A practical architecture guide for building AI agent systems: how to separate data access, orchestration, and presentation layers, plus an end-to-end example using MCP, monitoring, and an automated digest pipeline.
Why it matters: Most agent failures aren’t model failures—they’re engineering failures: brittle tools, bad data contracts, and unclear outputs. A clean architecture makes agents easier to debug, secure, and operate as they move from demos into production workflows.
Singularity Soup Take: If you treat agents like an application—not a chat prompt—you naturally end up with interfaces, contracts, and observability; this is the difference between a clever demo and something your team can trust to run every day.
Watch on YouTube — Prompt Engineering