# Core Problems

### Fragmented AI Agent Landscape

The rapid proliferation of AI agents across verticals — trading, research, DeFi, analytics — has led to a highly fragmented environment. Each agent operates in isolation, with its own logic, UI, and access model, resulting in inconsistent user experiences and reduced efficiency in AI-driven workflows.

### Data Silos & Redundant Queries

Accessing high-quality, cross-domain insights requires querying multiple platforms (e.g., Messari, Kaito, CMC). This manual, repetitive process results in data duplication, inconsistent context, and unnecessary cognitive and resource load for users and systems.

### Low Signal-to-Noise Ratio in Responses

Without standardized evaluation and routing mechanisms, AI outputs vary widely in quality. Users often receive generic, incomplete, or irrelevant results due to poor agent selection and lack of real-time performance benchmarking.

### Poor Automation & Execution Flow

Existing AI workflows remain highly manual. There is no unified system for decomposing complex tasks, orchestrating agent collaboration, or executing multi-step processes. This limits the scalability and operational efficiency of AI applications.

### Overpaid Underuse

The average user submits fewer than 300 queries per month while locked into a $20+ subscription tied to a single LLM - leading to up to 85% overpayment relative to actual compute consumption. Trendence optimizes value by pooling access across multiple agents and models, ensuring users pay for precision and outcome - not unused capacity.
