The effectiveness of AI agents is fundamentally limited by their data inputs. In the agent era, access to clean and structured web data is no longer a commodity but a critical piece of infrastructure, making tools that provide it immensely valuable. AI models have brains but are blind without this data.

The secret to effective enterprise agents is a "living context graph" that continuously crawls and maps all of an organization's data assets—code, databases, APIs, documents. This graph provides the essential, often undocumented, context agents need to reason and execute complex tasks accurately.

A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

Most tech vendors offer data only on sites within their proprietary network. Right.AI upended this by creating a digital twin for every research site globally, regardless of affiliation. This provides a comprehensive, unbiased view of the entire landscape, eliminating the limitations and blind spots of closed ecosystems.

AI agents are simply 'context and actions.' To prevent hallucination and failure, they must be grounded in rich context. This is best provided by a knowledge graph built from the unique data and metadata collected across a platform, creating a powerful, defensible moat.

The agent development process can be significantly sped up by running multiple tasks concurrently. While one agent is engineering a prompt, other processes can be simultaneously scraping websites for a RAG database and conducting deep research on separate platforms. This parallel workflow is key to building complex systems quickly.

The most powerful AI systems consist of specialized agents with distinct roles (e.g., individual coaching, corporate strategy, knowledge base) that interact. This modular approach, exemplified by the Holmes, Mycroft, and 221B agents, creates a more robust and scalable solution than a single, all-knowing agent.

One of the most immediately useful applications of agentic AI is creating persistent research bots. The "Opportunity Radars researcher" demonstrates this by continuously scanning the web for studies and surveys to inform a use-case database. This 24/7 automated intelligence gathering is a powerful, focused application of agents.

Classic RAG involves a single data retrieval step. Its evolution, "agentic retrieval," allows an AI to perform a series of conditional fetches from different sources (APIs, databases). This enables the handling of complex queries where each step informs the next, mimicking a research process.