Does the future of healthcare depend on Agentic RAG systems?

RAG and the current case of healthcare #

Imagine a healthcare startup where a doctor-researcher asks an AI assistant a critical medical question. Dr. Emily, the co-founder and pediatric endocrinologist, waits as the system, a standard Retrieval-Augmented Generation (RAG) chatbot, searches the company’s medical literature database.

The answer includes a brief mention of a drug name she recognizes (Teplizumab) but little else. Important details about how it works, recent trial outcomes, or usage guidelines are missing. Frustrated, Dr. Emily realizes the static RAG approach can’t fully answer her complex query.

In an era of rapidly evolving medical knowledge, from FDA approvals to new diabetes treatments, her question spans regulatory news, clinical trial data, and treatment guidelines, which a single retrieval cannot handle.

Limitations of RAG#

Dr. Emily’s situation is common. Traditional RAG systems typically follow a simple pipeline:

Query → Retrieve → Generate

The LLM only sees whatever the retriever returns, so the answer depends entirely on that one pass. This improves grounding compared to a plain LLM, but still leaves the system limited, as if the retriever misses something, the LLM has no way to go back and fetch it.

Her question, however, had multiple parts (a new FDA-approved drug, its evidence, and its integration with therapy) that a single-pass retrieval failed to cover adequately. The RAG system retrieved one research paper about Teplizumab, but it didn’t pull in the FDA announcement or guidelines, so the generated answer was incomplete. This limitation occurs because vanilla RAG usually relies on one-shot retrieval based on the user’s query and then stops. If the query or the vector search misses a relevant document, the answer lacks that information, and there’s no second chance for the AI to ask another question or dig deeper. Dr. Emily recalls that Tzield (Teplizumab) was the first new immunotherapy for Type 1 diabetes since insulin, shown to delay the onset of the disease by two to five years. A standard RAG bot with a static knowledge base might not have this up-to-date detail if its data isn’t refreshed. And even if the knowledge base is current, the single-step retrieval approach often surfaces only one aspect of a multipart question. For example, it may return the FDA approval but miss trial results or guideline details, because vanilla RAG does not autonomously break the query into subquestions.

Clearly, a more agentic approach was needed to handle such a nuanced medical query.

Agentic RAG: The new wave of revolution#

Faced with incomplete answers, the startup’s AI engineer, Alex, proposes a solution: augment the RAG system with an agentic workflow. He explains to Dr. Emily that Agentic RAG means giving the AI autonomy to plan and execute multiple retrieval steps. The assistant should behave like a diligent research assistant, not a basic search engine. Instead of returning the first information it finds, the agent will analyze the question’s subparts, formulate tailored searches, use multiple tools, and iteratively refine the results. This way, it can gather, for example, the FDA approval news, and the relevant trial publications, and any treatment guidelines or expert opinions, and then finally synthesize a thorough answer.

By embracing this agent-driven approach, the startup hopes to accelerate their research workflow. Dr. Emily’s question about the new diabetes treatment is an ideal test case: it’s the kind of complex, open-ended query that traditional RAG struggled with, but an autonomous agent could handle effectively. The team dubs their new system MediQuery Agent. The excitement builds as they move from design to implementation, eager to see how much more complete and context-rich the AI’s answers will become.

How Agentic RAG works: Core components#

To build MediQuery Agent, the team designs an architecture with multiple collaborating components, each with a clear role. At a high level, the system consists of an Orchestrator agent, a set of Retriever agents (with specialized tools), a Response Generator, and a Verifier for final checks. Here’s a closer look at each part of this architecture and how they work together to handle Dr. Emily’s question (and others like it):

Orchestrator agent: Planning and delegating searches#

The Orchestrator, an LLM-powered agent, acts as the system’s “brain.” It interprets and breaks down user queries, recognizing keywords and implying necessary searches (e.g., FDA-approved treatment implies official FDA sources). Unlike standard RAG, it uses reasoning and planning to create a sequence of actions rather than a single vector search.

• Tool 1 (FDA lookup): Query an external knowledge source for FDA approval details of Type 1 diabetes therapies.

• Tool 2 (literature search): Retrieve recent research articles or trial data on Teplizumab efficacy and usage.

• Tool 3 (guidelines check): Retrieve any diabetes guidelines or expert statements about integrating new therapies with standard care.

The Orchestrator directs Retriever agents to execute subtasks, concurrently or sequentially. If a retrieval is empty, the Orchestrator can reformulate queries or use different tools. This adaptive flow allows the Orchestrator to iterate and refine until the user’s question is fully addressed.

Retriever agents and tools#

MediQuery uses Retriever agents, each a specialized tool using AI, for planned subqueries. One agent uses a vector database for internal documents via semantic search, another a web search API (Firecrawl) for external information (like FDA announcements and recent news), and potentially a third for medical knowledge graphs. Each Retriever, an LLM, finds information using its tool. 

For example, the FDA Retriever might query “FDA approval Type 1 diabetes new drug 2023” to find press releases about Teplizumab’s approval. The Literature Retriever could search for “Teplizumab Phase III trial results early Type 1 diabetes.” These agents use semantic search and can refine their results by dropping duplicates or prioritizing relevant summaries.

The Orchestrator collects multimodal outputs (documents, snippets) from all Retrievers. For instance, Dr. Emily might receive an FDA press release on Teplizumab, recent journal articles on its clinical trials, and American Diabetes Association guidelines. This richer, dynamically sourced context surpasses older RAG approaches.

Note: It’s important to note that engineers don’t manually predefine each subtask. Instead, they expose the agent to tools (like FDA search, PubMed retriever, and internal guideline DB), and the Orchestrator dynamically decides how to break the query down and which tools to call for each part.

Response generation and verification#

The synthesis phase employs a Response Generator agent, an LLM that compiles the user’s query and retrieves knowledge into a coherent answer. This agent integrates facts, such as Teplizumab’s FDA approval in 2022 for delaying Stage 2 Type 1 diabetes and its ability to postpone onset by about two years. It also notes that current clinical guidelines recommend this therapy for high-risk individuals. All statements are sourced.

The following generation, a Verifier agent (another LLM) fact-checks the draft against retrieved sources. It catches unsupported claims, like exaggerating Teplizumab’s effect on insulin needs, and can correct or prompt for more information. This multi-agent system, while computationally intensive, delivers thorough, evidence-based, and up-to-date medical answers, similar to a diligent human expert, but faster.

Results and benefits#

Deploying the Agentic RAG system produced measurable improvements in both the quality and reliability of responses, particularly for complex medical queries.

• The system significantly improved answer quality for complex medical queries. For example, Dr. Emily’s Teplizumab question received a comprehensive, multi-sourced response that included FDA approval details, clinical studies, and guideline updates within minutes. The inclusion of up-to-date facts, such as the two-year delay statistic and the note that it was the first therapy since insulin, increased confidence in the AI’s knowledge currency.

• The benefits were quantifiable. There was a 30% increase in the number of distinct relevant sources per answer, and answer completeness scores rose significantly. Hallucinations and irrelevant information decreased due to the agent’s verification capabilities, which ensured that all claims were grounded in sources.

• A trade-off was the increase in latency, with responses taking 10–15 seconds compared to three to five seconds previously. However, users found this acceptable given the much higher quality output compared to hours of manual research. Optimization through the use of smaller models for Retriever agents helped to minimize both cost and latency.

• Giving AI agents reasoning abilities and access to tools resulted in responses that were more accurate, current, and thorough. This was particularly valuable in complex and fast-evolving fields such as healthcare.

When to choose Agentic RAG over standard RAG#

With the successful rollout of MediQuery Agent, the team was exhilarated, but Alex cautioned that not every problem calls for such an elaborate solution. There are plenty of straightforward questions where a simpler RAG (or even just a fine-tuned LLM on a fixed knowledge base) suffices. It’s important for engineers and architects to understand when the added complexity of an agentic approach is justified. A useful mental model is to consider the complexity of the query and the required information.

Lessons learned#

The journey of this healthcare startup highlights several key lessons for ML engineers and AI architects building agentic RAG systems:

• Match design with domain complexity: Agentic RAG is most effective in complex, information-rich domains like biomedical research, where completeness and accuracy are critical. For simpler FAQs or static knowledge, standard RAG or a fine-tuned LLM works well. Always consider the trade-off between depth and complexity.

• Keep architectures modular: Clear separation of Orchestrator, Retriever, Generator, and Verifier roles made upgrades easy; for example, swapping the vector DB or web search tool without reworking the whole system. Define clean inter-agent interfaces and leverage frameworks like LlamaIndex or LangChain for orchestration.

• Choose tools strategically: An agent is only as powerful as its tools. This team used a vector DB for internal research and a web search API for FDA updates. Other domains may need specialized connectors (e.g., clinical trial APIs, financial feeds). Ensure agents interpret tool outputs correctly and handle failures gracefully.

• Control autonomy with guardrails: Without constraints, agents can loop or waste resources. The Orchestrator was prompted to limit iterations and stop when enough information was found. Engineers should add similar heuristics and monitor for unnecessary tool use.

• Build in verification: Always include a fact-check step. A Verifier agent (or verification prompt) caught errors the Generator introduced and ensured all claims were supported. In sensitive domains like healthcare, this is critical for maintaining trust.

In short, agentic systems are powerful but complex. Thoughtful architecture, modular design, and rigorous verification are essential for reliability. MediQuery Agent shows how retrieval and reasoning can transform a Question and Answer bot into a proactive research collaborator, which can set the stage for more breakthroughs in healthcare and beyond.

Conclusion#

Agentic RAG represents a shift from generic retrieval to a layered ecosystem of intelligence, where different levels of agency are thoughtfully applied. The challenge ahead is building more powerful agents and knowing when to use them. Those who strike the balance can influence the next generation of AI assistants, applications, and enterprises.