The Business of AI: How Agentic Tools Create Economic Value
What You Will Learn
Three things. Concrete examples of agents already generating commercial value in healthcare, legal, and customer operations. The distinction between the infrastructure layer and the application layer where products are built. And why the barrier to building something real is not technical or financial — it is the scarcity of domain knowledge combined with a specific problem worth solving.
Watch: How to build a product and what to build — recommended by bioERGOtech (starts at 24:16)
Three Industries Already Using Agents Commercially
Healthcare
→Triage agents read patient messages and route them to the right clinical team — processing in seconds what previously took hours of manual review.
→Claim processing agents read, categorise, and approve low-complexity insurance claims in minutes instead of days.
→Research agents scan thousands of published papers and surface relevant findings for a specific clinical experiment.
Legal
→Contract review agents read thousands of pages, flag clauses that deviate from standard terms, and produce risk summaries — a task that used to take a junior lawyer a week now takes an agent a few hours.
→Due diligence agents cross-reference filings, court records, and public data to surface risks before a deal closes.
Customer Operations
→First-line agents handle routine enquiries autonomously and escalate complex cases — with full context already prepared for the human agent who picks it up.
→Resolution time for standard requests drops from days to minutes.
Infrastructure vs Application Layer
The Infrastructure Layer
The infrastructure layer consists of the large language models themselves — Claude, GPT, Gemini, Llama — and the compute required to run them. Building at this layer requires hundreds of millions of dollars, vast datasets, and teams of ML researchers. This is not where student builders operate.
The Application Layer
The application layer is where products are built on top of the infrastructure. You do not need to understand how a transformer works to build a useful agent — just as you do not need to know how a database engine works to build a web app that uses one. The application layer is where domain knowledge becomes the primary differentiator. An agent that helps school nurses triage flu symptoms requires deep understanding of school health workflows — not a PhD in machine learning.
Key Insight
The real scarcity is not technical skill — it is domain knowledge combined with a specific problem worth solving. If you understand a domain well enough to recognise a problem that repeats, you have the most important ingredient.
✏️ Student Reflection
Name one domain you know well — from school, family, volunteering, or personal experience. Describe one repetitive task in that domain that currently takes human time and attention. How would an agent handle it? What would be the measurable benefit? Write 3–5 sentences. This is the seed of your project brief.
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