A glimpse into the future with Agentic AI
Modern labs and R&D are entering a decisive shift towards true operational intelligence defined by autonomy, speed, and adaptability. With the increasing complexity of scientific data, traditional AI models built on static, linear, and predefined constraints are no longer sufficient. To tackle these challenges, Agentic AI exhibits a fundamental shift from reactive to proactive orchestration. The term “Agentic” refers to its capacity to act independently, adapt and learn continuously and an intelligence that is highly goal oriented. It consists of machine learning models or AI agents that mimic human decision-making to solve problems in real time. In a multi-agent system, each agent performs a specific sub-task required to reach the objective, and their efforts are coordinated through multi-agent orchestration.1
In high-impact domains like drug development and discovery, AI agents, in collaboration with scientists, have the ability to efficiently and rapidly screen and design novel chemical entities, propose hypotheses, and tune protocols according to gap assessments with precision, allowing research groups to stay at the forefront of innovation.2
The following sections explore how knowledge graphs and context-aware search form the intelligence background of Agentic AI into an actionable, decision-ready science.3
How does Semantics Empower Agentic AI?
Without semantics, Agentic AI cannot exist. In the context of understanding, semantics define business rules and relationships, helping agents understand what data means, not just what it is.1 With semantics, agents understand concepts, reason complex steps and recommend actions. In compliance and governance, semantic models embed policies that serve as guardrails, ensuring AI actions comply with rules and preventing abnormal behavior. Semantics can translate natural language into a context-driven, understandable language, allowing agents to accurately interpret intent and help users talk to their data in real time.2
Without a context-based approach, autonomous systems amplify AI hallucinations with data misinterpretation and make decisions based on pattern recognition rather than scientific understanding. This creates ambiguity, a dangerous prospect in R&D where speed without context is a big liability. Therefore, semantic technology makes Agentic AI a true scientific collaborator as it transforms agents into a genuinely trusted infrastructure that any lab can depend on to validate with governance.
From Simple Data-Driven Models to Knowledge-Driven Intelligence
The R&D landscape has evolved tremendously over the years, moving from basic scientific data capture to complex statistical models to knowledge graphs. With laboratory informatics, most labs have undergone digitization with knowledge graphs introducing explicit relationships, shared scientific context connecting the dots and representing them as structured knowledge rather than siloed and disconnected data.
Knowledge graphs are excellent at organizing and contextualizing scientific data, but they lack the capacity to reason, adapt, and act independently as an operational platform. They are good at providing intelligence-ready platforms but not intelligence platforms. How to bridge this gap? Agentic AI can be the connecting link that activates a knowledge graph into operational data with intelligence. Through multi-agent orchestration, it utilizes semantic context, plans actions, adapts from past mistakes, reasons with scientific context, and transforms static knowledge into an adaptive, goal-driven operational intelligence.
R&D has reached an Inflection point for adopting AI
With the widespread integration of AI tools, copilots, and predictive models that have dramatically accelerated discovery processes, the scientific industry is standing at a pivotal moment. A fundamental shift in how we use AI is underway. Thanks to an increased interest and general awareness of AI among customers and vendors in different domains.
Gen AI and traditional automation, which rely heavily on linear, static predictions, are reaching their limits when faced with the complexity of scientific contexts and true intelligence. From isolated analytics and static models to integrated intelligence systems, the transformation is already underway towards an adaptive, goal-driven platform that can actively reason and experiment with context-based intelligence. While knowledge graphs lay the essential groundwork for organizing vast datasets, Agentic AI helps R&D take the next significant leap with autonomy and true intelligence. Achieving true autonomy in R&D requires more than advanced algorithms; it demands an understanding of context-driven science, tying the different knowledge graphs into a single unified source of truth.3
What does Agentic AI mean for BioTech360?
Platforms built without semantics are a technical debt that today’s labs can’t afford. LabVantage’s BioTech360 is based on semantic-first architecture. Its Findable, Accessible, Interoperable, and Reusable (FAIR) foundations and intelligence-ready R&D ecosystem lay out the conditions required for autonomy. It gives R&D organizations a strategic advantage by turning today’s digital transformation into tomorrow’s agentic innovation.
Integrating Agentic AI with BioTech360 offers significant opportunities. It will enhance its domain-specific modules into autonomous, knowledge-driven discovery engines. By operating on a semantic-first foundation, AI agents will not only think but also reason across biological and chemical knowledge represented in the knowledge graph to accelerate design, optimization, and decision-making.
With multi-agent orchestration, agents can assist in faster lead identification, robust context-based search for specific antibodies, strains and plasmids from a vast repository, could even predict and establish possible risks and adverse events, and design therapeutic strategies based on the context. In drug discovery platforms, agents will have the capability to provide accelerated high-throughput screening with absolute accuracy using structure-activity relationships (SAR) and context-based hit-to-lead compound optimization.
The Dawn of Agentic AI in R&D
The CAGR for Agentic AI is reported to be 42.8% from 2025 to 2032, and the projected market growth is 88.35 billion USD by 2035, with a 171% ROI.4 These are not some mere predictions. It sets a trend for the future. Organizations that invest in Agentic AI early will not only be data-driven but intelligence-ready.
If knowledge graphs make scientific data understandable, Agentic AI will take it a step further by turning it into actionable, intelligent data. Agentic AI can reason, act, and automate multi-step scientific tasks, reshaping expectations for innovation in life sciences. But while headlines focus on advanced AI capabilities, the real strategic question for R&D leaders is more grounded
“Are my data ready for AI?”
At LabVantage, we help you lay this critical foundation with BioTech360 through our semantic knowledge platform and extend it with Agentic AI capabilities that automate reasoning across your R&D workflows.
The result? Your organization becomes not just data-driven, but truly intelligence-ready.
Having experience of over 40 years and a pioneer in laboratory informatics, LabVantage enters a new chapter of embedding autonomic intelligence into complex laboratory solutions. To know more, visit us at LabVantage/BioTech360
References:
- What is Agentic AI?
- Agentic AI for Scientific Research: Autonomous agents transforming experiment design
- Model Context Protocol (MCP) and Its Impact on AI-Driven Startups
- Fortune Business Insights
