Agentic AI

Introduction: Standing at the Inflection Point As we conclude the Agentic Omics series in March 2026, we find ourselves at a genuine inflection point. The past two years have witnessed extraordinary progress: AlphaFold 3’s extension to protein complexes and ligands, the emergence of 7B-parameter genome models like Evo, foundation models for single-cell biology achieving clinical utility, and the first wave of agentic systems orchestrating multi-step scientific workflows. Yet we also face sobering realities: Phase III clinical trial results remain the ultimate arbiter of success, regulatory frameworks are still crystallising, and the gap between computational prediction and biological causality remains stubbornly wide. ...

Introduction: The Closed Loop of Discovery For centuries, the scientific method has followed a familiar rhythm: a human scientist observes a phenomenon, formulates a hypothesis, designs an experiment, executes it manually or with basic automation, analyses the results, and iterates. This cycle — hypothesis, experiment, analysis, refinement — is the engine of scientific progress. But it’s also a bottleneck. Each iteration takes days, weeks, or months. Human bandwidth limits the search space we can explore. And crucially, the loop is open: the scientist must close it manually, bringing their intuition and experience to bear at every step. ...

The Dual-Use Dilemma In July 2024, the Arc Institute published a paper in Science describing Evo, a 7.6 billion parameter foundation model trained on 300 billion nucleotides spanning all domains of life. The model could generate functional DNA sequences, predict fitness effects of mutations, and even design novel regulatory elements. It was a scientific breakthrough—and immediately raised a question that every researcher in biological AI now confronts: Could this same technology be used to create biological weapons? ...

Introduction: The Open Science Paradox In May 2024, Google DeepMind published AlphaFold 3 in Nature, describing a system that could predict the structure of protein complexes with DNA, RNA, ligands, and small molecules—a dramatic leap beyond AlphaFold 2’s protein-only predictions. But there was a catch: the code wasn’t released. For six months, researchers could read about the breakthrough but couldn’t reproduce it, build on it, or verify the claims independently. ...

The Agentic AI space is maturing fast. This week brought clear winners in the framework wars, a convergence among coding agents, and a decisive shift toward enterprise security. Here’s what you need to know. The Multi-Agent Framework Landscape: Winners Emerge LangGraph: The Production Choice If you’re building agents that need to run reliably in production, LangGraph has become the default choice. Companies like Uber, LinkedIn, and Klarna have had LangGraph agents running in production for over a year. ...

Introduction No single AI agent can master all of biology. A genomics specialist doesn’t reason like a proteomics expert. A literature review agent has different skills from an experimental design agent. Yet biological discovery demands all of these perspectives working together. This is the promise of multi-agent systems for biology: collaborative AI teams where specialized agents debate, coordinate, and peer-review each other’s work — mimicking the collaborative nature of real scientific teams. ...

Introduction: The Precision Oncology Imperative Cancer is not one disease but hundreds—each with distinct molecular drivers, treatment responses, and clinical trajectories. The promise of precision oncology is simple in concept but staggering in execution: match the right treatment to the right patient at the right time, guided by the molecular profile of their tumor. In practice, this requires orchestrating a complex workflow: tumor sequencing to identify mutations, interpretation of those variants against clinical databases, integration of genomic data with transcriptomic and proteomic profiles, therapy matching against drug databases, clinical trial matching, and longitudinal monitoring for resistance and recurrence. Each step generates data, requires expert interpretation, and carries uncertainty. ...

Agents for Drug Discovery: From Target to Molecule The pharmaceutical industry faces a productivity crisis. Developing a new drug costs an average of $2.3 billion and takes 10-15 years, with over 90% of candidates failing in clinical trials. Traditional drug discovery is a sequential, labor-intensive process: identify a target, validate it, screen millions of compounds, optimize leads, test safety, run clinical trials. Each stage can take years. Agentic AI — autonomous systems that reason, plan, and execute multi-step workflows — promises to compress this timeline dramatically. By orchestrating domain-specific models (AlphaFold for structure, ESM for protein embeddings, generative models for molecule design) with LLM reasoning, agents can automate the entire pipeline from target identification to clinical candidate selection. ...

Building Biological Tool-Use Agents: Architecture and Patterns The vision of agentic omics — autonomous AI systems that orchestrate biological discovery — depends on a deceptively simple capability: tool use. An agent that can reason about biology but cannot access BLAST, AlphaFold, or single-cell analysis pipelines is like a biologist who understands theory but has never touched a pipette. This post provides a practical architecture for building biological tool-use agents. We cover the essential tool inventory, the unique error-handling challenges of biological data, prompt engineering patterns for biological reasoning, and a reference architecture based on the ReAct (Reason + Act) loop. This is the “how-to” companion to Post 13’s conceptual overview and Post 14’s vision of agentic omics. ...

Introduction: The Convergence Point In Post 13, we defined agentic AI as systems that autonomously plan, reason, use tools, and execute multi-step scientific workflows. Now we arrive at the central thesis of this entire series: Agentic Omics — the convergence of large language model (LLM) reasoning with domain-specific biological AI models like AlphaFold, ESM, scGPT, and DNABERT to create autonomous systems capable of end-to-end biological discovery. This is not science fiction. As of early 2026, agentic systems are being deployed in operational drug discovery settings at companies like AstraZeneca, with documented implementations compressing workflows that once took months into hours while maintaining scientific traceability (Seal et al., 2025). The question is no longer if this convergence will transform biology, but how — and what architecture will get us there most reliably. ...