Pipeline in a Trench Coat: When to Build an Agent vs. a Pipeline

Description and use

In 2026, many systems called "agents" are actually deterministic pipelines with an LLM inside. Converting them to a linear pipeline improves speed, reduces cost, and simplifies debugging.

Problem

Developers often build AI agents that are actually deterministic pipelines, leading to higher cost, nondeterminism, and debugging challenges.

Outcome

A deterministic pipeline outperforms a misnamed agent in production: faster, cheaper, testable, and easier to debug.

Workflow

• 1. Identify the task steps that can be predetermined.
• 2. Design a fixed sequence of steps (e.g., extract, transform, respond).
• 3. Insert LLM calls only where the model adds value (e.g., extraction, classification).
• 4. Remove reasoning loops and autonomous decision points.
• 5. Implement deterministic control flow and logging.
• 6. Write regression tests for each step.
• 7. Deploy and monitor for performance and errors.

Evidence

The agent did the same three steps every single time: Extract, transform, respond.

Source

When the model chooses the path, the same input can take different paths on different runs.

Source