By Phylo Team

Biologists enter the field to make sense of life and cure disease, to understand how cells divide or die, to map the brain, and to learn how tumors evolve resistance to drugs and immune attack.

But that is not how most biologists spend their days.

Picture a typical morning: laptop open, notes out. You pull up yesterday’s qPCR results, set up a cell culture plate, and disappear into the tissue culture room. Between incubations, you sprint back to your desk to analyze data in an old Excel template, make figures in Prism, and answer a colleague’s question about RNA-seq. By noon you have bounced between six tools, made multiple trips between bench and screen, and burned through your cognitive budget.

Other fields have faced the same problem. Early software development was slow and fragmented: code was written in one place, executed in another, and debugged only after long delays. The breakthrough came with the Integrated Development Environment (IDE). By unifying writing, execution, and debugging in a single workspace, IDEs collapsed feedback loops and changed how engineers worked.

Biology has not had an equivalent, largely because the work is historically fragmented. A single project spans databases, analysis tools, file formats, software packages, and wet-lab experiments. Thousands of tools were built in isolation and were never designed to interoperate.

Why this is changing now

That constraint is starting to dissolve. Large language models are no longer limited to narrow tasks or brittle pipelines. They can synthesize information, reason through problems, and learn new concepts quickly.

When you build agents on top of these models, AI gains the ability to act, not just think. Agents can query databases, run code, orchestrate workflows, and even control instruments. A scientist describes an intent, and the agent figures out how to carry it out.

For biology, this is a step change. The agent becomes the integration layer biology has been missing for decades: the connective tissue that can move across tools, databases, and formats, and handle the tedious work in between. That opens the door to a new workflow: biologists stay focused on science, asking questions and interpreting results, while the agent handles the friction.

We built IBE to make this real.

What is an IBE?

An Integrated Biology Environment is a shared workspace where biologists and agents collaborate. The biologist brings questions, intuition, and judgment. The agent brings the ability to search, analyze, execute, and keep track of everything.

In one place, you can design an experiment, review the literature, analyze data, interpret results, check a protein structure, order reagents, launch an experiment, call an AI model, and plan next steps. The agent remembers what you have tried, understands your goals, and helps you reason toward what to do next.

Three parts make this work:

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  • The interface: the workspace where biologists and agents collaborate, designed around how biologists think and work.

  
  

  • The agent: the intelligence that understands scientific context, reasons with you, and executes on your behalf.

  
  

  • The integrations: connections to the tools, databases, and software biologists rely on, so the agent can act across all of them.

This is how biology should feel: the biologist asks, the agent executes, and the environment holds everything together.

Principles for building an IBE

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  • Native to biologists. Built around biological concepts and the natural rhythm of research, from literature review to experiment design to analysis to writing. The interface has to be rethought from scratch to fit how scientists actually spend their days.

  
  

  • Rigorous. Scientific rigor mattered the most. Results are reviewable and reproducible, grounded in evidence. The agent challenges its own outputs and flags shaky assumptions from you.

  
  

  • Collaborative. A true partner: it brainstorms, clarifies ambiguity, proposes a plan before acting, follow-up with ideas, and lets you intervene at any step.

  
  

  • Personalized. Every biologist works differently. The environment adapts to your preferences, tools, and style, and becomes an extension of how you think.

  
  

  • Scalable. Complexity is abstracted. Models, compute, and robotics appear as simple actions, without requiring you to manage infrastructure.

The AI-native biologist

IBE changes how biologists work.

You wake up thinking about hypotheses, not which tools to open. You describe what you want to investigate, and the agent handles the mechanics. You review, steer, and decide. What remains is the core of science: reasoning, intuition, and creative leaps. IBE shifts the focus back to science.

From weeks to hours

An AI-native biologist operates on shorter cycles. A hypothesis raised in the morning can be tested by lunch and dropped by afternoon if it fails. Work that used to take weeks, like reviewing hundreds of papers, running complex omics analyses, or iterating on protein designs, can compress into hours. The limiting factor shifts from logistics to thinking.

Expertise without boundaries

No individual or team is an expert in every domain their work touches. Structural biologists are not necessarily medicinal chemists. Geneticists are not always experts in protein function prediction. Cell biologists may not track the latest advances in machine learning.

An AI-native workflow bridges these gaps. Domain expertise becomes part of the environment, not a bottleneck that requires months of collaboration, funding, or coordination. You can follow your curiosity farther, without stopping at the edge of what you personally know.

Unlocking hidden discoveries

Many discoveries sit at intersections: between disciplines, inside under-explored datasets, and in the connections between new tools and old questions. CRISPR came from microbiologists studying bacterial immune systems. Metformin’s surprising benefits surfaced from decades of patient data. The problem is cost. Cross-disciplinary exploration takes time, collaborators, and constant learning, so most researchers never go looking.

An IBE lowers the activation energy. It makes it practical to connect decade-old data to models published last week, and to trace ideas across fields until something clicks. The discoveries were always there. Now they are easier to reach.

Making tacit knowledge explicit

Science has a provenance problem. Papers show conclusions, not the dozens of failed attempts that led there. Labs rely on tacit knowledge: which antibody actually works, why a “30 minute” step really needs 45, which protocol breaks under specific conditions. That knowledge disappears when people leave or memories fade.

In an IBE, that trail is captured by default. Queries, analyses, decisions, and rationale are preserved as you work. A new student can see how the thinking evolved. A collaborator can understand why you made a call. Tacit knowledge becomes explicit.

Closing the experiment loop

Biology is still bottlenecked by the physical world: preparing reagents, running protocols, collecting results. Even if analysis is instant, experiment cycles can take weeks.

We envision an AI-native biologist closing that loop: design an experiment, send it directly to a robotic lab, and receive results back in the same environment. Cycles shrink from weeks to hours.

When an organization becomes AI-native

Now imagine every researcher working inside the same IBE.

A chief scientist gains a living map of what the organization knows. When a clinical trial fails, you can trace backward through animal studies, cell assays, and target selection, and identify where assumptions broke. Documentation happens as a byproduct of work. The boundary between wet lab and dry lab fades because both operate in the same shared context.

Over time, the IBE becomes the organization’s scientific memory and execution layer.

Abundance of discoveries

There is a future where working with an agent feels as normal as pipetting. Today, agents still feel new and their ROI can be unclear. But so did sequencing once, and so did gene synthesis. Costs became standard line items because the capabilities were worth it. The same shift will happen here: running analyses, calling models, and orchestrating workflows becomes as routine as paying for reagents or instrument time.

When that happens, the bottleneck is no longer bandwidth. It is imagination: the questions you think to ask.

That is the future we are building toward. Not AI that replaces biologists, but an environment that lets biologists do more of what drew them to biology in the first place: cure disease, understand mechanisms, and chase the questions that matter.

An abundance of discoveries.