SLM Harness

SLM Harness

PromptPilot uses a small language model as the harness around a frontier coding agent.

Why use an SLM?

Many coding-agent sessions contain control decisions that do not require a frontier model:

• Is this prompt ambiguous?
• Is this mostly repeated log output?
• What are the important file paths?
• What user constraints must be preserved?
• Should the prompt be passed through unchanged?
• Is this a simple answer that does not require agent execution?

The SLM controls the workflow; it does not replace the coding agent.

What the SLM is trusted to do

• Extract constraints
• Classify intent
• Detect ambiguity
• Compress repetitive output
• Recommend route
• Preserve structured facts
• Flag high-risk transformations for passthrough

What the SLM is not trusted to do

• Implement complex code changes
• Debug deep logic bugs
• Infer hidden product requirements
• Drop constraints to save tokens
• Make irreversible changes
• Override explicit developer instructions

Fallback principle

When uncertain, passthrough.

The SLM is useful only when it reduces noise without changing intent. A slightly more expensive run is better than a cheap but wrong run.

Harness outputs

A successful harness result should make the downstream agent's job clearer while preserving meaning. Typical outputs include:

• A clarification question when the request is ambiguous.
• A direct answer when no coding-agent execution is needed.
• A passthrough recommendation when rewrite risk is high.
• A safe rewrite that preserves constraints and file references.
• A compressed tool-output summary that keeps failures, paths, stack frames, commands, and API boundaries.