What you’ll build in 24 hours

• A Checkout Recovery Agent that triggers on abandoned checkout and cart updated events, engages shoppers via email/SMS/chat, and hands off to support if needed.
• Governed access via an agent registry (identity, policy, secrets) so the agent only sees what it should.
• Observability with OpenTelemetry’s Generative AI and Agent semantic conventions for success rate, time‑to‑first‑touch, and cost per recovery.
• Cost guardrails and smart incentives so you recover revenue without margin leaks.

Why standards: Microsoft just introduced Agent 365 to manage fleets of agents across enterprises. Whether or not you use it, building on open standards (MCP for tool access, OpenTelemetry for telemetry) keeps you portable. Wired.

Who this is for

• Shopify/WooCommerce founders who want a measurable lift on BFCM without a long integration project.
• Growth and CX leads who need a governed, observable alternative to opaque third‑party apps dominating the SERPs.
• Platform PMs standardizing on MCP + OpenTelemetry for agent portability and compliance.

The 24‑Hour Plan

Hour 0–2: Define the mission and SLOs

• Primary goal: increase recovered revenue from abandoned checkouts in 7 days.
• Key SLOs: Recovery Success Rate (recovered checkouts / abandonments), Time‑to‑First‑Touch (TTFT) < 5 minutes, Cost per Recovery (tokens + messaging + incentives), Escalation Rate to human < 15%.
• Use our 7‑day SLO guide for definitions and dashboards: Ship Agent SLOs That Matter.

Hour 2–6: Events and connectors

1. Shopify events: subscribe to checkout/update and abandoned_checkout (or cart intent equivalent). Use a lightweight edge function to normalize payloads.
2. Channels: email + SMS/WhatsApp + on‑site chat. Keep channel‑mix simple at launch; expand after you see telemetry.
3. MCP for tools: expose your messaging providers and discount service as MCP tools so any agent can call them consistently. See MCP.

Hour 6–12: The agent brain

Start with a single Task Router + Recovery Worker pattern:

• Task Router: classifies abandonment reason (price sensitivity, shipping friction, address failure, payment error, distraction) and picks channel + incentive.
• Recovery Worker: executes a 2–3 step sequence, e.g., gentle reminder → targeted nudge (shipping, sizing help) → limited incentive. Escalate to a human if signals indicate confusion or high AOV risk.

Instrument with OpenTelemetry’s Generative AI/Agent semconv so every step is traceable and comparable across models. References: GenAI semconv; Agent spans.

Hour 12–18: Governance, identity, and policy

• Register the agent (name, purpose, owner, allowed tools, secrets) in your agent registry. Start here: Build an Agent Registry for MCP/A2A.
• Apply OPA‑style policies: who can approve incentives, max discount %, and PII redaction for telemetry.
• Prep a basic red‑team pass focused on prompt injection and discount abuse. See: Red‑Team Your Customer Support Agent.

Hour 18–24: Dashboards, thresholds, and roll‑out

• Create an Agent Recovery dashboard with: Recovery Success Rate, TTFT, Conversions by Channel, Cost per Recovery, Discount Efficiency (revenue lift / incentive).
• Set budget guardrails and model routing strategies for spikes. Use our FinOps playbook to cut costs 25–40%: AI Agent FinOps.
• Launch to 10–20% of abandoned checkouts. Increase coverage as SLOs hold.

Telemetry you must capture (and why)

• gen_ai.operation.name, gen_ai.provider.name, tokens, latency → model cost and speed baselines. OpenTelemetry GenAI.
• Agent spans for task routing, action execution, and tool calls → end‑to‑end recovery attribution. Agent semconv.
• Business metrics: conversion, AOV change, discount % used, gross margin impact.
• Safety events: personally identifiable information (PII) exposure attempts, unusual incentive requests, repeated handoffs.

The minimal message playbook (tested patterns)

1. Reminder (no discount): personalize with item names, size/color, and shipping promise.
2. Obstacle removal: offer sizing help, alternative payment method, or address validation link.
3. Targeted nudge: limited, conditioned incentive for price‑sensitive segments only (e.g., free shipping over threshold).

Why it works: shoppers respond to relevance more than blanket discounts. Baymard’s research shows friction and uncertainty drive a large chunk of abandonment—not just price. Baymard.

Build vs. buy: where your agent fits

Search results for “AI cart recovery” skew toward app listings (voice/SMS/email bots for Shopify). If you need a plug‑and‑play tool, the ecosystem is rich. If you want portability, policy, and deep telemetry, building a thin agent on MCP + OpenTelemetry keeps you in control and future‑proof for platforms like Agent 365 as they mature. Examples of app‑style approaches in the wild: Revana, CartMind. Context: Agent 365 coverage, and the industry’s definition debate: TechCrunch.

Architecture sketch (portable and observable)

Flow: Shopify Webhook → Edge Normalizer → Task Router (classify) → Recovery Worker (channel + message) → Incentive Service → Telemetry Exporter → Dashboard/Alerts.

{
  "task": "recover_checkout",
  "signals": {"channel": "sms","ttft_ms": 8200},
  "policy": {"max_discount_pct": 10, "aov_floor": 50},
  "otel": {"gen_ai.provider.name": "openai","gen_ai.operation.name": "chat"}
}

MCP exposes tools like send_email, send_sms, create_discount, and handoff_to_human so different agent frameworks can interoperate. See the MCP ecosystem: GitHub org.

Guardrails you shouldn’t skip

• Policy: incentives capped and conditioned; handoff on payment/address issues.
• Privacy: redact PII in telemetry; store only hashed identifiers.
• Red‑team: test for prompt injection, code execution, and discount manipulation before ramp. Start here: 48‑Hour Red‑Team.
• FinOps: throttle model usage during spikes; prefer cheaper models for reminders, premium models for escalations. FinOps Playbook.

What “good” looks like by Day 7

• Recovery Success Rate up 10–25% from baseline (varies by category).
• TTFT < 5 minutes on 90% of attempts.
• Cost per Recovery trending down week‑over‑week via routing.
• Discount Efficiency ≥ 3:1 revenue lift vs. incentive cost.

Benchmark context: 70%+ carts are abandoned industry‑wide; during holiday peaks, shoppers are mobile‑first and time‑sensitive. Tight TTFT matters. Sources: Baymard; Adobe.

Going further after Black Friday

• Expand to post‑purchase upsell/returns prevention flows.
• Add memory and A/B evaluation harnesses with standardized telemetry. See: Agent Memory.
• Prepare for platform governance audits (ISO/IEC 42001, EU AI Act alignment). See: Governance Checklist.
• Evaluate agent management platforms as they mature (context: Agent 365).