Customer Support Agent

A B2B SaaS company handling 500+ support tickets daily has a three-tier support structure. L1 agents handle password resets and known issues from runbooks. L2 handles technical troubleshooting. L3 handles bugs and escalations to engineering. The problem: L1 agents resolve only 30-40% of tickets, forwarding the rest to L2 with incomplete context.

Intercom's Fin AI reports a 66% average resolution rate across 6,000+ customers. Forethought claims up to 98% resolution with well-optimized knowledge bases. Ada handles enterprise-scale multilingual support. These platforms proved AI can resolve support tickets by understanding the problem and providing accurate answers.

The gap: most SaaS companies have product-specific context (user account state, feature flags, subscription tiers, recent actions) that generic chatbots cannot access. Resolution requires integrating with the product database, not just the help center.

This agent connects to your product database, knowledge base, and ticketing system. When a ticket arrives, it pulls the customer's full context: subscription tier, feature flags, recent actions, error logs, and account health signals. It understands the problem in context — not just what the customer said, but what their account state reveals.

For known issues, it resolves directly: walks the customer through steps, triggers account actions (password resets, feature toggles, data exports), and confirms resolution. For novel issues, it performs diagnostic analysis: correlating symptoms with error logs, recent deployments, and known bug reports.

When escalation is necessary, the agent creates a structured handoff: customer context, diagnostic findings, attempted resolutions, and a recommended next step. L2 agents start working the problem immediately instead of re-gathering context.