Building Your First AI Agent for Customer Support
A practical guide to deploying an AI support agent that actually helps — without hallucinating your refund policy.
AI support agents are only useful when they're grounded in your real data and bounded by clear rules. Here's a pattern we use when building agents for businesses.
Start with a narrow scope
Don't launch an agent that handles "everything." Start with one category:
- Order status lookups
- FAQ answers
- Appointment scheduling
A focused agent is easier to test, monitor, and improve.
Ground it in your knowledge base
Connect the agent to documents it can search — policies, product docs, internal wikis. The Model Context Protocol (MCP) makes this straightforward: your agent calls tools to fetch authoritative answers instead of guessing.
Add guardrails
Every production agent needs:
- Escalation rules — when to hand off to a human
- Action limits — what the agent can and cannot do (e.g., read orders but not issue refunds)
- Logging — full transcripts for review and training
Measure what matters
Track resolution rate, escalation rate, and customer satisfaction — not just "messages handled." An agent that deflects tickets but frustrates users isn't a win.
Iterate in production
Ship a v1 quickly, review real conversations weekly, and tighten prompts and tools based on failure patterns. The best agents are maintained products, not one-time deployments.