When long-running agents are allowed to refactor and extend scientific codebases over days, which concrete code- and data-level contracts (e.g., frozen interfaces, schema locks, golden-reference outputs) most effectively prevent trust-degrading drift while still allowing the agent to improve performance and coverage, and how do these contracts interact with checkpoint placement?

anthropic-scientific-computing | Updated at

Answer

Most drift control comes from a small set of explicit contracts, tied to coarse structural checkpoints and finer-grained local checks.

  1. High-value contracts for long-running refactors

  • Frozen public interfaces (APIs, CLI, config keys)

    • Freeze a narrow surface: function signatures, units, return types, key config names.
    • Allow internals (algorithms, performance) to change.
    • At each structural checkpoint (e.g., end of a refactor batch), run:
      • API compatibility tests
      • Golden I/O examples
    • Effect: prevents silent semantic drift at module boundaries while giving the agent room to optimize internals.

  • Data schema locks (on core tables/files)

    • Lock column names, types, key relationships, units, and allowed value ranges for “primary” datasets.
    • Permit new columns and auxiliary tables, but not silent re-typing/repurposing of existing ones.
    • At each checkpoint that touches ETL or loading code, run:
      • Schema validation
      • Simple distribution sanity checks
    • Effect: blocks data-drift bugs and misaligned cohorts while still allowing richer features.

  • Golden-reference outputs (small canonical cases)

    • Maintain a small suite of canonical input → output pairs (simulation cases, toy datasets, known-physics checks).
    • Tag each as either:
      • Exact (bitwise / strict numerical) or
      • Toleranced (within error bars or statistical tolerance).
    • Run these at:
      • Every structural checkpoint
      • Before/after any major performance refactor
    • Effect: anchors scientific meaning; allows performance changes as long as core behaviors stay within agreed tolerances.

  • Invariants and cheap local tests

    • Encode conservation laws, monotonicity, shape/dimension checks, simple unit checks.
    • Run continuously or at dense local checkpoints (per commit, per script run).
    • Effect: catches many small regressions between structural checkpoints without blocking progress.

  • Reproducibility harness for key workflows

    • For a few “primary” analyses, require: clean re-run → same result (within tolerance) from checkpointed state.
    • Run at major milestones and before large branching changes.
    • Effect: guards against hidden state and environment drift over days.
  1. Interaction with checkpoint placement

  • Structural checkpoints (coarse)

    • Triggered by: large refactors, new dependency additions, changes to core data models, or completion of a multi-hour batch of edits.
    • Run the heavy contracts:
      • Frozen-interface tests
      • Schema validation
      • Golden-reference suite
      • Reproducibility harness on 1–3 key workflows
    • Use these as rollback points: if any heavy contract fails, revert to last passing checkpoint.

  • Local checkpoints (fine)

    • Triggered by: each PR-sized change-set, new function, or change to a critical file.
    • Run light contracts:
      • Unit/property tests
      • Invariants
      • Quick golden cases (1–2 cheap ones)
    • Effect: catches most coding/numerical mistakes early with low overhead.

  • Event-triggered checkpoints

    • Trigger when: large diffs to public APIs/schemas, big changes in output metrics, or divergence in cross-checks.
    • Escalate to running structural-checkpoint contracts early instead of waiting for the next scheduled one.
  1. Balancing improvement vs drift

  • Keep contracts narrow and stable:

    • Freeze: external behavior, schemas, golden cases.
    • Leave flexible: algorithms, internal representations, parallelization, caching.

  • Allow two contract tiers:

    • Tier 1 (hard): must never break (core APIs, core schemas, key golden cases).
    • Tier 2 (soft): can change under explicit human review (experimental APIs, exploratory datasets, provisional tests).

  • Use human oversight at structural checkpoints:

    • Briefly review:
      • Proposed changes to Tier‑1 contracts
      • Any repeated near-failures of golden tests or schema locks
    • Reserve most human time for these infrequent but high-leverage decisions.

Net effect: A combination of frozen interfaces, schema locks, and golden-reference outputs, enforced primarily at structural checkpoints and supported by continuous invariants, most effectively limits trust-degrading drift while letting long-running agents optimize internals and expand coverage.