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Agents vs Simple LLM Apps

Understand the key differences between simple LLM applications and autonomous AI agents

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Practical Application

Now you understand the differences. But when should you actually choose each approach? Let's build a decision framework.

The Decision Matrix

✅Use Simple LLM When...

  • →Task is well-defined: "Summarize this text," "Translate to Spanish," "Generate product description"
  • →Single-step execution: Input → Output, no iteration needed
  • →Speed is critical: Need sub-second response times (<500ms)
  • →Cost is a constraint: Budget is $0.001-0.01 per request
  • →No external data needed: Answer can be generated from model knowledge
  • →Predictability matters: Same input should give similar output every time
EXAMPLE USE CASES
• Content generation (blog posts, emails)
• Text classification (sentiment, categories)
• Code completion (autocomplete, snippets)
• Translation & summarization

🤖Use Agent When...

  • →Task requires exploration: "Find the cheapest flight," "Diagnose this bug," "Research this topic"
  • →Multi-step reasoning: Plan → Act → Observe → Adjust loop needed
  • →External tools required: APIs, databases, file systems, browsers
  • →Latency is acceptable: Users can wait 5-30 seconds for thorough results
  • →High-value tasks: ROI justifies 5-20x cost increase
  • →Autonomy adds value: User wants to "set and forget"
EXAMPLE USE CASES
• Customer support automation
• Data analysis & research
• Software debugging & testing
• Booking & scheduling

Interactive Decision Framework

Answer these questions about your use case, and we'll recommend the best approach:

1. Does your task require external tools or APIs?

2. Can the task be completed in a single response?

3. What's your latency requirement?

4. What's the value of automation?

Real-World Case Studies

📝

Grammarly: LLM for Speed

Real-time writing assistant needs <100ms latency. Uses fine-tuned LLMs for grammar/style suggestions.

Why not agent? Speed is non-negotiable. Single-pass corrections sufficient.
🤖

Intercom Fin: Agent for Autonomy

Customer support agent searches knowledge base, checks order status, processes refunds autonomously. 50% ticket resolution rate.

Why agent? Multi-step workflows, tool use, decision-making required.
🔀

GitHub Copilot: Hybrid Approach

Code completion uses LLM (fast). Workspace feature uses agent (codebase analysis, multi-file edits).

Why hybrid? Different features have different requirements.