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Inference Pilot Scenarios

Goal

This document defines the first recommended pilot scenarios for structured risk inference.

In simple terms:

start where semantic risk is real, but still bounded enough to compare outcomes.

Why pilot scenarios matter

Alpha 5 should not begin as a universal capability.

It should begin where teams can ask a concrete question:

did structured risk inference improve the quality of the decision and the validation plan in this kind of task?

That means the first scenarios should be:

  • high enough risk to justify the artifact
  • narrow enough to compare outcomes
  • common enough to repeat more than once

1. Patch review with semantic uncertainty

Use when a proposed change looks plausible but its semantic effect is not obvious from a quick read.

Good fit when:

  • the patch is not large
  • the logic change is subtle
  • the main question is behavior, not style

What to observe:

  • whether the artifact surfaces assumptions that a normal review would miss
  • whether suggested tests become sharper
  • whether the review discussion becomes more concrete

2. Refactor with invisible regression risk

Use when a refactor appears mechanically safe but may still change behavior in indirect ways.

Good fit when:

  • abstractions are being moved or rewritten
  • signatures stay similar
  • downstream behavior could shift without obvious compiler or lint errors

What to observe:

  • whether impacted paths are identified earlier
  • whether invariants are made explicit
  • whether hidden regression risk becomes easier to validate

3. Semantic regression risk with weak test coverage

Use when tests exist but do not really cover the risk surface that matters.

Good fit when:

  • the suite passes
  • confidence still feels fragile
  • the risky behavior depends on workflow meaning rather than syntax or type correctness

What to observe:

  • whether test gaps are identified more honestly
  • whether suggested tests are more aligned with real risk
  • whether confidence becomes better calibrated

4. High-stakes inter-agent handoff

Use when work is being passed to another agent and the next step depends on preserving rationale, not only instruction.

Good fit when:

  • the receiving agent must continue execution
  • assumptions matter
  • the task has real downstream consequence if misunderstood

What to observe:

  • whether the next agent needs less reinterpretation
  • whether validation continuity improves
  • whether reasoning survives the handoff better than instruction alone

5. Regression-aware maintenance round

Use when a maintenance loop no longer looks trustworthy enough to continue on patch confidence alone.

Good fit when:

  • a previously stable behavior regressed
  • a lane health read or alert suggests hidden degradation
  • the next round would otherwise continue on weak assumptions
  • the team needs to decide whether to widen the gate, stay blocked, or proceed with stronger proof

What to observe:

  • whether the artifact clarifies why the current gate is too light or sufficient
  • whether the suggested tests target the real degradation surface
  • whether the round becomes easier to review and continue safely

Scenarios to avoid first

Do not begin Alpha 5 pilots with:

  • cosmetic changes
  • trivial local fixes
  • formatting-only tasks
  • broad open-ended redesign work
  • tasks where no meaningful semantic risk exists

These cases make it too hard to tell whether the capability is helping.

Evaluation questions

For each pilot scenario, ask:

  1. Did the artifact reveal meaningful assumptions or unknowns?
  2. Did it improve the validation plan?
  3. Did it reduce semantic ambiguity?
  4. Did it improve handoff clarity, if a handoff was involved?
  5. Did it improve gate selection or explain why the current gate was insufficient?
  6. Was the extra overhead justified by the risk level?

Alpha 5 pilots should begin as:

  • small
  • selective
  • repeatable
  • reviewable

The goal is not to prove universal value immediately.

The goal is to find the first task shapes where the capability clearly earns its cost.

Suggested next reading

  • docs/structured-risk-inference.md
  • starter-pack/templates/inference-artifact-template.md
  • starter-pack/guides/inference-trigger-guidance.md
  • starter-pack/guides/risk-to-gate-mapping.md
  • examples/structured-risk-inference/README.md

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