Airtable entries below are illustrative scenarios, drawn from publicly documented Airtable failure modes. They describe patterns we assess, not engagements we have run, and they carry no client names and no outcome claims.
Mid-market SaaS (sanitized)
2026-Q1
AI Production Systems · Engagement
Stripe billing reconciliation, autonomous worker mesh Production concurrency controls
Single implementation utilizing production concurrency controls for nightly Stripe reconciliation across multi-entity ledger.
Replaced a 7-hour nightly batch job with a parallel worker mesh. Failure mode named: Stripe rate-limit ceiling caps throughput when entity count exceeds ~120; documented in the runbook with a backoff strategy and an alert.
Claude MCP Stripe API Supabase Python
Permissioned/sanitized client work · Last verified 2026-08-07
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Regional services firm (sanitized)
2025-Q4
AI Production Systems · Engagement
Invoice intake, manual data entry reduction Exception-queue-first intake
Inbound supplier invoices are classified, extracted, and GL coded by the system, with human review retained for line-item exceptions and higher-value invoices. Outcome figures are held back until the client approves publication.
AP team kept the exception queue and the sign-off authority. The system handles classification, line-item extraction, and GL coding for the long tail. Loom walkthrough and architecture diagram in the case file.
Claude MCP n8n Postgres
Permissioned/sanitized client work · Last verified 2026-08-07
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US enterprise IT (sanitized)
2025-Q3
AI Production Systems · Engagement
Intune policy rollout assistant Human approval before every write
Scoped JWT and RLS scaffolding against an existing Azure AD tenant, then a policy rollout assistant shipped to production with no standing write authority for the agent. Delivery figures are held back until the client approves publication.
Claude reads Intune policy state via Microsoft Graph through a scoped MCP server, drafts policy changes, and routes them to a human approver. Audit log is non-negotiable; every action carries the operator's identity.
Claude MCP Microsoft Graph TypeScript
Permissioned/sanitized client work · Last verified 2026-08-07
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Mid-market hospitality (sanitized)
2025-Q3
AI Production Systems · Engagement
UniFi multi-site network triage agent First triage hypothesis, no autonomous remediation
Correlates alerts across sites and writes a first triage hypothesis for the on-call engineer. Limit named: the agent does not act on hardware faults, it pages a human. Response-time figures are held back until the client approves publication.
MCP server exposes UniFi controller telemetry as typed tools. Claude correlates events across sites and writes a triage note into the incident channel. No autonomous remediation; that line stays on the human side.
Claude MCP UniFi API Python
Permissioned/sanitized client work · Last verified 2026-08-07
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Mid-market platform team (sanitized)
2025-Q2
AI Production Systems · Engagement
Azure cost guardrails and anomaly review Daily cost drift review with an owned ticket queue
Cost drift is reviewed daily instead of monthly, and anomalies open a ticket the FinOps team owns. Coverage is limited to the subscriptions enrolled in the MCP scope. Drift and savings figures are held back until the client approves publication.
Daily run reads Azure Cost Management exports, flags anomalies, writes a daily Slack digest, and opens a ticket for any drift over threshold. FinOps team owns the ticket queue; the agent never closes its own work.
Claude MCP Azure Cost Management TypeScript
Permissioned/sanitized client work · Last verified 2026-08-07
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Illustrative scenario, not a client engagement
Pattern
Airtable Exit/Rescue · Illustrative scenario
The base that hit a plan ceiling before anyone planned for it Pattern: hard platform ceiling
Publicly documented Airtable pattern: per-base record caps and per-base automation caps are tied to plan tier, so a base can stop accepting work or stop running automations before its owner has a migration decision ready.
The Continuity-First Decision Map treats this as a continuity question first: which processes stop if the ceiling is reached again, and does lifting the tier, trimming the base, or moving one module off the platform carry less risk. Staying on a higher tier is a legitimate answer here.
Airtable Automations Sync
Reference implementation · Last verified 2026-08-07
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Illustrative scenario, not a client engagement
Pattern
Airtable Exit/Rescue · Illustrative scenario
Business rules that only exist inside automations and views Pattern: unenforceable rules
Publicly documented Airtable pattern: as a base becomes a production system, the real business rules end up spread across automations, formula fields, and view filters, where nothing enforces them and no one can read them end to end.
The map separates rules that must be enforced from rules that are conventions. Enforcement pressure is usually what justifies a hybrid split rather than a full exit: the rule-bearing module moves, the rest of the base stays where the team already works.
Airtable Interfaces Formulas
Reference implementation · Last verified 2026-08-07
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Illustrative scenario, not a client engagement
Pattern
Airtable Exit/Rescue · Illustrative scenario
Permission model and per-editor cost pushing the decision Pattern: access pressure
Publicly documented Airtable pattern: teams commonly reach permission and per-editor limits before they reach a record ceiling, so access control and seat cost, not data volume, become the reason a change is on the table.
The map asks who genuinely needs write access, what a read-only surface would satisfy, and whether the pressure is a configuration problem or a platform problem. Stabilizing in place is frequently the safest justified outcome.
Airtable Permissions Integrations
Reference implementation · Last verified 2026-08-07
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