Agentic Workflow Automation

Transform AI from a chat interface into a proactive teammate with "arms and legs." By using the Model Context Protocol (MCP) and agentic frameworks, you can move beyond "vibe coding" to autonomous execution that saves 8–10 hours of manual work per week.

Core Principles

• Give the Brain "Arms and Legs": An LLM is just a brain; use standardized wrappers (MCP) to give it the ability to touch your data (Snowflake), your communication (Slack), and your production environment (GitHub).
• Start Small, Then Extend: Don't boil the ocean. Automate one specific, repetitive task (like a weekly marketing report) before attempting to build a general-purpose assistant.
• Value Over Code Quality: Focus on whether the agent solves the merchant or customer problem. Use AI to build "disposable" tools that solve immediate needs rather than over-engineering for long-term maintenance.

Implementation Workflow

1. Identify "High-Friction" Integration Points

Look for tasks where humans act as the "glue" between systems.

• Example: Taking data from a SQL database, analyzing it in Excel, and pasting it into a Slide deck.
• Criteria: The task should be well-defined, repetitive, and involve digital tools with APIs.