LlamaIndex passes RAG so agents can make tough decisions

Common AI orchestration features LlamaIndex has introduced Agent Document Workflow (ADW) a new architecture that the company says bypasses repetitive processes (RAG) and increases agent productivity.

As call systems become more effective, this approach can give organizations the opportunity to improve their decision-making skills.

LlamaIndex says ADW can help agents manage “operational challenges that go beyond simple outsourcing or comparison.”

Some agents’ goals are based on RAG systems, which provide the information they need to complete tasks. However, this approach does not allow agents to make decisions based on this information.

LlamaIndex provided real examples of how ADW can work effectively. For example, in contract evaluations, human resource analysts must extract key information, useful for reference management, identifying potential risks and making recommendations. Once deployed in the workflow, AI agents follow the same process and make decisions based on the documents they read to review agreements and information from other documents.

“ADW solves these problems by viewing documents as part of business information,” LlamaIndex said in a blog post. “The ADW system can keep the government on track, apply business rules, connect different departments and take action based on the content of the document – not just analysis.”

LlamaIndex has previously stated that RAG, although an important method, they are oldespecially for businesses that want to make powerful decisions using AI.

Understanding the context of decision making

LlamaIndex has developed an architecture to integrate its LlamaCloud parsing and support capabilities. “It builds systems that can understand what’s going on, save the world and run multiple processes.”

To do this, each channel has a document that works as a singer. It can direct agents to use LlamaParse to extract data from a document, save the document’s state and structure, and then extract the data from other data sources. From here, agents can begin to make recommendations on the use of contract analysis or other decisions that can be made in different situations.

“By maintaining the state throughout the process, agents can solve many problems that go beyond simple extraction or matching,” the company said. “This approach allows them to have more information about the documents they are processing while coordinating the various parts of the system.”

Different types of agents

Agent singers it’s an emerging field, and many organizations are still exploring how agents – or multiple agents – can work for them. Orchestrating AI agents and services can be great conversation this year as agents move from single systems to multi-agent ecosystems.

AI assistants are complementary to what RAG offers, that is, the ability to obtain information based on business intelligence.

But as more and more businesses start deploying AI assistants, they also want them to perform many of the tasks that human workers do. And, for these complex applications, “vanilla” RAG is not enough. One of the leading strategies that businesses consider is RAG agentwhich increases the awareness of agents. Samples can decide if they need to find information, the tool they will use to find the information and if the topic they just picked up is relevant, before coming up with results.