The "Agentic AI" Reality Check: When to Say No

When you have a hammer like LLMs, everything looks like a nail. But agents are expensive, slow, and unpredictable. Applying an agentic architecture to a problem that could be solved with three lines of Python is a major engineering failure.

Here is when you should AVOID agents.

1. High-Stability Workflows

If the task is strictly defined and never changes (e.g., calculating sales tax), use Code.

• Agent: Might decide to "research" the tax rate instead of using your local library, doubling the cost and potentially getting the number wrong.
• Code: Always correct, zero cost, sub-millisecond latency.

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2. Low-Latency Requirements

Agents take time to "think." Every turn of an agent loop involves an API call to an LLM, which can take 1-5 seconds.

• Agent: Not for real-time gaming, high-frequency trading, or instant search suggestions.
• Rule of Thumb: If the result is needed in less than 500ms, an agent is not the solution.

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3. Fixed Step Sequences

If a process always has the exact same 5 steps:

1. Fetch User
2. Fetch Order
3. Compare IDs
4. Update Database
5. Send Email

Do NOT use an agent. Just write a function. Using an agent here is "Autonomy Theater"—you’re paying for a model to "decide" to do exactly what you already told it to do.

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4. Sensitive Financial/Medical Actions

Unless you have a Human-in-the-Loop (see Module 3), do not let an autonomous agent move money or provide medical prescriptions. The risk of a "Hallucinated Action" is too high.

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5. The "Over-Engineering" Checklist

Ask yourself these four questions before building an agent:

1. Can I solve this with a Regex? (If yes, use a Regex).
2. Can I solve this with a fixed Python script? (If yes, use a script).
3. Is the path to the solution variable? (If no, use a script).
4. Is the data format unpredictable? (If yes, this is a good candidate for an agent).

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6. The Cost Comparison

Factor	Script / Function	Agentic AI
Development Time	Low	High (Prompt tuning + testing)
Unit Cost	$0.0001	$0.01 - $1.00+
Reliability	99.99%	85% - 95%
Complexity	Linear	Exponential
dock

7. Visualization: Script vs Agent Complexity

graph TD
    subgraph "Script (Stable & Fast)"
    A[Input] --> B[Step 1: Fetch]
    B --> C[Step 2: Calculate]
    C --> D[Step 3: Save]
    D --> E[Output]
    end

    subgraph "Agent (Flexible but Expensive)"
    F[Goal] --> G{LLM Plan}
    G -->|Try 1| H[Tool Call A]
    H --> I[Observe Result]
    I --> G
    G -->|Try 2| J[Tool Call B]
    J --> K[Observe Result]
    K --> G
    G -->|Success| L[Final Output]
    end

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Key Takeaways

• Agents are for Unstructured inputs or Dynamic environments.
• Don't use an agent for a fixed-step workflow.
• Latency is a deal-breaker for many agent use cases.
• Cost scales with the number of "Thinking loops" the agent performs.
• When in doubt, start with simple code and only add agency when the code breaks.