The Agentic Lab: Build AI Agents for Biotech and Pharma R&D

An AI agent's confidence is not evidence. The trace is.Every week another tool promises to run your literature search, clean your instrument exports, draft your protocols and dock your compounds. Almost none of them tells you how to check the result. This book does.The Agentic Lab teaches bench scientists to build AI agents for research work and, more importantly, to audit them. You will write a working agent loop from scratch in plain Python, then build twelve agents across the tasks that consume a research group's week: literature triage, data wrangling for omics and imaging exports, protocol drafting and adaptation, molecular docking, ELN and LIMS integration, and run provenance. Every chapter closes with a Validation Gate, a set of tests the build must pass before you trust its output.Built around a real case study. The book follows one research question from start to finish: does ivermectin act on protein kinase C isoforms in hepatocellular carcinoma? The author spent two months answering it by hand at IIT Guwahati, from Cytoscape networks and AutoDock runs to MTT assays on HepG2 cells. Chapter by chapter, the agents rebuild that work, and Chapter 12 reports honestly what they cost, how long they took, and where human judgement still had to intervene.What you will learn- What an agent actually is: instruction, tools, memory, loop and stopping condition, in language you can audit- How to read an execution trace and reconstruct what an agent did, as opposed to what it says it did- Why hallucinated citations, silent unit errors, drift and loop pathologies happen, and the specific tests that catch them- Run manifests and audit replay, so a result can be re-run six months later- Where agents sit under GxP and FDA credibility guidance, and what you may not automateWorking code, not pseudocode. All twelve builds live in a public GitHub repository with 335 passing tests, a Colab path for readers without a local machine, and QR codes in every chapter linking to the current version. Python 3.11 or later is the only prerequisite; you need to read a loop and a function, not write a framework.Who it is for. Master's students, doctoral researchers and early-career scientists in the life sciences who can run a Python script and want to automate clerical research work without surrendering the judgement that makes it science. Computational biologists will find the failure taxonomy and validation discipline useful even if the engineering is familiar.