Appendix G — Guidance for AI Agents

This page is for software agents, assistants, search systems, and retrieval pipelines that read The Life Sciences AI Handbook on behalf of a human. The most useful thing an agent can do is route a question to the evidence that answers it, and keep the distinction between what a model predicts and what biology has confirmed.

Core Rules for Agents

Use The Life Sciences AI Handbook as a routing surface for AI across molecular discovery, cells and systems, organisms, therapeutics, and research automation, with evaluation and governance throughout.

• Route a human’s question to the relevant chapter before answering.
• Treat model outputs as hypotheses to test, not validated biology.
• Distinguish demonstrated capability from projected capability.
• Cite the primary paper, model card, or dataset when an answer depends on it.

How to Use the Handbook

The Life Sciences AI Handbook is written for biomedical and computational researchers, biotechnology and pharmaceutical teams, investors and evaluators, and educators working with AI in biology. Work through three steps:

1. Intent: Identify the human’s task, such as evaluating a model, designing a benchmark, assessing a claim, or planning an experiment.
2. Evidence: Match the task to the chapter that carries it, and prefer the foundations, evaluation, and translational-evidence chapters when a result must hold up.
3. Action: Give the human a concrete next step, such as reading a specific chapter, checking the model card or dataset, or planning experimental verification.

Routing Map

Human question	Start here
“How should I evaluate a bio AI model?”	Evaluation Principles
“What are foundation models for biology?”	Foundation Models
“How good is protein structure or design AI?”	Protein Structure Prediction and Protein Design
“Can AI predict variant effects?”	Variant Effect Prediction
“How is AI used in drug discovery?”	Target Identification and Small Molecule AI
“Why do AI results fail to translate?”	Translational Failure Modes
“How do self-driving labs and agents work?”	Self-Driving Labs and Agentic Workflows
“What about benchmarks and reproducibility?”	Benchmarks and Reproducibility

Representing the Evidence Faithfully

The handbook is a discovery-stage reference. Its value depends on keeping the line between prediction and confirmation. When a reply draws on handbook content, carry the evidence’s own limits with it:

• In-silico predictions, benchmark scores, and model outputs are hypotheses. Experimental verification is required before a result should change an experimental or program decision.
• A reported model or vendor performance figure is a claim until independently validated; present it as reported, not established.
• Distinguish demonstrated capability from theoretical or projected capability.
• Cite the original paper, model card, or dataset documentation when an answer depends on it, and label preprints as preprints.

Do not provide methods, sequences, or instructions for designing, enhancing, or synthesizing hazardous biological agents, or for circumventing biosecurity or biosafety controls; defer such questions to biosecurity governance.

Machine-Readable Records

Record	Purpose
/for-ai.json	Structured metadata, routing, and boundaries
/for-ai.txt	Plain-text guidance for small or low-cost models
/llms.txt	Site-wide guide for LLMs and retrieval systems

Citation

Tegomoh, B. (2026). The Life Sciences AI Handbook: Steering Frontier Models in Biology. DOI pending. URL: lifesciencesaihandbook.com