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Kamal G
Kamal G

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Local-First Review Gateway for Kiro-Assisted Development

#ai #productivity #aws #python

Building a Local-First Review Gateway for Kiro-Assisted Development

AI-assisted development tools are becoming very powerful, but one question keeps coming back for serious engineering work:

How do we keep the workflow reviewable, auditable, and under human control?

Over the last few weeks, I have been working with Kiro for spec-driven software development. Kiro helps structure the development flow around requirements, design, task planning, implementation, and verification.

That structure is useful. But while using it seriously, I noticed that I wanted an additional layer around the workflow: an independent review and approval gateway before accepting outputs, moving to the next task, or preparing a commit.

That led me to build an open-source project:

Kiro External Spec Review Gateway

GitHub: https://github.com/doesit380/kiro-external-spec-review-gateway

Latest release: v1.2.0 — Local provider support and controller stabilization

This is an independent open-source project. It is not an official AWS or Kiro tool.

The problem

AI coding tools can generate a lot of useful output:

  • Requirements
  • Design notes
  • Task plans
  • Implementation steps
  • Code changes
  • Completion summaries
  • Commit recommendations

But generated output is not the same thing as accepted engineering work.

Before moving forward, a developer still needs to ask:

  • Are the requirements complete?
  • Is the design coherent?
  • Is the task plan executable?
  • Did the implementation actually complete the expected task?
  • Is the output safe to accept?
  • Is the code ready to commit?
  • Is the next task safe to start?

In a small project, these checks may happen informally.

In a more serious workflow, especially around enterprise systems, architecture decisions, regulated environments, or production software, I think these checkpoints should be explicit.

What I built

Kiro External Spec Review Gateway is a local-first CLI controller that wraps around a Kiro-assisted development workflow.

It does not replace Kiro. Instead, it acts as an external control layer.

The controller supports review checkpoints such as:

  • Requirements review
  • Design review
  • Task plan review
  • Task hardening
  • Task execution output review
  • Task completion review
  • Commit-readiness review
  • Final slice verification

The basic idea is:

Kiro output → External review → Human approval → Next Kiro action

At each checkpoint, the gateway can capture the relevant Kiro artifact or task output, send it to a configured reviewer, record the decision, and produce a copy-ready prompt that can be pasted back into Kiro.

The goal is to separate four things:

  • Generation
  • Review
  • Approval
  • Continuation

This makes the workflow easier to reason about and easier to audit.

Why local-first?

A key design goal was to make the project local-first.

Development artifacts can contain sensitive information: project context, business rules, architecture decisions, implementation details, or security-relevant notes.

So the default reviewer is a mock reviewer. That means no model call is made unless the user explicitly configures one.

The gateway supports three review modes:

  1. Mock reviewer

This is the default mode. It is useful for validating the workflow without sending content to any model.

  1. Local OpenAI-compatible provider

This supports local tools such as Ollama or LM Studio through a localhost OpenAI-compatible API.

  1. External OpenAI-compatible provider

This is opt-in and approval-gated. It is intended for cases where the user explicitly allows an external provider.

For local providers, the gateway restricts endpoints to loopback addresses such as:

  • localhost
  • 127.0.0.1
  • ::1

This helps ensure that a provider configured as local is actually local.

Safety and privacy choices

The project includes several safety-oriented choices:

  • Mock reviewer is the default
  • External provider usage is opt-in
  • Local provider usage requires explicit configuration and approval
  • Local provider endpoints are restricted to loopback hosts
  • API key values are not stored or printed
  • Request bodies are not stored
  • Runtime review state is excluded from Git
  • Provider metadata records whether content leaves the local machine

The goal is not only to call a model.

The goal is to make the review path explicit and traceable.

For example, the metadata can record whether the selected provider sends content outside the local machine. That distinction matters when working with private repositories, enterprise projects, or sensitive architecture documents.

Example workflow

A typical workflow looks like this:

  1. Kiro produces or updates a requirements document.
  2. The gateway reviews the requirements.
  3. The reviewer returns a decision such as PASS, NEEDS_CHANGES, or ERROR.
  4. The user approves or rejects the checkpoint.
  5. The gateway produces a copy-ready prompt for the next Kiro action.
  6. Kiro continues only after the review step is complete.

The same pattern can be applied to design review, task planning, implementation output, task completion, and commit readiness.

This keeps the human in control while still benefiting from AI-assisted development speed.

Current release

The current release is:

v1.2.0 — Local provider support and controller stabilization

This release includes:

  • Local OpenAI-compatible provider support
  • Local provider endpoint validation
  • Optional local provider API key support
  • Cleaner provider metadata
  • Security and privacy documentation
  • Local provider setup guide
  • Public README
  • Apache-2.0 license
  • Changelog
  • CLI stabilization fixes

The current test suite is passing with:

998 passed, 0 failed

Why this matters

AI-assisted coding is becoming more capable, but engineering control still matters.

For production-oriented teams, I think the important question is not only:

Can AI generate code?

It is also:

Can we control, review, approve, and audit the workflow around that generation?

For teams working in enterprise software delivery, cloud architecture, regulated environments, or production engineering, these capabilities become important:

  • Human-in-the-loop review
  • Approval gates
  • Audit trails
  • Local-first execution options
  • Clear provider metadata
  • Separation between AI generation and engineering acceptance
  • Repeatable workflow control

This project explores how those ideas can be added around a Kiro-assisted workflow without making the process too heavy.

What I learned

Building this project helped me think more clearly about the difference between AI generation and engineering acceptance.

AI can help produce artifacts quickly, but a reliable workflow needs more than generated output. It needs checkpoints, decisions, traceability, and clear responsibility.

For me, this project sits at the intersection of:

  • AI-assisted software engineering
  • Spec-driven development
  • Developer tooling
  • Local-first AI workflows
  • Enterprise architecture governance
  • Human-in-the-loop control

Feedback welcome

The project is still early, and I would be happy to receive feedback from other builders and developers.

GitHub repository:

https://github.com/doesit380/kiro-external-spec-review-gateway

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