Architecture

Building Biological Tool-Use Agents: Architecture and Patterns The vision of agentic omics — autonomous AI systems that orchestrate biological discovery — depends on a deceptively simple capability: tool use. An agent that can reason about biology but cannot access BLAST, AlphaFold, or single-cell analysis pipelines is like a biologist who understands theory but has never touched a pipette. This post provides a practical architecture for building biological tool-use agents. We cover the essential tool inventory, the unique error-handling challenges of biological data, prompt engineering patterns for biological reasoning, and a reference architecture based on the ReAct (Reason + Act) loop. This is the “how-to” companion to Post 13’s conceptual overview and Post 14’s vision of agentic omics. ...

It is relatively easy to build an SRE agent that can solve a single, well-defined problem in a demo environment. You give it a prompt, access to a few tools, and watch it restart a pod or query a log file. It feels like magic. But taking that agent and asking it to run 24/7, monitor thousands of services, handle concurrent incidents, and never hallucinate a destructive command is a different engineering challenge entirely. It moves us from the realm of “AI scripting” to distributed systems architecture. ...

When we talk about “Agentic SRE,” we often focus on the what—what the agent can do, what models it uses, or what access it has. But in 2026, the critical architectural decision is actually the where. Does your SRE agent live inside your cluster, running as a Kubernetes operator with direct access to the control plane? Or does it live in a SaaS vendor’s cloud, ingesting telemetry and sending commands back over an API? ...