1.10 – AI Dependency and Supply Chain Resilience Lesson

Lesson Overview

AI features often look simple on the surface, like a smarter search box or an automation agent. Underneath, they sit on a chain of external services and hidden limits. Foundational models, vector databases, embedding services, tool APIs and MCPs, and content delivery networks all introduce potential single points of failure. When one service stalls or changes without warning, the whole feature can stop.

This lesson focuses on making those dependencies visible and manageable. You will learn to map everything your AI features rely on and to document the limits that matter at 2:00 AM when an outage hits. You will run a business impact analysis that goes beyond technical breakage. The goal is to understand what breaks for your customers and teams, how bad it is, and how quickly you can recover.

We also look at vendor agreements, redundancy patterns, and graceful degradation. Examples include falling back from model-driven search to keyword search, and pausing an internal agent when it loses a key tool so a human can step in. Finally, you will see how to write runbooks and kill switches, and how to test them through planned drills that reveal real readiness, not just pretty diagrams.

Who This Is For

If you depend on external AI services or plan to add AI to business workflows, this lesson will help you keep operations steady during incidents. It is useful for teams that need clear plans and practical fallbacks, not just theory.

• Product managers shipping AI features that must keep working during vendor issues
• Engineering leaders responsible for uptime and failover
• Operations and support teams who need workable playbooks during outages
• Security and risk managers aligning SLAs with business risk
• Startup founders balancing speed with reliability

Where This Fits in a Workflow

Use this lesson when planning, launching, and operating AI features at scale. It belongs early in the design phase, when you choose vendors and architectures, and it continues through incident response and ongoing maintenance.

For example, before releasing AI-powered search, map every dependency and set a fallback to keyword search if the model stalls. If your automation agent relies on an external tool, define a pause and alert flow so it never acts without key data. During operations, use your runbooks and kill switches to disable risky features quickly and keep core functions available.

Technical & Workflow Benefits

The old way treats outages as surprises. Teams scramble to diagnose rate limits, decipher vague error codes, and contact a vendor in a different time zone. That burns time and customer trust. The approach in this lesson makes failures predictable and survivable.

By mapping dependencies and documenting limits, you shorten diagnosis. By ranking business impact, you protect what matters most, such as checkout or support tools. Redundancy and graceful degradation keep partial service available, such as cached content or a backup model. Runbooks and kill switches turn confusion into action, with clear owners and steps.

Two areas show a clear difference. In customer search, switching to keyword results maintains continuity instead of showing a blank page. For internal agents, pausing and alerting a human prevents bad decisions based on missing tools. Tracking failover success and mean time to recovery helps you see whether your resilience is real, and where to improve next.

Practice Exercise

Scenario: Choose one AI-powered feature you own, such as customer search, a support assistant, or an internal automation agent.

• Step 1: Map and document. List every dependency for that feature. Include model providers, vector databases, embeddings, tool APIs or MCPs, content delivery, quotas, rate limits, throttling, and geographic restrictions. Note who owns each dependency and how to contact them.
• Step 2: Plan for failure. Write a short business impact analysis for three outage cases. Rank severity and expected recovery time. Design a graceful degradation path for each case. Examples include caching likely results, falling back to a simpler algorithm, or switching to a secondary provider. Add a runbook and define the kill switch owner.
• Step 3: Test in a sandbox. Simulate a model outage or spike rate limits. Trigger your fallback and record failover success rate and mean time to recovery. Adjust your runbook, caching, or backup selection based on what you observe.

Reflection: In your test, did the fallback preserve the core value of the feature, or just keep it technically alive? What small change would most improve recovery time?

Course Context Recap

This lesson focuses on AI supply chain dependency and how to keep business functions available when external services fail. It builds your ability to map risk, plan fallbacks, and act quickly with clear runbooks and kill switches. Earlier lessons introduced risk thinking for AI features. Here you put that thinking into practice across vendors and infrastructure. Next, continue through the course to strengthen operations with testing habits and measurable recovery targets. Watch the video and follow along with the drills to make your resilience plans real.