AI Agents in Real Business Applications

Everyone is talking about AI agents. Few are shipping them in production. Here's what actually works.

What "Agent" Actually Means

An agent is just: LLM + tools + loop. The model decides which tools to call, calls them, processes results, decides again. That's it.

For business apps, the useful tools are:

• Database queries (read customer data, orders, tickets)
• HTTP calls to internal APIs
• Email/Telegram/Slack send
• File read/write

Real Use Case 1: Lead Scoring in CRM

Every new lead in Seven Suite gets scored by Claude:

• Reads company size, industry, last activity
• Compares to historical won/lost deals
• Returns score 0-100 + reasoning

No training data needed. Claude understands context out of the box.

Real Use Case 2: Support Ticket Routing

Incoming ticket → Claude reads title + body → assigns category + priority + suggests response template.

Time to first response drops from hours to seconds.

Real Use Case 3: Report Generation

Instead of building complex report builders, I let users describe what they want in plain text: "Show me revenue by country for Q1, exclude refunds." Claude generates the SQL query, runs it, formats the result.

What Doesn't Work (Yet)

• Autonomous agents making business decisions without human review — too risky
• Real-time data analysis on large datasets — cost and latency
• Replacing domain experts — they know edge cases the LLM doesn't

Keep humans in the loop for anything consequential.