Product Operations Is Becoming the AI Translation Layer — And Now the Company OS
Description and use
The article explains moving from treating AI as a query tool to an operational layer of scheduled agents that perceive, plan, and act using MCP and A2A protocols, n8n orchestration, and Supabase context storage. This speeds decision-making, reduces manual effort, and keeps monthly costs around $325.
Problem
Product operations teams struggle to translate raw AI model outputs into actionable product decisions, leading to bottlenecks in decision-making.
Outcome
Implementing scheduled agents with MCP/A2A, n8n orchestration, Supabase context storage, and layered validation reduces manual decision surface time to ~2 days for first agent and ~2 weeks for full cadence, while keeping monthly costs around $325 and improving trust through deterministic gates and rubric evaluation.
Workflow
- Perception: ingest inputs from Slack, Notion, GitHub, analytics, support tickets and ground them with RAG against living context.
- Planning: weigh goals and constraints with a rule engine, produce a ranked decision set.
- Action: answer, update records, trigger workflows, call other systems, close the loop for next perception cycle.
Evidence
Product operations is becoming the AI translation layer — the connective tissue between raw model output and product decisions.
Perception → Planning → Action loops that run on a schedule.
MCP (Model Context Protocol) gives agents standardised access to tools — file systems, APIs, databases, browsers — without custom integrations per model.
Layer 1 — Deterministic Gates (cheap, fast, binary) … Layer 2 — Rubric Evaluation (expensive, nuanced, weekly)
Total ~ $325/mo
- Record date: 2026-10-11T01:55:53.932Z