System Architecture and Reliability

• The support system now operates across Discord, Slack, and an internal React-based console, using an observable workflow where every agent action generates traces and metadata.3:45
• Because remote Model Context Protocol (MCP) documentation tools proved unreliable for customer-facing inquiries, the team built a RAG (Retrieval-Augmented Generation) fallback based on a dedicated knowledge pipeline and vector database.4:58
• The team implemented a layered safety design that combines deterministic guardrails, limited tool access, and lightweight-model evaluation to monitor outputs for accuracy and risk.7:08

Workflow and Operations

• Every support task follows a rigorous intent-based lifecycle: defining intent, grooming with Claude Code and MCP tools, evaluating through cross-model review, and requiring human approval before production.9:31
• Unexpected multilingual traffic in Discord surfaced as an immediate operational challenge, highlighting the need for broader language support that the team had not originally prioritized.11:48
• Internal Slack triage channels turn Discord activity into actionable signals, allowing the team to use LangSmith trace data as a control plane for identifying systemic failures and refining agents.7:52

Development Philosophy

• The internal Support Assistant remains in a read-only state for now, with human-in-the-loop features like "send customer response" being developed as the next phase of deployment.8:39
• Evan Kormos, the speaker and a builder of Coinbase engineering teams, argues that developers must treat agent engineering as a distinct discipline that requires both product-focused engineering skills and a machine-learning mindset.12:47