We’re arguably experiencing the Rise of the Robots right now — in the form of the rapidly spreading adoption of AI agents, which are able to complete far more complex tasks and make AI chatbots seem old-fashioned and limited.
However, there are still bumps in the road. Even the smartest new autonomous agents are hitting their limits when they’re assigned actions or required to make decisions which cross domains. These activities come up more often than you may realize, because we humans tend to cross domains in our thinking and analysis without really noticing.
For example, customer churn analysis, supplier risk assessment, forecasting operational bottlenecks, and the revenue impact of delays in hiring more software engineers all require drawing on data or other inputs from at least three domains.
Even though AI agents are built for multi-step tasks, they still find sequential task execution to be a challenge. They can end up floundering when executing multi-step processes that require context from several different resources and logical realms, such as adaptive surveys that need real-time sentiment analysis and follow-up questioning.
Another element in the challenge is contextual understanding. Traditional business intelligence tools rely heavily on relatively static, internal datasets, which forces the data to remain within silos.
Resolving this isn’t as simple as giving an AI agent access to all data repositories, because different departments tend to speak different data languages, use different systems, and can apply the same word or terminology in very different ways. What’s more, LLMs often struggle to interpret the variations in domain-specific terminology and map user intent accurately across different business areas, resulting in fragmented and/or inaccurate insights.
What’s needed is to find a way to unify input data, empower AI agents to understand the full import of all the data they draw on, and equip them to find the best ways to move through complicated cross-domain tasks. Otherwise agentic AI will never meet its full potential.
Multi-agent systems for sophisticated workflows
Many of the elements in cross-domain tasks are concurrent rather than consecutive, which means that they need to be completed at the same time. If it’s all left to one agent, then that agent has to understand all the different aspects of the task and identify and draw on the right data for the job while also carrying out another part of the task in another domain.
One way of simplifying the puzzle is to deploy multiple specialized AI agents, each one tailored to handle specific tasks or understand a particular domain. Crew AI offers an open-source framework that’s designed to build collaborative teams of AI agents that coordinate to achieve their goals.
Each agent focuses on a particular aspect of the complex query, having been trained to understand the jargon and context of “their” sphere of responsibility. The agents divide the task between them and then bring together the results of their work, overcoming the difficulty of sequential task execution.
Smart data retrieval for integrated web data discovery
Cross-domain AI agents rely on relevant, accurate, up-to-date information from all the relevant domains, often extending well beyond existing internal databases. Data retrieval tools need to be upgraded with Retrieval Augmented Generation (RAG) methodologies, which combine data retrieval with generative models to improve contextual understanding.
Live web data is a treasure trove of data unconstrained by departmental divisions. As an infrastructure solution that allows agents to pull information from the internet, Bright Data uses advanced, powerful data retrieval protocols that can understand context, handle variability, import content from any number of domains and integrate with data intake workflows. These tools operate effectively across internal data domains and throughout the web to extract meaning, not just text, from structured internal and unstructured external datasets.
After all, web data enables AI agents to make better use of structured internal data products and produce more relevant cross-domain insights. With more sophisticated data extraction, AI agents can contextualize external data against internal metrics for real-time web-to-enterprise linking, like connecting a news story about a supplier to actual risk scores in procurement systems and automatically updating the internal dashboard or even opening an order in the ERP system.
Semantic layer for better understanding
Linking live, unstructured web data with structured internal datasets requires one more element — a semantic layer. This standardizes information across departments and ensures that terms are interpreted consistently in different domains, helping to bridge gaps between siloed datasets.
For example, Wren AI brings a powerful semantic engine that provides the business context and governance that AI agents need to interact with internal enterprise data. This semantic layer can then be used to build knowledge graphs and ontologies that tag and align disparate web data with enterprise vocabularies.
Mapping the relationships between entities using a semantic layer and knowledge graphs equips AI agents to perceive more accurate, context-based relationships between data repositories. Connecting curated, semantically rich internal data products with real-time live web data is the final step that turns AI agents into cross-domain thinkers.
Cross-domain AI agents can be a reality
Although operating AI agents across enterprise domains remains challenging, all the elements are in place to make it happen. Fielding a sophisticated system of multiple AI agents and equipping them with the semantic layer that unlocks and connects internal datasets with live web data empowers them to bridge domain siloes and unlock richer cross-domain insights.