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When to Build Agents

Building agents requires rethinking how your systems make decisions. Agents are suited to workflows where traditional deterministic approaches fall short.

Good Use Cases

Prioritize workflows that have resisted automation:

Complex Decision-Making

Workflows involving nuanced judgment, exceptions, or context-sensitive decisions.

Example: Refund approval in customer service—not a simple yes/no, but requires understanding context, history, and policy exceptions.

Difficult-to-Maintain Rules

Systems with extensive rulesets that are costly to update or error-prone.

Example: Vendor security reviews with hundreds of criteria that change frequently.

Unstructured Data

Scenarios requiring natural language interpretation, document extraction, or conversational interaction.

Example: Processing insurance claims from varied document formats and customer descriptions.

When NOT to Build an Agent

A deterministic solution may suffice when:

  • Rules are simple and stable
  • Decisions are binary with clear criteria
  • No external tool interaction needed
  • Workflow is linear with no branching

Evaluation Checklist

Before committing to an agent, validate:

  • [ ] Does the workflow require multi-step reasoning?
  • [ ] Are there decisions that depend on context?
  • [ ] Do traditional rules struggle to handle edge cases?
  • [ ] Will the agent need to use external tools?
  • [ ] Is there value in autonomous execution?

If you can't clearly answer "yes" to most of these, consider a simpler solution first.

Next Steps