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Performance and Scaling

Performance work should preserve the deterministic and contract-driven behavior the package already promises.

This page keeps optimization work honest. A package is not healthier if it gets faster by becoming harder to reason about, harder to replay, or easier to break for downstream readers.

Treat the operations pages for agentic-proteins as the package's explicit operating memory. They should make common tasks repeatable without relearning the workflow from logs or oral history.

Visual Summary

flowchart TB
    page["Performance and Scaling<br/>clarifies: repeat workflows | find diagnostics | release safely"]
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    classDef positive fill:#dcfce7,stroke:#16a34a,color:#14532d;
    classDef caution fill:#fee2e2,stroke:#dc2626,color:#7f1d1d;
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    classDef action fill:#fef3c7,stroke:#d97706,color:#7c2d12;
    step1["packages/agentic-proteins/pyproject.toml"]
    step1 --> page
    step2["CLI entrypoint in src/agentic_proteins/interfaces/cli.py"]
    step2 --> page
    step3["HTTP app in src/agentic_proteins/api/v1"]
    step3 --> page
    run1["tests/unit for api, contracts, core, interfaces, model, and runtime"]
    page --> run1
    run2["tests/e2e for governed flow behavior"]
    page --> run2
    run3["tests/regression and tests/smoke for replay and storage protection"]
    page --> run3
    release1["pyproject.toml"]
    run1 --> release1
    release2["README.md"]
    run2 --> release2
    release3["CHANGELOG.md"]
    run3 --> release3
    class page page;
    class step1,step2,step3 positive;
    class run1,run2,run3 anchor;
    class release1,release2,release3 action;

Performance Review Anchors

  • inspect workflow modules before optimizing boundary code blindly
  • use the package tests that exercise realistic workloads
  • treat artifact and contract drift as a regression even when performance improves

Test Anchors

  • tests/unit for api, contracts, core, interfaces, model, and runtime
  • tests/e2e for governed flow behavior
  • tests/regression and tests/smoke for replay and storage protection
  • tests/golden for durable example fixtures

Concrete Anchors

  • packages/agentic-proteins/pyproject.toml for package metadata
  • packages/agentic-proteins/README.md for local package framing
  • packages/agentic-proteins/tests for executable operational backstops

Use This Page When

  • you are installing, running, diagnosing, or releasing the package
  • you need repeatable operational anchors rather than architectural framing
  • you are responding to package behavior in local work, CI, or incident pressure

Decision Rule

Use Performance and Scaling to decide whether a maintainer can repeat the package workflow from checked-in assets instead of memory. If a step works only because someone already knows the trick, the workflow is not documented clearly enough yet.

What This Page Answers

  • how agentic-proteins is installed, run, diagnosed, and released in practice
  • which checked-in files and tests anchor the operational story
  • where a maintainer should look first when the package behaves differently

Reviewer Lens

  • verify that setup, workflow, and release statements still match package metadata and current commands
  • check that operational guidance still points at real diagnostics and validation paths
  • confirm that maintainer advice still works under current local and CI expectations

Honesty Boundary

This page explains how agentic-proteins is expected to be operated, but it does not replace package metadata, actual runtime behavior, or validation in a real environment. A workflow is only trustworthy if a maintainer can still repeat it from the checked-in assets named here.

Next Checks

  • move to interfaces when the operational path depends on a specific surface contract
  • move to quality when the question becomes whether the workflow is sufficiently proven
  • move back to architecture when operational complexity suggests a structural problem

Purpose

This page records the posture for performance work in agentic-proteins.

Stability

Keep it aligned with the package's actual performance-sensitive paths and validation surfaces.