"AI agents" is one of the most overused terms in tech right now. Before building an agent, understand what you're choosing between and why.
Traditional automation
Scripts, cron jobs, Zapier workflows, and rule engines follow explicit logic:
IF invoice.amount > 10000 THEN route_to_manager
IF ticket.category == "billing" THEN assign_to_billing_team
Strengths: Predictable, fast, cheap, easy to debug, auditable.
Weaknesses: Brittle when inputs vary, requires explicit rules for every case, can't handle unstructured data.
AI agents
Agents use LLMs to decide what actions to take, often with access to tools (APIs, databases, search):
User: "Research Acme Corp and prepare a briefing for tomorrow's call"
Agent: [searches web] → [checks CRM] → [reads past emails] → [generates briefing]
Strengths: Handle unstructured tasks, adapt to variation, combine multiple data sources, natural language interface.
Weaknesses: Unpredictable, slower, more expensive, harder to debug, can fail in unexpected ways.
When to use traditional automation
- The workflow has clear, stable rules
- Inputs are structured and predictable
- Speed and cost matter at scale
- Errors must be impossible, not just unlikely
- You need full auditability
Examples: Invoice routing, scheduled reports, data sync between systems, email auto-responders for known queries.
When to use AI agents
- The task requires understanding unstructured input (emails, documents, web pages)
- The steps vary based on context and can't be fully scripted
- The cost of an occasional error is low (with human review)
- The alternative is significant manual human effort
Examples: Sales research, document analysis, multi-step investigation tasks, code review assistance.
The hybrid approach (usually best)
Most production systems combine both:
- Rules handle the predictable parts — routing, validation, scheduling
- AI handles the variable parts — understanding intent, generating content, making judgment calls
- Humans review high-stakes outputs — before sending to customers or making financial decisions
Decision framework
| Question | Traditional | AI Agent | |----------|------------|----------| | Are the rules well-defined? | Yes | No | | Is input structured? | Yes | No | | Is speed critical? | Yes | No | | Is cost per execution important? | Yes | No | | Does the task require understanding context? | No | Yes | | Can occasional errors be caught by review? | N/A | Yes |
The most common mistake
Building an AI agent for a workflow that a 20-line script could handle. Start simple. Add AI only when the simple solution breaks down.