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What should I use AI Agents for?
A practical framework for choosing your first use case and scaling from there.
Key takeaways
Start with high-volume, repeatable, low-risk tasks
Use traces and evals to build confidence before scaling
Multi-agent pipelines handle complex workflows by combining simple agents
Some tasks are better as human-AI collaboration, not full automation
The ideal first use case
Look for tasks that are high-volume (hundreds or thousands per week), follow a repeatable process (someone could write a checklist), and where a wrong answer has low consequences (a bad FAQ response, not a financial decision). Customer FAQ, ticket triage, data extraction, and content summarization hit all three criteria.
Growing into structured workflows
Once your first agent is running and you have traces proving it works, expand into structured workflows: document classification, approval routing, invoice processing, compliance checks. These are more complex but still follow clear rules — perfect for agents with the right guardrails.
Multi-agent pipelines
The most powerful pattern is chaining multiple specialized agents into a workflow. One agent triages incoming requests, another researches the context, a third drafts a response, and a fourth reviews it. Each agent is simple and testable — the orchestration handles the complexity.
What agents are not good at (yet)
Tasks requiring deep domain expertise with no room for error (medical diagnosis, legal advice), creative work with subjective quality (brand voice, design), and problems with very little training data. These are better suited for human-AI collaboration than full automation.