Agentic AI

Introduction: The Language of the Cell While genomics maps the static blueprint of life, transcriptomics captures its dynamic execution. If the genome is the dictionary, the transcriptome is the conversation—the precise subset of genes being expressed by a specific cell, at a specific moment, under specific conditions. For decades, bulk RNA sequencing averaged these conversations across millions of cells, giving us a cacophonous blend that masked individual cellular identities. The advent of single-cell RNA sequencing (scRNA-seq) changed everything, allowing us to listen to individual cellular voices. ...

The genome is the ultimate source code. For decades, computational biologists have relied on alignment algorithms, hidden Markov models, and specialized machine learning to decode it. Today, a new paradigm is taking hold: DNA foundation models. By treating the genome as a vast, continuous text and training large language models (LLMs) on billions of nucleotides, researchers are teaching AI to “read” the book of life in its native language. In this fifth installment of our Agentic Omics series, we examine the state of the art in genomic AI. We explore how models like DNABERT-2, Nucleotide Transformer, Evo, and HyenaDNA are moving beyond sequence classification to predict gene expression, identify regulatory elements, and quantify variant effects. Crucially, we will dissect the architectural innovations that make this possible—and the biological complexities that still confound these models. ...

When a new foundation model in computational biology is released, the accompanying paper inevitably features tables of bolded numbers demonstrating state-of-the-art performance. Whether it is predicting protein structures or annotating single-cell data, the claims are often spectacular. But how do we truly know if these AI systems work in ways that matter to biology, rather than just optimizing arbitrary computational metrics? For the vision of Agentic Omics to become reality—where autonomous agents orchestrate models like AlphaFold and DNABERT-2 to drive drug discovery—we need a rigorous understanding of when these models succeed, when they hallucinate, and when their benchmarks deceive us. Claims of AI breakthroughs are only as strong as their evaluation methodologies. ...

The bottleneck for AI in computational biology is rarely a shortage of sophisticated models; it is the sheer difficulty of making biological data AI-ready. The “Agentic Omics” vision—where autonomous AI agents orchestrate domain-specific models to accelerate drug discovery—fundamentally rests on the assumption that these agents have access to standardized, clean, and computable data. In this post, we explore the unglamorous but critical foundation of omics AI: the data infrastructure. We trace the journey from raw sequencing reads to the structured tensor formats required by modern foundation models, exploring the evolving standards, the scale of the challenge, and how cloud infrastructure is adapting. ...

In the first post of this series, we mapped the omics landscape: genomics, transcriptomics, proteomics, metabolomics, metagenomics, phenomics. The next question is obvious: why did AI suddenly get so good at several of these fields at once? The short answer is that biology turned out to be unusually compatible with the same family of models that transformed natural language processing. DNA, RNA, proteins, and even single-cell expression matrices are not “language” in any literal sense, but they are structured symbol systems with long-range dependencies, rich context, and vast quantities of unlabeled data. That is exactly the setting where self-supervised foundation models thrive. ...

Welcome to the first installment of Agentic Omics: When AI Reads the Book of Life. In this 24-part series, we will systematically review the state of the art of Artificial Intelligence (AI) across all major omics disciplines. We will explore how large language models, foundational transformer architectures, and eventually fully autonomous “Agentic Omics” systems are orchestrating domain-specific models to accelerate drug discovery, personalized medicine, and our fundamental understanding of biology. ...

As we conclude our series on Agentic SRE, it’s time to pull back and look at the broader horizon. Over the past 11 posts, we’ve explored how autonomous agents are transforming incident response, change management, chaos engineering, and disaster recovery. But what happens when these point solutions fuse into a cohesive, system-wide paradigm? The transition from human-driven runbooks to AI-assisted operations was profound, but the shift from single-agent task execution to multi-agent, self-architecting systems will redefine the very nature of infrastructure. As we look toward 2027 and beyond, the technological landscape is shifting from fragmented AIOps tools to dynamic “agentic ecosystems” [1]. ...

If you ask an SRE in 2026 what their biggest fear is, it’s rarely “the site is down.” Agents like Sherlocks.ai or Azure’s SRE Agent handle that before the human even wakes up. The new fear is subtler: de-skilling. In the previous posts of this series, we’ve built a technological marvel: autonomous incident response, self-healing infrastructure, and AI-driven chaos engineering. But technology doesn’t exist in a vacuum. As we hand the pager to AI agents, the role of the human Site Reliability Engineer is undergoing its most radical shift since Google coined the term in 2003. ...

It is relatively easy to build an SRE agent that can solve a single, well-defined problem in a demo environment. You give it a prompt, access to a few tools, and watch it restart a pod or query a log file. It feels like magic. But taking that agent and asking it to run 24/7, monitor thousands of services, handle concurrent incidents, and never hallucinate a destructive command is a different engineering challenge entirely. It moves us from the realm of “AI scripting” to distributed systems architecture. ...

Disaster Recovery (DR) has traditionally been the “eat your vegetables” of IT operations: universally acknowledged as vital, but often neglected until a crisis forces the issue. In the pre-agentic era, DR testing was a high-stakes, high-effort event—a “Game Day” that required weeks of coordination, executive sign-off, and often a weekend of anxious monitoring. The result? Most organizations test their full DR plans annually at best. Between these rare tests, infrastructure drifts, configurations change, and the “tested” recovery plan slowly decays into fiction. ...