When to Use Agents vs. Simpler AI

The biggest waste in AI projects isn't failed agents—it's using agents when a simple prompt would suffice.

Every layer of complexity you add (chains, tools, agents, multi-agent systems) introduces:

• More failure points
• Higher latency
• Greater cost
• Harder debugging

The goal isn't to build the most sophisticated AI system. It's to solve the problem with the minimum viable complexity.

Knowing your problem type before you start building saves weeks of wasted effort.

The AI Solution Spectrum

Sharp-Edged vs Rough-Edged Problems

The Problem Classification Framework

Before building anything, run your problem through this decision tree:

START: What problem are you solving?
│
├─ Is there ONE correct answer?
│  │
│  ├─ YES → Can you compute it without AI?
│  │        │
│  │        ├─ YES → Use code, not AI (Level 0)
│  │        │        Example: Calculate shipping cost
│  │        │
│  │        └─ NO → Use single prompt or chain (Level 1-2)
│  │                 Example: Extract specific data from document
│  │
│  └─ NO → Does it require multiple distinct steps?
│          │
│          ├─ NO → Use single prompt (Level 1)
│          │       Example: Write a product description
│          │
│          └─ YES → Does it need dynamic decisions mid-process?
│                   │
│                   ├─ NO → Use prompt chain (Level 2)
│                   │       Example: Research → Outline → Draft → Edit
│                   │
│                   └─ YES → Does it need parallel work streams?
│                            │
│                            ├─ NO → Use single agent (Level 3)
│                            │       Example: Support bot handling varied requests
│                            │
│                            └─ YES → Use multi-agent system (Level 4)
│                                     Example: Complex research with multiple specialists

Complexity Matching Examples

Warning Signs of Over-Engineering

You're probably over-engineering if:

1. Your agent mostly follows the same path → Use a chain instead
2. You're building "just in case" capability → YAGNI (You Aren't Gonna Need It)
3. Simple prompts solve 90% of cases → Handle the 10% separately
4. You can't explain why you need agents → You probably don't
5. Debugging takes longer than the task → Reduce complexity

When Agents ARE the Right Choice

Agents earn their complexity when you have:

• Dynamic tool selection: Different inputs require different tools
• Uncertain path length: Can't predict steps needed upfront
• Real-time adaptation: Must respond to external changes
• Autonomous operation: Runs without human guidance
• Complex reasoning chains: Multi-step logic with branching

Exercise: Classify These Problems

For each scenario, identify:

1. Sharp-edged or rough-edged?
2. What level on the solution spectrum?
3. Why that level (not higher or lower)?

Scenarios:

1. Generate weekly social media posts from blog content
2. Route incoming emails to the right department
3. Create personalized workout plans based on user goals
4. Validate that uploaded invoices match purchase orders
5. Research and summarize industry news for executives
6. Answer employee questions about HR policies
7. Generate code documentation from source files
8. Orchestrate a hiring pipeline (screen, interview, assess)
9. Translate product descriptions to 5 languages
10. Monitor competitor pricing and alert on changes

The Complexity Audit

Before your next AI project, ask:

1. What's the simplest solution that could work?
2. What specific capability requires the next complexity level?
3. Can I defer that capability until proven necessary?
4. What's my fallback if the complex approach fails?

1. Start at Level 0 and only add complexity when forced
2. Sharp-edged problems often don't need AI—code works better
3. Rough-edged problems are AI's strength, but start simple
4. 80% of use cases need prompts or chains, not agents
5. Match complexity to problem, not to what's technically possible

The best AI operators know when NOT to use agents.