LLMs vs Agent Architectures: When to Build Which

There's a common misconception that every AI application needs to be an "agent." The reality: most should stay as simple LLM calls. Knowing the difference is critical to shipping production systems that actually work.

The LLM: Simple, Fast, Deterministic

An LLM call is just function execution:

Input: prompt + context
Output: text completion
No state, no loops, no tool calling

When to use:

Content generation (blogs, summaries, rewrites)
Classification tasks (sentiment, categorization)
Q&A over static knowledge
Anything where the output is directly derivable from the input

The Agent: Stateful, Iterative, Tool-Using

An agent is a control loop:

Observe: Read current state
Reason: Decide what to do next
Act: Call tools, update state, request input
Repeat: Continue until goal satisfied

When to use:

Multi-step workflows with dependencies
Tasks requiring external data or actions (API calls, database queries, file operations)
Situations where the path isn't known upfront
Long-running processes that need to pause and resume

The Decision Framework

Ask these questions:

Does it need memory? If the system must remember past interactions across sessions → agent
Does it need tools? If it must call APIs, query databases, or take actions → agent
Is the path unknown? If the system must decide its own next steps → agent
Is it one-shot? If input → output with no iteration → LLM

The Cost of Complexity

Agents are expensive in every dimension:

Latency: Multi-turn reasoning adds seconds
Cost: More tokens, more API calls
Reliability: More moving parts = more failure modes
Debugging: Non-deterministic paths are hard to trace

Hybrid Patterns

The best production systems often combine both:

Example: Document Q&A

LLM for answering from retrieved context (fast, deterministic)
Agent for query planning and multi-source retrieval (only when needed)

Example: Code Generation

LLM for generating individual functions (stateless)
Agent for multi-file refactoring with testing loops (stateful)

The Rule: Start Simple

Always start with the simplest architecture that could work. Upgrade to an agent only when you have concrete evidence that the simpler approach can't solve the problem.

Most "agent" projects fail because they're solving LLM problems with agent complexity.