Custom AI Agents for Computational Biology
LLM-based AI agents are rapidly gaining traction in biology, highlighted as a “Method to Watch” in the December 2025 issue of Nature Methods. Yet most implementations remain too generic to deliver meaningful impact in real research environments.
In practice, the limiting factor is not the model or the agent orchestration framework. The real challenge is integration; connecting AI agents to the tools, data, and infrastructure that your team actually relies on.
I design and implement custom agentic systems and LLM-based tools that integrate directly into your computational biology workflows to accelerate your research.
The key bottleneck is integration
Off-the-shelf agents work well for broad, general-purpose tasks. But computational biology operates in highly heterogeneous environments: bespoke pipelines, custom scripts, legacy tools, evolving standards, and fragmented data landscape.
In this reality, generic agents can only deliver limited value.
Real impact comes from systems built around your data and your workflows. Success depends on whether the agent can integrate into your scientific processes in a reliable, secure, reproducible way.
Built for production science — not demos
A production-ready system may need to
- connect to internal tools, in-house databases, and bioinformatics pipelines
- work with custom data models, and heterogeneous file formats
- coordinate external services, cloud infrastructure, and execution workflows
- support human-in-the-loop review and approval
- preserve reproducibility, auditability, and governance
- fit securely into existing research environments
This goes beyond prompt engineering. It requires system design, workflow orchestration, infrastructure integration, and careful implementation around how scientific work is actually done.
- Agent Orchestration & Multi-Agent Systems
- Retrieval (RAG, embeddings, knowledge bases)
- Data Layer Integration (internal/external data sources)
- API Connectivity & MCP Servers
- Computational Workflow Integration (cloud/HPC pipeline execution)
- Human-in-the-Loop Checkpoints
- Private LLM Deployment (on-prem / VPC) for Data Security
- Observability, Tracing & Evals
Example use cases
LLM-based workflows and agents can accelerate work in computational biology in many ways. Here are only a few high-impact examples.
User poses complex research questions to the agent, such as: "Using publicly available gene expression and clinical outcome data and relevant publications not older than 5 years, can you identify novel biomarkers for drug resistance in triple-negative breast cancer, and propose a mechanism by which they mediate resistance?"
To answer this, the agent must orchestrate a sequence of coordinated tasks to check heterogeneous data sources such as GEO, TCGA, and cBioPortal for transcriptomic and clinical datasets, while simultaneously retrieving recent literature from PubMed. It then needs to reason across diverse types of information, and synthesize its findings into a coherent hypothesis.
User tells the AI agent to modify the access control settings of a protected dataset: “Grant our bioinformatician, John Doe, access to the RNA-seq dataset RNASeq_ASSAY_1, excluding samples 1–6.”
To fulfill this request, the agent needs to modify the underlying AWS IAM policy to give a user permission to access an S3 bucket folder, excluding the specified subfolders. Data access control is therefore as simple as issuing a natural language instruction, shielding users from the complexity of AWS policy syntax and reducing the risk of misconfiguration.
User instructs AI agent: “Find peer-reviewed publications about colorectal cancer that discuss any genes we found to be differentially expressed in our in-house assays on Grade 4 CRC samples. Have any of those been linked to metastasis?"
In order to answer this request, the agent needs to have access to the internal data repository containing the results of the in-house assays, and any associated metadata about the samples. It also needs to interface with external data sources such as PubMed to retrieve relevant papers. This setup enables the agent to seamlessly integrate internal experimental data with external scientific knowledge to generate more comprehensive and context-aware insights.
If the agent works with sensitive or proprietary data, transmitting information to third-party LLM API endpoints (e.g., those provided by OpenAI or Anthropic) may raise data confidentiality and regulatory compliance concerns.
The solution is a private, secure deployment of open-source or open-weight LLMs, either locally or in the cloud, with inference running on on-premises or VPC-isolated infrastructure. While LLM inference typically requires powerful GPUs to achieve low latency, open models are becoming increasingly efficient and accessible. Smaller parameter footprints, quantization techniques, and optimized runtimes are significantly reducing hardware requirements.
My approach
I work closely with clients to understand the full context behind the problem — not just the task, but the workflows, constraints, data, and infrastructure around it. From there, I design and implement the system around that use case: defining integrations, structuring workflow logic, and building a deployment that fits cleanly into existing environments. The focus is on delivering systems that work under real conditions. Not prototypes, but reliable tools for day-to-day scientific work.
Learn more about working together from the FAQ.