2. Market Background and Problem Analysis

By 2026, the Web3 ecosystem is transitioning from experimental prototypes to production-grade applications. However, several core bottlenecks continue to hinder large-scale adoption. These issues are not isolated; they stem from intertwined deficiencies in trust, privacy conflicts, and execution inefficiencies.

2.1 Reliability Crisis of Real-World Data Injection

In DeFi and RWA domains, on-chain applications are highly dependent on external data inputs. Existing oracle systems (such as Chainlink or Band Protocol) have revealed limitations in high-frequency scenarios: latency issues amplify slippage, manipulation attacks (e.g., flash-loan price feed manipulation) are frequent, and adjudication of unstructured events (such as sports results or governance vote finality) often relies on single nodes or static rules, rendering disputes difficult to resolve. Industry data indicates that over 30% of DeFi hacking incidents in 2025 originated from oracle failures. This reflects a deeper question: how can a distributed system generate “truth signals” that are challengeable and final, rather than unidirectional broadcasts vulnerable to economic attacks?

2.2 Absence of Economic and State Management for AI Entities

The rise of AI Agents (e.g., autonomous agents in DeFi or AI-driven DAOs) introduces new opportunities, but current frameworks (such as LangChain or OpenAI tool invocation) are limited to transient interactions, lacking persistent state tracking and economic autonomy.

For example, an AI Agent handling multi-round liquidation cannot accumulate credit history or dynamically adjust risk thresholds, leading to fragmented decision-making. Gartner predicts that by 2027, AI economies will account for 15% of Web3 value. Without reliable state machines and incentive-alignment mechanisms, however, Agent collaboration risks falling into prisoner’s dilemmas, Sybil attacks, or selfish behavior.

2.3 Privacy–Compliance Trade-off Dilemma

High-value applications (such as institutional RWA or cross-border payments) must simultaneously satisfy privacy requirements (preventing data leakage) and compliance requirements (auditability and AML). Traditional zero-knowledge solutions (e.g., zk-SNARKs as used in Zcash) offer strong privacy but incur high computational overhead, limiting real-time complex condition processing. Conversely, compliance channels (e.g., USDC transfers via Circle) often sacrifice anonymity.

Regulatory frameworks such as the EU’s MiCA and U.S. SEC guidance exacerbate this tension: projects must prove transaction legality without exposing user details. As a result, many applications adopt hybrid custodial models, reintroducing centralized risk.

2.4 Ecosystem Fragmentation and Redundant Engineering

Different vertical domains (gaming incentives, DeFi derivatives, AI prediction markets) independently build data verification, settlement logic, and coordination protocols, leading to resource waste and interoperability barriers.

For example, a gaming DApp’s participation-tracking system cannot directly interface with a DeFi liquidation engine, forcing developers to reimplement oracle interfaces or privacy circuits. Messari research indicates that such fragmentation increases overall Web3 development costs by 40% and inhibits composable cross-chain value growth.

manadia is designed precisely to address these pain points: by providing a unified protocol framework that bridges real-world data, AI decision-making, and value execution pathways through economic games, verifiable computation, and state consensus mechanisms—shifting the ecosystem from “isolated innovation” to “composable infrastructure.”