BioAgents: Accelerating Decentralized Science with AI Agents
We saw how AlphaFold revolutionized protein prediction with millions of volunteer compute hours. Imagine that same global collaboration incentivized via cryptoeconomics. At Bio Protocol, we’re driving this change with a decentralized fleet of BioAgents to accelerate scientific breakthroughs.
We expect the impact of AGI to be uneven. Although some industries will change very little, scientific progress will likely be much faster than it is today; this impact of AGI may surpass everything else.
Bio Protocol's mission is to commercialize the best science, faster.
We see the need to put significant resources into agentic science, with the two key overarching goals:
Create a decentralized fleet of BioAgents capable of using novel biotech tools, and deeply integrate BIO tokenomics to accomplish scientific tasks in longevity research, synthetic biology, and any BioDAO discipline.
Equip every BioDAO with a Coordination Agent, allowing scientists to focus on science. Such agents will aid in different facets of community governance, deal flow, marketing, patient/researcher onboarding, multi-sig management, and more, all to reduce common scaling bottlenecks and let countless BioDAOs flourish.
When Google DeepMind released AlphaFold, it changed how we approach protein structure prediction. Incredibly, over 2 million people donatedtheir compute and electricity for years, completely altruistically. The program resulted in over 200,000 protein structures being solved and made publicly available, and DeepMind's team winning the 2024 Nobel Prize for Chemistry.
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Looking forward, we have to ask the questions – how many people will contribute to research if they receive the financial benefits of their compute? And what if that compute, instead of having a mechanistic model, has the ever-expanding reasoning capabilities of LLM Agents?
BioAgents are a leap beyond static predictions, harnessing a user-run, decentralized, agentic system built on the Eliza framework and utilizing new software for running the agents either at home or in the cloud.
Each BioAgent runs continuously and becomes part of the BioAgent fleet. The individual agents take on given tasks from the BioDAOs, and as a whole they become capable of the following:
Reading and Analyzing Vast Scientific Literature
Modern biology produces over one million new publications annually, making it nearly impossible for any single human to keep pace. BioAgents can split up and digest these papers in parallel.
Specialized agents that each focus on a particular domain—genomics, proteomics, or specific disease areas—then reconvene to synthesize findings with near-perfect recall.
Seamless Interaction with a Decentralized Knowledge Graph (DKG)
Beyond just reading, BioAgents also write findings to a shared knowledge layer. Using a fork of OriginTrail’s DKG plugin, our Eliza agent automatically adds new nodes and edges based on insights drawn from literature. Over time, this builds a publicly accessible “BioGraph” of all relevant scientific information.
Executing Complex Reasoning and Decision Trees
Biology research often demands step-by-step logic: if an assay reveals a certain result, pivot to the next experimental protocol. AI fleets can collectively brainstorm hypotheses, refine them, and then plan multifaceted studies—much like a team of expert scientists collaborating in real time.
BioAgents can perform their own experimental design and submit actual experiments to be tested to a virtual wet lab appropriate to the experiment, such as Ora, Gingko, and many more.
High-Throughput Data Analysis (Multi-Omics & Imaging)
From patient genomic data to massive libraries of microscopy images, data in biotech is extensive and unwieldy. Each BioAgent can tackle a slice of the dataset—one agent for gene expression, another for imaging, yet another for proteomics—and share insights to form a cohesive interpretation.
Building Claims and Corroborating Data for IP-NFTs
The crowning achievement: generating robust, evidence-based claims that can be packaged into IP-NFTs. Once a BioAgent in the fleet uncovers a novel discovery—like a promising drug target or an innovative synthetic biology design—it can format a claim that can result in an IP submission, and store that IP onchain through Molecule, ensuring transparent, tamper-proof provenance.
To complete the comparison to AlphaFold, with BioAgents, we move from donation-based, protein predictions to an incentivized, decentralized, collaborative intelligence that accelerates every step in biotech R&D.
We are selecting specific, repeatable steps of the scientific process and perfecting an Agent to complete that step extremely well. One way to think about this is we are automating the process to move data from a less valuable format to a more valuable one.
research paper → knowledge graph → hypothesis generation → experiment design → experimental validation → IP claim → IP productization/sale
Each BioDAO is going through this process, and has unique bottlenecks according to their scientific field and research goals. This is where BioAgents will help the most.
Let’s take a moment to talk about what BioAgents are not:
They do not come with a new token
They are not chatbots
They are not social media influencers
BioAgents are: agentic science machines, creating non-stop knowledge work to serve the BioDAOs
BioAgents run on $BIO
The BIO token is the key way we incentivize and manage the growth of the BioAgent fleet. Critically, we see blockchain and the network incentivization through BIO as what AlphaFold is missing to scale beyond 2 million nodes at home.
Instead of relying on donations of compute, we properly reward people for their agentic contributions. Additionally, we also discuss below a new buy & burn mechanism for BIO, for any external usage of the BioAgent fleet.
Parameters and Caps
Since each agent requires real network resources, we’ll set a cap on how many agents can be utilized at once—adjustable as our capacity and research demands evolves. If all BioDAO research needs are satisfied at a given moment, we anticipate finding useful tasks for the BioAgents from biotech startups and pharma firms.
The fees generated from external partners utilizing the BioAgent fleet will be burnt. This adds a new deflationary element to the BIO token, and is proportionate to the size of the BioAgent fleet and the value of the tasks it can be assigned.
Modified from FutureHouse Schematic
Coordination Agents for BioDAO Operations
Thus far we've now talked about decentralized BioAgents contributing to true citizen science.
Now let’s switch to our vision for the agents assisting the humans of a BioDAO – scientists, DAO admins, community members, investors.
This kind of Agent has the goal of coordinating the daily life of a BioDAO, and so we are calling these CoreAgents. They help in the many tasks of marketing, governance, crypto wallet management, member onboarding, and therapeutic product updates.
In the current BioDAOs, all of these areas are run by the BioDAOs with some help from the Bio Protocol. Yet the mission of a BioDAO is related to science, and not the above operations tasks, which can introduce significant overhead. We need to provide BioDAO contributors with the best tools for the job, and Agents, whether they are actively interacted with akin to Iron Man’s Jarvis, or passively watching the Discord chat and Github submissions and updating the community.
CoreAgents are a significant part of the solution in getting web2 scientists to succeed in graduating through the Bio Launchpad and thriving as a BioDAO by lowering the technical barrier for BioDAO creators and automating time-intensive operations.
Here is an outline of how the CoreAgent can help each BioDAO, according to 3 key phases of Bio Protocol's permissionless Launchpad: Preparation, Launchpad Fundraise, and Active BioDAO Research.
BioDAO Preparation
In this initial phase, the focus is on solidifying the potential BioDAO’s vision, legal foundation, and community basics, as outlined in the incubator’s first milestone. The CoreAgent can streamline these early tasks:
Mission Definition & Knowledge Setup: The DAO’s founding team uses AI to refine a clear mission and scientific scope, which is the appropriate starting point for any BioDAO. The AI curates relevant research, prior art, and market data into a knowledge base, ensuring the concept is grounded in data.
Onboarding & Team Assembly: An AI-driven onboarding system accelerates bringing core contributors and mentors into the project. It automates orientation materials about web3 and biotech, tailored to each member’s role (e.g. scientific, technical, legal). This mirrors the incubator’s mentorship support in governance, tokenomics, legal, and community building.
Legal Incorporation Automation: To establish a compliant foundation, the AI assists in setting up the BioDAO as a Swiss Verein (non-profit association) – a legal wrapper commonly used for DAOs. It auto-generates the association’s charter and bylaws in line with best practices.
Governance Framework Initialization: The CoreAgent configures the DAO’s preliminary governance system, drawing on BIO’s governance playbooks. It helps draft voting processes and decision-making frameworks.
Branding & Community Platform: Aligning with the program’s marketing module, AI tools generate branding assets (name, logo, website content) and spin up community platforms (BIO Program Modules). The AI can create a basic website and social channels, and deploy a Discord/Discourse server pre-configured with relevant channels.
Fundraising Through the Launchpad
The second phase is the BioDAO’s token launch and public fundraising. The CoreAgent is critical here to coordinate the complex steps of token generation, compliance, and community rollout, while aligning with BIO Launchpad processes for curation, funding, and liquidity support.
Curation Readiness & Tokenomics Design: Before a BioDAO can launch its token on the BIO Launchpad, it must be curated into the BIO network. AI analytics help prepare the DAO for this selection process by compiling a compelling data room of the DAO’s achievements, community strength, and research pipeline. BIO token holders in the wider network then review and lock BIO tokens with promising BioDAOs.
Automated Launchpad Auction Execution: When it’s time to launch, the AI interfaces with the BIO Launchpad to run a fair token auction. The CoreAgent monitors the live auction, updating the team on bid status, and even adjusting marketing in response to participation lulls (e.g. triggering an automated community AMA if mid-auction activity dips).
Immediate Governance & Community Onboarding: Following the token sale, governance transitions to include the new token holders. AI tools immediately onboard these participants into the DAO’s governance platforms, walking them through how to vote, make proposals, and claim any rewards (such as airdrops or participation NFTs).
Treasury and Liquidity Automation: Once funds are raised, the AI immediately helps secure and allocate the new treasury. It could automate conversion of a portion of raised funds into stablecoins or other assets per the treasury management plan formulated earlier.
Research and Growth
In this last phase, the BioDAO is fully operational – conducting research, managing proposals, and growing its community. The CoreAgent now focuses on automating the DAO’s ongoing operations and ensuring scientific rigor, governance participation, and community member growth.
Decentralized Proposal Review: As researchers submit project proposals, AI tools triage and summarize them for the community. It flags any that don’t meet the DAO’s standards or duplicates prior research, ensuring only high-quality proposals reach a vote. This speeds up diligence by providing the community and governance board with concise analyses and risk assessments for each proposal.
Data Validation & IP-NFT Preparation: When funded projects start producing data, the AI validates the experimental results. It cross-checks reported findings against protocols and known literature to catch anomalies or data integrity issues early, potentially using the yesnoerror agent directly.
Community Engagement & Growth: With a growing global community, AI chatbots and content tools keep members informed and involved. They answer routine questions, onboard new scientists or enthusiasts, and translate complex updates into layperson summaries.
Ongoing Knowledge Management: All proposals, research outputs, and governance decisions are continuously indexed by the AI in the DAO’s knowledge repository. This living archive, aligned with BIO’s open-source knowledge ethos, makes it easy to retrieve prior discussions or data when new proposals arise.
Performance Monitoring & Operations: AI analytics track key performance indicators of the BioDAO’s operations in real time. This includes scientific KPIs including that could qualify the BioDAO for bio/acc rewards from the Bio protocol (e.g. number of projects funded, milestones achieved), community metrics (membership growth, proposal participation rates), and financial health (treasury balance and burn rate).
In all of these ways, a CoreAgent assigned to each fledgling BioDAO is our solution to the small but inherent difficulties in onboarding web2 scientists into the onchain world. It goes beyond fixing problems, and the agentic automation will help scientists focus on doing science.
The open and permissionless nature of decentralized science (DeSci) creates vast potential for AI technologies to automate the process of drug discovery and commercialization. This article has laid out our initial high-level vision for BioAgents to reduce bottlenecks at individual BioDAOs and cross-pollinate scientific and operational automations across Bio's open network of BioDAOs.
In the coming weeks, we will open-source the code repositories for BioAgents, and we invite developers to join the BioAgents Builders Telegram for further updates.
We'll be launching pilots for BioAgents and dog-fooding them within specific BioDAOs before rolling them out on a wider basis, and we will have a global hackathon (with an in-person Berlin event) on April 8-10th!
