without prebuilt parsers, and execute strategy constraints around potentially fuzzy goals. Frictions exist not because agents are doing the same as algorithms but more difficult, but because they are trying to do fundamentally different things: operate in an open, dynamic behavioral space rather than within a closed, pre-integrated system.

Structurally, this contradiction does not stem from flaws in blockchain consensus but from the way the overall interaction stack developed around it. Blockchain guarantees deterministic state transitions, finality of states, and consensus on the final state. It does not attempt to encode economic interpretation, intent verification, or goal tracking at the protocol layer. These responsibilities have always been handled by front-end interfaces, wallets, indexers, and other off-chain coordination layers, which always require human intervention.

Even experienced participants reflect this design in current interaction patterns. Retail users interpret states via dashboards, select operations through user interfaces, and sign transactions with wallets—without formally verifying outcomes. Algorithmic trading firms automate execution but still rely on humans to select protocol sets, check anomalies, and update integrations when interfaces change. In both cases, protocols only ensure correct execution; interpretation of intent, handling anomalies, and discovering new opportunities are performed by humans.

Agents compress or even eliminate this division. They must programmatically reconstruct economically meaningful states, evaluate progress toward goals, and verify execution results—not just confirm transactions are on-chain. On blockchain, these burdens are especially prominent because agents operate in open, adversarial, rapidly changing environments where new contracts, assets, and execution paths can appear without centralized review. Protocols only guarantee correct transaction execution, not that economic states are easily interpretable, contracts are standardized, execution paths match user intent, or opportunities are programmatically discoverable.

The following sections will analyze these frictions at each stage of the agent operation cycle: discovering existing contracts and opportunities, verifying their legitimacy, obtaining economically meaningful states, and executing operations around goals.

Friction arises because DeFi’s behavioral space expands in permissionless environments, and relevance and legitimacy are filtered through human social, market, and tooling layers on-chain. New protocols emerge via announcements and are filtered through front-end integrations, token lists, data platforms, and liquidity formation layers. Over time, these signals often form a practical standard to distinguish which parts of the behavioral space have economic value and sufficient trustworthiness.

Agents can be provided with filtered data and credit signals, but they lack the intuitive shortcuts humans use to interpret these signals. From an on-chain perspective, all deployed contracts are equally discoverable: legitimate protocols, malicious forks, test deployments, and abandoned projects all exist as call-able bytecode. Blockchain itself does not encode which contracts are important or safe.

Therefore, agents must build their own discovery mechanisms: scan deployment events, identify interface patterns, track factory contracts, and monitor liquidity formation to determine which contracts should be included in decision-making. This process is not just about finding contracts but also about judging whether they should enter the agent’s behavioral space. Identifying candidates is only the first step. After initial discovery, they must undergo standardization and authenticity verification.

Discovery frictions are not about detecting new deployments per se. Mature algorithmic systems can do this within their strategy scope. For example, monitoring Uniswap factory events and automatically including new pools is a form of dynamic discovery. Frictions appear at two higher levels: judging whether discovered contracts are legitimate and whether they relate to open-ended goals, not just matching preset strategy types.

The discovery logic of a searcher is tightly coupled with its strategy. It knows what interface patterns to look for because the strategy defines them. But an agent executing broader instructions like “configure risk-adjusted optimal opportunities” cannot rely solely on strategy-based filters. It must evaluate new opportunities against its goals, which requires parsing unfamiliar interfaces, inferring economic functions, and judging whether the opportunity should be included in decision space. This is a general autonomy problem, exacerbated by blockchain’s open and dynamic environment.

Control layer frictions arise because identity and legitimacy verification are often performed outside protocols, relying on filtering, governance, documentation, interfaces, and operator judgment. In many workflows today, humans remain key decision-makers. Blockchain guarantees deterministic execution and finality but does not ensure that the caller is interacting with the intended contract. Intent verification is externalized to social context, websites, and manual screening.

Currently, humans rely on the reputation layer of web pages as informal verification. They visit official domains and treat these sites as standard mappings between human concepts and contract addresses. Front-end interfaces then establish a credible baseline, clarifying which addresses are official, which tokens to use, and which entry points are safe.

Intelligent agents cannot inherently interpret branding, social signals, or “officiality” from social context. They can be fed filtered data derived from these signals, but transforming that into durable machine trust assumptions requires explicit registries, policies, or verification logic. They can be configured with whitelists, verified addresses, and credit policies provided by operators. The issue is not that social context cannot be accessed but that maintaining these protections in a dynamically expanding behavioral space is costly, and lacking or imperfect measures leave agents without fallback verification mechanisms humans typically rely on.

On-chain agent-driven systems have already experienced real consequences from weak credit judgments. Cases include agents allegedly depositing funds into honeypot contracts or misjudging address states due to status or context errors, resulting in large token balances being transferred to online “beggars.” These are not core arguments but illustrate how failures in credit judgment, state interpretation, and execution strategies can directly lead to funds loss.

The problem is not that contracts are hard to discover but that blockchain generally lacks the native concept of “this is the official contract of an application.” This absence is partly due to permissionless system features—open deployment, weak standard identities, and heterogeneous interfaces—but still creates coordination challenges for autonomous systems. This issue partly stems from weak open system architecture for standard identities, partly from immature registries, standards, and credit distribution mechanisms.

An agent attempting to interact with Aave v3 must determine which addresses are standard, whether they are immutable, upgradeable via proxies, or under pending governance changes. Humans resolve this through documentation, front-end interfaces, and social media. Agents must verify: proxy patterns and implementation details; management permissions and timelocks; governance-controlled parameter update modules; and matching bytecode or ABI of known deployments.

Without a standard registry, “officiality” becomes an inference problem. Agents cannot treat contract addresses as static configurations. They must either maintain continuously verified whitelists, dynamically infer standards via proxy and governance checks at runtime, or risk interacting with deprecated, compromised, or counterfeit contracts.

In traditional software and market infrastructure, service identities are maintained via institution-controlled namespaces, credentials, and access controls. On-chain, a contract can be callable and operational but may lack standardity from the caller’s perspective at the economic or business layer.

Token authenticity and metadata are the same issue. Tokens appear self-descriptive but lack authoritative validation: token metadata is just code-returned byte data. For example, WETH’s code explicitly defines name, symbol, and decimals, but this is not authoritative. Any contract can implement the same ERC-20 interface with arbitrary values for these functions.

In fact, nearly 200 tokens on ETH share the name “Wrapped Ether,” symbol “WETH,” and 18 decimals. Without consulting CoinGecko or Etherscan, it’s impossible to distinguish the standard version. Agents face this dilemma. Blockchain does not verify uniqueness, nor does it cross-reference any registry or impose restrictions.

On-chain, some heuristic checks exist—such as matching ETH balances to total supply, querying liquidity on major DEXs, or verifying collateral in lending protocols—but none provide absolute proof. Each method depends on thresholds or recursive validation of other contracts’ standards. This is why token lists and registries serve as off-chain filtering layers.

For agents, the core issue is not only the low trustworthiness of metadata but also that standard identities are often established through social or institutional layers, not protocol-native mechanisms. Reliable on-chain identifiers are contract addresses, but mapping human intents like “swap for USDC” to the correct address still heavily relies on external filtering, registries, whitelists, or credit layers.

Decentralized agents optimizing across protocols need to standardize each opportunity into economic objects: yields, liquidity depth, risk parameters, fee structures, oracle sources, etc. From one perspective, this is a common system integration problem. But on blockchain, protocol heterogeneity, direct capital exposure, multi-call state splicing, and the lack of a unified economic model further complicate this burden.

Blockchain generally does not expose standardized economic objects at the protocol layer. It exposes storage slots, event logs, and function outputs, from which economic objects must be inferred or reconstructed. Protocols only guarantee correct contract call outputs; they do not ensure that these outputs map clearly to readable economic concepts or that the same concepts can be retrieved via cross-protocol interfaces.

Thus, abstractions like markets, positions, or health factors are not protocol primitives. They are reconstructed off-chain by indexers, data analysis platforms, front-end interfaces, and APIs, transforming heterogeneous protocol states into usable abstractions. Humans usually see only this standardized layer. Agents can also use it but will inherit third-party assumptions, delays, and trust models; otherwise, they must reconstruct these abstractions themselves.

This issue is increasingly prominent across protocols. Vault share prices, collateralization ratios, liquidity depths in DEX pools, staking rewards—all are fundamental components with economic significance but lack standardized interfaces. Each protocol has its own methods, structures, and conventions. Even within the same class, implementations differ.

Lending markets exemplify this problem. Their economic concepts—supply and borrow liquidity, interest rates, collateral ratios, limits, and liquidation thresholds—are generally consistent, but retrieval paths differ. In Aave v3, market enumeration and reserve state retrieval are separate steps. In Compound v3, each deployment corresponds to a single market, with no unified reserve structure; instead, multiple function calls are needed to assemble a snapshot.

From an agent’s perspective, both are lending markets; but from an integration standpoint, they are structurally different retrieval systems. No common interface exists. Agents must adopt different asset enumeration methods and perform multiple calls to reconstruct state.

Beyond structural differences, this fragmentation introduces latency and consistency risks. Because economic states are not exposed as atomic market objects, agents must perform multiple remote procedure calls across contracts to rebuild snapshots. Each call adds delay, risk of rate limits, and potential inconsistency due to block timing. In volatile environments, interest rates may change between calls; without locking block heights, configuration parameters may not match the current liquidity state.

Users rely on UI caches and aggregation backends to mitigate these issues indirectly. Agents using raw RPCs must explicitly manage synchronization, batching, and temporal consistency. Non-standardized retrieval hampers performance, synchronization, and correctness. The lack of a standardized economic data retrieval scheme means that even protocols implementing similar primitives may expose different states depending on their internal structure. This structural heterogeneity is a core component of data friction.

Accessing economic states on-chain is fundamentally a pull model, even if signals can be streamed. External systems query nodes for required states rather than receiving continuous, structured updates. This reflects blockchain’s core function: on-demand verification, not maintaining application-level persistent state views.

Push primitives exist—WebSocket subscriptions can stream new blocks and event logs in real time—but these do not include most economically meaningful stored states unless explicitly published redundantly. Agents cannot directly subscribe to on-chain data like utilization rates, pool reserves, or position health factors. These are stored in contract storage, and most protocols do not provide native mechanisms to push this information downstream.

The best current approach is to subscribe to new block headers and re-query on each block. Logs can hint at state changes but do not encode the final economic state; reconstructing that state still requires explicit reads and historical access. Agents might benefit from reverse workflows: instead of polling hundreds of contracts, they could receive structured, precomputed state updates pushed to their environment.

Push architectures reduce redundant queries, lower latency in perceiving state changes, and allow intermediate layers to package state as semantically meaningful updates rather than requiring agents to interpret raw storage. This reverse shift is non-trivial: it requires subscription infrastructure, relevance filtering, and transforming storage changes into executable economic events. But as agents become persistent participants rather than intermittent queryers, the inefficiency of pull models becomes more costly.

Treating agents as continuous consumers rather than sporadic clients suggests a more fitting infrastructure. Whether push-based systems are truly superior remains an open question. Massive state changes create filtering challenges; agents still need to determine which changes are relevant, reintroducing semantic reasoning at another layer. The key is not that pull architecture is inherently flawed but that current designs do not consider persistent machine consumers. As agent usage scales, exploring alternative models may be worthwhile.

Execution friction arises because many current interaction layers encode intent translation, transaction review, and result verification within workflows designed around front-end, wallet, and operator supervision. In retail and subjective decision-making scenarios, humans typically perform these functions. For autonomous systems, these functions must be formalized and directly encoded.

Blockchain guarantees deterministic execution based on contract logic but does not ensure that transactions align with user intent, adhere to risk constraints, or achieve expected economic outcomes. In current workflows, user interfaces and humans fill this gap: sequences of operations are combined, wallets provide final “review and send,” and humans often make informal strategic judgments at the last step. They judge safety, acceptable quotes, and may retry, adjust slippage, or abandon if results are unfavorable.

Agent systems remove humans from this cycle. They must replace these functions with machine-native implementations: integrating intent, executing strategies, and verifying results. This introduces the need for completion checks—not just transaction inclusion.

An intent-centered architecture can partly address this by shifting more execution burden from agents to dedicated solvers. By broadcasting signed intents rather than raw calls, agents can specify outcome-based constraints that must be satisfied for execution to be acceptable.

Most DeFi operations are inherently multi-step. A yield strategy might require approval → swap → deposit → borrow → stake. Some steps are independent transactions; others can be bundled via multi-call or routing contracts. Humans tolerate partial completion, returning to the interface to continue. Agents need deterministic orchestration: if any step fails, they must decide whether to retry, reroute, revert, or pause.

This leads to new failure modes often hidden in human workflows: state drift between decision and on-chain execution; non-atomic partial fills; approval and risk limits; path selection and implicit costs.

The core argument for execution friction is that DeFi’s interaction layer relies on human wallet signatures as the final control plane. This layer carries current intent validation, risk tolerance, and informal “reasonableness” judgments. Removing humans turns execution into a control problem: agents must translate goals into behaviors, enforce strategy constraints automatically, and verify outcomes under uncertainty.

This challenge appears in many autonomous systems but is especially acute on blockchain: execution directly involves capital, composable unknown contracts, and faces adversarial state changes. Humans rely on heuristics and trial-and-error; agents must do the same at machine speed, often in a rapidly changing behavioral space. Therefore, the notion that “agents only submit transactions” underestimates the difficulty. Submitting transactions is the simplest part.

Blockchain’s original design does not natively provide the semantic and coordination layers agents need. Its goal is to ensure deterministic execution and state transition consensus in adversarial environments. The interaction layers built on top have evolved around human interpretation: reading states via UI, selecting operations, and manually verifying results.

Agents disrupt this architecture. They remove human interpreters, approvers, and verifiers, requiring these functions to be machine-native. This shift exposes structural frictions in four areas: discovery, credit judgment, data acquisition, and execution flow. These frictions are not because execution is impossible but because most infrastructure still assumes human involvement between state reading and transaction submission.

Bridging these gaps likely requires new multi-layered infrastructure: normalizing cross-protocol economic states into machine-readable middleware; indexing or remote calling services for positions, health factors, and opportunities; registries for standard contract mappings and token verification; and frameworks for encoding strategy constraints, multi-step workflows, and programmatic goal verification.

Some gaps stem from permissionless system features—open deployment, weak identity standards, interface heterogeneity. Others depend on current tooling, standards, and incentive designs. As agent adoption grows and protocols compete to improve integration friendliness, these gaps are expected to narrow.

As autonomous systems begin managing capital, executing strategies, and directly interacting with on-chain applications, the assumptions underlying current interaction layers will become increasingly apparent. Most of the frictions described reflect the evolution of blockchain tools and interaction patterns around human workflows; some arise from permissionless openness, heterogeneity, and adversarial environments; others are common challenges faced by autonomous systems in complex settings.

The core challenge is not just enabling agents to sign transactions but providing reliable pathways for them to perform semantic interpretation, credit judgment, and strategy execution—tasks currently shared with humans and software, often in a combined manner.