Real-World Agent Use Cases and Constraints

It is easy to build an agent that greets you and tells you the weather. It is incredibly difficult to build an agent that manages a corporate supply chain or audits a medical billing system. In this lesson, we will look at three high-impact industry use cases for AI agents and the four "Production Killers" (constraints) that you will spend most of your career managing.

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1. Industry Use Case: The Autonomous Customer Support Suite

The "First Generation" of AI support was simple FAQ retrieval (RAG). The "Agent Generation" is about Action.

The Scenario

A customer contacts an e-commerce platform because their package is missing.

• Traditional RAG: Tells the user the refund policy.
• AI Agent:
  1. Authenticates the user.
  2. Calls the Shipping API to track the package.
  3. Finds the package is stuck at a local sorting facility.
  4. Calls the Logistics API to flag the issue.
  5. Checks the Inventory API to see if a replacement is available.
  6. Offers the user a choice: "Wait 2 days for the original, or I can ship a replacement now for free."
  7. Executes the chosen action.

Architectural Diagram

graph TD
    User[Customer Query] --> Routing{Agent Intent}
    Routing -->|Action Required| Tools[Action Layer]
    Tools --> ShipAPI[Shipping API]
    Tools --> InvAPI[Inventory API]
    Tools --> RefundAPI[Refund API]
    ShipAPI --> Reasoning[Evaluate Problem]
    InvAPI --> Reasoning
    Reasoning --> Result[Propose Solution to User]

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2. Industry Use Case: The Software Engineering "Teammate"

Agents are moving from "snippets" to "systems." Instead of generating a single function, agents are being given access to entire repositories.

The Scenario: Automated Bug Triaging

When a new issue is opened on GitHub:

1. The Triage Agent reads the issue description.
2. The Search Tool finds the relevant files in the repo.
3. The Debugger Tool attempts to reproduce the bug by writing and running a small test case.
4. The Planning Node proposes a fix.
5. The GitHub Tool opens a Draft PR with the fix and the passing test.

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3. Industry Use Case: Financial Auditing and compliance

In high-stakes industries, "Agency" means exhaustive verification.

Scenario: Know Your Customer (KYC)

1. The Scanner Agent receives a PDF of a business license.
2. The Verification Agent compares the names against global sanctions lists.
3. The Cross-Reference Agent looks for the business on LinkedIn and Google Maps to verify it's a real entity.
4. The Auditor Agent (Human-in-the-loop) reviews the final "Confidence Report" before approving the account.

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The Four "Production Killers" (Constraints)

This is the most important part of the lesson. In a lab, you ignore these. In production, they are your metrics for success.

1. The Reliability Gap (The "90% Problem")

LLMs are probabilistic. If an agent is 95% reliable at each step, and it needs to take 5 steps to solve a problem, its overall success rate is $0.95^5 \approx 77%$.

• The Constraint: As agent complexity increases, reliability decreases exponentially.
• The Solution: We build "Self-Correction" loops in LangGraph where the agent reviews its own work before moving to the next node.

2. The Latency Wall (The "Human Patience" Problem)

A reasoning loop (Thought -> Action -> Observation -> New Thought) takes time.

• 1 step: 2 seconds.
• 5 steps: 10 seconds.
• The Constraint: Users will not wait 30 seconds for a "thinking" spinner.
• The Solution: Use Streaming Updates. Instead of waiting for the final answer, show the user the agent's thoughts in real-time. This "Perceived Latency" is much better.

3. The Token Tax (The "Wallet" Problem)

Autonomous agents can be expensive. If an agent gets stuck in a loop trying to solve a hard problem, it can consume 100,000 tokens in minutes.

• The Constraint: You cannot build a product with an infinite cost-per-query.
• The Solution: We implement "Recursion Limits" and "Budget Guards" in the orchestration layer (Module 11).

4. The Security Sinkhole (The "Isolation" Problem)

If you give an agent access to your shell, and a malicious user tells the agent: "Delete all files in /root", the agent might do it.

• The Constraint: You cannot give AI agents raw access to your production infra.
• The Solution: We use Dockerized Sandbox Containers. Every agent runs in its own "mini-computer" that is destroyed after the task is done (Module 7).

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Detailed Constraint Matrix

Constraint	Impact on User	Developer Challenge	Metrics to Track
Non-Determinism	Unexpected results	Testing is hard	Pass rate on Golden Dataset
Model Drift	System stops working	Updates break prompts	Regression testing latency
State Bloat	Context window overflows	Managing history	Tokens per session
Tool Failure	System hangs	Defensive programming	Tool error rate

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Summary and Mental Model

When designing an agent, always ask: "What is the cost of failure?"

• If the agent is recommending a movie: Low cost. High autonomy is fine.
• If the agent is executing trades on Wall Street: High cost. Extreme guardrails and human-in-the-loop are mandatory.

In the next lesson, we will reveal the full architecture of the course and show you exactly what we will be building (and deploying!) to solve these real-world constraints.

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Exercise: Design the Guardrails

Imagine you are building an agent for a car rental company that allows users to book cars via chat.

1. Which tool is the most "Dangerous"? (Access to personal info? Payment gateway? Car unlock API?)
2. Where would you put a "Human-in-the-loop" interrupt?
3. If the "Booking API" is down, how should the agent respond without sounding like a broken script?