The Rise of AI Agents in Biotech, Where Are We Now?

In this post, we take a grounded look at these emerging systems. No hype, just the current landscape: what’s working, what isn’t, and what technical opportunities and constraints are shaping the field today.

🤖 What Are AI Agents Doing in Biotech—Technically?

Modern agents in biotech combine three technical layers:

I. LLM Core: Typically GPT-4, Claude 3.5, or DeepSeek for task decomposition and natural language reasoning. Some use fine-tuned BioLLMs like BioGPT for domain context.

II. Tool Layer: Dynamically called APIs and function endpoints. These can include:

• Structured biomedical APIs (OpenFDA, ChEMBL, PubChem);
• Local database queries (e.g., from curated gene sets or lab-specific datasets);
• Code execution for data analysis using internal Python kernels.

III. Orchestration Framework: Chain-of-thought agents often use custom ReAct-style loops, where the LLM issues tool calls and updates an internal scratchpad. ToolRAG (used in TxAgent) performs retrieval-aware tool selection, for instance.

Agents like TxAgent and BioDiscoveryAgent are not monolithic—they rely on this orchestration model to dynamically pull in tools and retrievable biomedical context at each reasoning step.

🧭 Spotlight: SpatialAgent and the Frontier of Spatial Genomics

One of the most technically complete and domain-specific agent frameworks to emerge recently is SpatialAgent by a team of Genentech, developed to tackle spatial biology problems using a self-governing LLM architecture.

Spatial genomics—studying how genes are expressed across tissues in space—is computationally intensive and biologically complex. Existing methods are fragmented, require significant manual input, and often underperform on multimodal datasets.

SpatialAgent changes that by integrating LLM reasoning with domain-specific tools and structured task decomposition. It operates with three tightly integrated modules—memory, planning, and action:

• The memory module stores task goals and contextual knowledge (e.g., tool capabilities, experiment objectives);
• The planner uses chain-of-thought reasoning and optional predefined templates to deconstruct tasks into sequenced subtasks;
• The action module executes those subtasks via curated tools—e.g., querying gene databases, calling external APIs, or running in-context code.

For gene panel design tasks (e.g., designing a 100-gene panel for spatial transcriptomics in the human cortex), SpatialAgent uses a mix of reference databases (like PanglaoDB, CellMarker2) and learned scoring functions to rank candidate genes. It demonstrated better performance than multiple baselines (including Spapros and human experts), achieving up to 47.1% R² gains in spatial accuracy and higher clustering scores.

In multimodal annotation of heart tissue datasets, it outperformed CellTypist and GPTCellType, particularly in capturing spatially relevant cell niches and preserving anatomical structures. It also supports co-pilot mode, where users can inject custom input (e.g., their own gene panel) and ask the agent to refine or expand it, blending automation with human guidance.

Where SpatialAgent really shines is data-to-insight loops. Given MERFISH data from a colitis mouse model, it ran an entire analysis pipeline—including differential composition, cell-cell interaction scoring via LIANA, and factor analysis—returning a structured 7,000-word report. Impressively, it recapitulated major findings of the original study and surfaced new hypotheses (e.g., the role of IL-11 and TGF-β signaling in fibroblast-pericyte interactions).

The system isn’t without faults. It sometimes misses tissue-specific annotations, falls back on high-frequency terms from its pretrained corpus (e.g., mislabeling epicardial cells), and can produce hallucinated connections when lacking high-quality tool outputs. But the modular design—supporting template-guided planning, tool chaining, and memory updates—makes it one of the most technically ambitious biomedical agents to date.

It sets a benchmark for how agentic systems can be evaluated: not only in classification accuracy or runtime, but in how well they across novel biological domains.