How Agentic AI Is Challenging the Boundaries of Life, Intelligence and Innovation

Agentic artificial intelligence is redefining the scientific landscape, evolving from an advanced tool to something that resembles a living system, capable of reasoning, planning, and learning across disciplines. These dynamic AIs not only accelerate research and innovation but also increasingly act with autonomy, raising fundamental questions about oversight, reproducibility, and the very nature of life and intelligence. Their emergence compels us to reconsider what it means to be “alive” as we shape the evolutionary pressures that guide both their development and our shared future.

Global scientific output doubles approximately every nine years, resulting in over five million new articles annually and generating substantial volumes of experimental data. Traditional research teams cannot keep pace with this scale. Agentic AI sets research agendas, identifies connections in complex data, and manages workflows from hypothesis to results. These systems expand research team capabilities and create new sources of innovation.

What Is Agentic AI?

Agentic artificial intelligence refers to AI systems that not only process information or generate content but also initiate, plan, and execute actions toward open-ended goals, adapting as they learn from experience. It is unlike the rule-based chatbots or recommendation engines most people encounter, which are limited to responding within programmed boundaries. What makes agentic AI distinct is its “agency”. It determines the most effective way to achieve outcomes, sequences steps, interprets setbacks, and moves beyond static instructions. This agent-like autonomy enables agentic systems to function as innovative research partners, not just digital assistants. As such, they are capable of expanding their functions over time, blurring the boundaries between software tools and entities that appear—at least in some respects—to be alive.​

Literature Reviews and Data Analysis in Hours, Not Weeks

Literature reviews, traditionally a labor-intensive challenge, are now being completed in hours rather than weeks thanks to agentic AI tools. Systems such as Anara and similar tools scan academic databases, preprints, and recorded presentations. They dynamically adjust queries and cluster findings for synthesis. What once required a team of experts now produces thorough, bias-resistant knowledge bases with unmatched speed and efficiency. Institutions benefit from faster innovation cycles and a reduced risk of missed insights.

Laboratories must efficiently analyze large volumes of data. Agentic AI processes diverse datasets, identifies outliers, recommends alternative analyses, and visualizes patterns that may be missed. In genomics and epidemiology, these agents automate analytical tasks in parallel. Organizations report that agentic AI designs and executes follow-up experiments, adjusting parameters to optimize results and support reproducibility. This approach yields more robust discoveries in less time, enabling researchers to focus on strategic questions.

Drug Discovery Gets a Digital Colleague

Long considered slow and costly, drug development is being transformed by agentic AI. These autonomous systems analyze biomedical and chemical databases, map disease pathways, and identify promising drug targets at previously impossible speeds. During compound screening, agentic AI simulates molecular interactions, reprioritizes libraries as new data arrive, and learns continuously from experimental feedback. Leading pharmaceutical firms report research and development cost reductions, which dramatically accelerate timelines and expand the set of viable new drug candidates.

The protein folding problem in molecular biology also demonstrates the capabilities of agentic AI. For decades, researchers were unable to predict three-dimensional protein structures from amino acid sequences. DeepMind’s AlphaFold now predicts structures at scale, representing a significant advance. AlphaFold 3 predicts static shapes, folding pathways, synthetic enzyme interactions, and complex dynamics. Researchers continue to address challenges such as protein aggregation and interactions within living cells.

Rethinking the Scientific Team

Agentic AI expands the capabilities of researchers. Human-AI teams design experiments, support grant writing, and enable cross-disciplinary collaboration. Multi-agent systems manage projects from start to finish and connect specialist teams for integrative analysis. This approach allows small organizations and startups to undertake projects that were previously limited to large, well-resourced research consortia.

Greater autonomy introduces new risks. Biased training data, opaque AI reasoning, and challenges in auditing automated decisions complicate reproducibility and accountability in science. In high-risk fields such as healthcare, lapses in safety protocols or interpretation can have significant consequences. Stakeholders require robust governance, clear human-AI boundaries, and careful calibration of each system’s impact on the research ecosystem.

Agentic AI deployments raise ethical dilemmas: privacy, authorship, misuse risk, and sustainability issues. Regulators are beginning to address these challenges by using AI for submission triage, continuous oversight, and the development of new standards for liability in clinical trials and related areas. Responsible deployment must ensure transparent operations, establish clear accountability, and promote ongoing collaboration between machines and humans.

Agentic Intelligence and What It Means to Be “Alive”

The rise of agentic AI changes our understanding of intelligence and the debate over whether AI is “alive.” In a recent essay, David Mou compares biological evolution to AI development and urges examination of emergent machine intelligences. He argues that the processes underlying gene complexity and consciousness may parallel the development of advanced AI models.

AI models, like genes, evolve by optimizing fitness scores, which measure how well a model achieves its designated task. Biology selects for survival, while agentic AI systems optimize for goals defined by their creators, such as accuracy or engagement. Silicon-based AI evolves much faster than carbon-based biological evolution, with models improving and transferring knowledge across generations.

As agentic AI evolves, society faces questions about intelligence, autonomy, and consciousness. Some theorists argue that AIs with long-term memory, multi-step planning, self-reflection, and recursive learning may begin to mirror aspects of biological consciousness, although their ethical instincts may differ.

However, philosopher and cognitive scientist Bernardo Kastrup argues that digital liveness in AI is marked by reliable, predictable responses and smooth engagement, but fundamentally lacks the disorder, unpredictability, and emergent private consciousness that define biological life. While AI can create narrative and emotional continuity with users, Kastrup asserts that this presence remains distinct from organic beings, as machine intelligence does not possess the embodied, unpredictable substrate of true living systems.

What Happens Next?

Humanity is rapidly approaching a new era, one in which we will share our world with machine intelligence that is capable of learning and evolving independently. Understanding and influencing the forces that shape this artificial evolution is now an urgent global concern. Unlike biological evolution, these systems develop on paths we have designed, and the outcomes will depend not only on technical advance but also on our collective wisdom and foresight. Our ability to steer the growth of advanced AI may prove to be the pivotal test of our time.

For now, Agentic AI continues to reshape science. Traditional bottlenecks, including slow peer review, manual data preparation, and isolated silos, give way. By 2030, AI agents will transform most laboratories, driving discovery, analysis and decision-making. It’s leading science into a new era that remains smarter, more inclusive, and creative, with ethics and accountability keeping pace with ambition