Agentic AI Is Ready to Solve Scientific R&D's Hard Problems

The conversation around AI in scientific R&D has shifted dramatically in a short period of time. Fields like chemistry, materials science, and life sciences have relied on machine learning for decades before the arrival of ChatGPT, but the last few years have triggered a significant change in how we think about the scientist-AI partnership.

How We Got Here

Understanding where we are in 2026 requires a quick look at how the conversation evolved. Each year had a distinct character.

2023: The Year of Exploration

In 2023, the primary theme was explosive growth and experimentation. Following the public debut of advanced Large Language Models (LLMs), AI moved from being a specialized technical topic to boardroom priority.

In 2023, the focus was:

• GenAI applications having a breakout year across image, video, coding, and voice, driving $18B in VC and corporate investment according to the 2023 State of AI Report
• Most companies were in a discovery phase, focusing on identifying potential applications for AI
• Most organizations still in discovery mode, identifying potential applications rather than deploying them at scale

2024: The Year of Adoption

By early 2024, the results were becoming hard to ignore. According to Stanford HAI's AI Index Report, the business case had been proven, leading to rapid investment and organizations moving beyond just experimentation.

In 2024, the focus shifted to:

• Generative AI investment nearly octupling from 2022 to reach $25.2 billion, with AI mentioned in nearly 80% of Fortune 500 earnings calls
• Measurable business impact taking hold: 42% of organizations reported cost reductions from AI implementation and 59% reported revenue increases
• AI-related regulations in the U.S. grew 56.3% in a single year, yet most organizations had only mitigated a small portion of their actual risk exposure

2025: The Rise of the AI Agent

While 2024 was about proving that AI could deliver value, 2025 was the year R&D leaders realized that standard chat interfaces weren't enough for complex science. The conversation shifted toward operationalizing the agentic foundation.

According to McKinsey’s 2025 data, this was the year of stubborn growing pains. While nearly 88% of organizations were regularly using AI, the majority were still stuck in the pilot phase. The high performers who broke through were those who stopped treating AI as a search engine and started treating it as a workflow orchestrator.

In 2025, the focus became:

• Realizing that to get 10x value, you couldn't bolt on an LLM to an old process
• Saw the first real-world deployments of agents capable of acting in the real world
• In 2025, 64% of respondents reported that AI was finally enabling true innovation, helping them explore white space in materials and molecular design that was previously too complex for human-only teams

2026: The Era of Agentic Systems

The current situation reflects a significant change. The move from AI as an assistant to AI as an orchestrator is already underway, and the organizations leaning in are operating at a different level of scientific throughput.

So what actually makes an agentic system different? It's not just a smarter chatbot. It's an architecture built around a continuous loop: the agent perceives its environment, reasons over what it finds, sets a goal, decides on an action, executes it, and then learns from the outcome all before starting the cycle again. Each pass through that loop is more informed than the last and three capabilities make it powerful in practice.

Tool Usage

Systems of agents can call external tools dynamically to assist them in executing code, querying databases, invoking APIs, and interacting with external systems, all within a single reasoning loop. Agents are taking actions, evaluating real outputs, and deciding what to do next based on what it gets back instead of just retrieving and summarizing.

Memory Systems

Well-designed agents operate with multiple memory layers. Short-term memory, also known as working memory, holds the immediate reasoning state. Episodic memory stores what has been tried before, what failed, and what succeeded. Semantic memory encodes domain knowledge that can be referenced at decision points. Procedural memory stores and recalls skills, rules and learned behaviors that enable an agent to perform tasks automatically without explicit reasoning each time. Together, these layers mean the agent builds on prior context rather than starting from scratch each session.

Planning and Reasoning

Agentic systems can now break down long-horizon goals into executable sub-tasks. The typical architecture involves an orchestrator agent that reasons about the overall objective and delegates to specialized subagents. The reasoning loop is recurring in which it observes, reflects, acts, and evaluates. It will revise the plan when outputs don't match expectations. This capacity to self-correct based on real results is what separates a true agentic system from a scripted workflow.

For scientific R&D, this architecture changes what's possible. The constraint was never data or models. It was the ability to reason across them continuously.

The Intelligence Loop in Practice

Tool use, memory, and planning are each meaningful on their own. But the real shift happens when they operate together as a unified system. The agent isn't just answering questions or executing tasks in isolation; it's maintaining context, acting on the world, learning from what it gets back, and adjusting its next move accordingly.

For the scientist, this changes the nature of the partnership. Rather than managing each step of a workflow, the researcher shifts toward defining objectives and evaluating outcomes. The operational work of moving between data, tools, and decisions becomes something the system handles, freeing the scientist to focus on what only scientists can do.

Looking Ahead

The R&D teams redesigning their workflows around agentic architectures, rather than layering AI on top of traditional processes, will be the winners unlocking improvements in discovery speed, experimental efficiency, and the ability to explore scientific white space that was previously unreachable.

To dive deeper into our latest on Agentic AI, explore our R&D Leader's Playbook for Agentic AI Success or read our recent interview with Lab Manager, Agentic AI and the Future of Scientific R&D.