Workflow Automation

Scan, Plan, Report: When Agentic AI Starts Thinking Like a Radiologist Report writing is the visible part of radiology. It is also the part easiest for AI vendors to misunderstand. A radiology report looks like text, so the naive automation pitch is obvious: give the CT scan to a vision-language model, ask for a report, and let the model type faster than a human. Congratulations, we have reinvented autocomplete with more liability. ...

A small nonprofit moved from human-coordination-heavy grant administration to an AI-agent-enabled workflow that scans opportunities, drafts proposals, structures evidence, and prepares donor reports under human approval gates.

Workflow is where agent demos go to die. A user asks for something that sounds simple: “Assess flood damage in this coastal district after the typhoon.” The agent smiles, metaphorically, and begins its little ritual. It searches, summarizes, calls a tool, thinks again, calls another tool, corrects itself, forgets one preprocessing step, invents a plausible shortcut, then produces a confident final answer that looks fine until someone who actually understands geospatial analysis asks an inconvenient question: where did the corrected satellite imagery come from? ...

Tools are where many agent demos quietly become invoices. A multi-step LLM agent may look intelligent because it reasons, acts, observes, and repeats. Under the hood, though, it often pays the model to decide every small next move: search here, load that node, look around, check valid actions, fill this argument, try again. Some of those decisions need judgement. Others are basically muscle memory wearing a lab coat. ...

Office work is not one task. It is a chain of small obligations pretending to be one task. “Check the homework submissions, download the attached Python files, run them, grade the students in Canvas, and use the latest submission if someone sent more than one.” That sounds like a normal administrative request. It is also a compact torture device for an AI agent. The agent must read email, handle attachments, inspect local files, run code, interpret results, map students to course records, update Canvas, and not confidently grade the wrong person. Easy, apparently, as long as nothing has to actually work. ...

A high-value commercial farm redesigned daily crop, irrigation, pest, harvest, labor, and buyer-delivery coordination around a reviewed AI operations brief instead of fragmented messages and manager memory.

A small insurance broker redesigned a fragmented claims-preparation workflow into a human-reviewed agentic process that turns scattered documents into completeness-checked, risk-screened, underwriter-ready files.

The demo is easy. The DAG is not. Pipeline automation has a wonderfully deceptive user story. A business analyst writes: “Take this customer file, clean the locations, geocode the addresses, add weather data, then save the enriched output.” An LLM replies with a Python file. The file looks plausible. There are imports. There is an Airflow DAG. There are operators. There are dependencies. A demo audience nods approvingly. ...

A mid-sized private K-12 school redesigned fragmented admissions, parent communication, attendance, fee, and teacher-report workflows into an AI-agent-enabled operating layer with human checkpoints for sensitive decisions.

Tickets are not equal. Some user requests are glorified form-filling. Some are ambiguous investigations with missing context, tool calls, intermediate checks, and enough failure modes to keep a compliance officer quietly blinking at the ceiling. Yet many agentic systems still behave as if every query deserves the same ritual: summon the agents, run the workflow, pass outputs around, maybe add a debate round for theatrical effect, and hope the bill does not look too much like modern art. ...