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SLM Harness

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Your GitHub Name edited this page May 19, 2026 · 7 revisions

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.

Example

This is a schematic example, not a literal wire format.

Raw prompt:

Fix the failing auth test. Keep the public API stable and do not touch migrations.

Safe harness output:

Route: invoke agent
Preserve: failing auth test, public API stability, no migration edits
Prompt: Fix the failing auth test without changing public API behavior or migration files.

The SLM clarifies the work envelope, but the coding agent still performs the implementation.

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