The Challenge

Operations teams often lose dozens of hours every week manually sorting support requests, extracting incident parameters, and routing tickets to specialized engineering pods.

Without automated triage, high-priority issues get buried in high-volume queues, response times degrade, and manual tracking leads to context loss during handoffs.

The System Architecture

We designed a multi-step agent pipeline that operates with deterministic boundaries while using reasoning models for unstructured document classification and action dispatch.

• Intake & Normalization: Inbound webhooks from communication tools and ticket systems are validated and stored with immutable event logs.
• Context Retrieval: Fast vector lookups retrieve related operational runbooks, historical incident resolutions, and team ownership matrices.
• Classification & Reasoning: The agent analyzes severity, routes to the appropriate tier, and drafts an initial root-cause summary.
• Human-in-the-Loop Review: A dedicated approval UI presents high-impact actions for verification before execution.

Engineering & Execution

The service was built on Next.js 16 and Node.js server runtimes, coordinating with Claude API via structured tool use. Postgres with pgvector provides low-latency semantic indexing over runbooks and escalation policies.

Every agent decision is logged with input snapshots, token budgets, and verification signatures, ensuring full observability across operations.

Measurable Impact

By pairing automated classification with instant human handoffs, the system handles 80% of routine categorization autonomously while ensuring that complex edge cases are brought to the right operators with zero lost context.