How should I scope an architecture review to simplify a private Python/JavaScript harness?

[Achillesfeel]

🏷️ Discussion Type

Question

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I’m developing a private software project using Python and JavaScript, mainly on Windows. It includes document-processing/reference workflows, an audit workflow and a web interface, spread across several repositories.

AI coding tools have helped me build a substantial amount of working functionality. I also have tests, CLI tools, logs, review steps and safeguards. My concern is that the supporting harness has become too large and too specific to its original arrangement. Moving or simplifying one part can affect shared helpers, file paths, configuration, serialized records, tests and workflow steps elsewhere.

The problems I want to address are:

• Too much coupling between project-specific rules, task state, filesystem/Git operations and agent interfaces.
• Repeated context, JSON records and instructions, which make it difficult for an AI assistant to retain the current goal and acceptance criteria.
• Uncertainty about which tools, wrappers and archived components can actually be removed safely.
• Passing individual tests without enough confidence that the overall architecture is simple, maintainable and aligned with the intended product.

I want to preserve the useful functionality, outputs and safeguards while making the structure substantially lighter. I would value an experienced engineer’s perspective rather than adding another large framework or doing a wholesale rewrite.

For this discussion, I’m asking for advice on how to prepare and scope that review:

1. What small set of diagrams, dependency information, examples and test evidence should I prepare so a senior reviewer can assess the architecture without first reading every repository?
2. Would you begin with one end-to-end workflow and separate its pure decisions from its storage, CLI and agent adapters? What warning signs would make you choose a different approach?
3. How would you establish that a component is safe to remove when some dependencies are file paths, CLI calls or serialized names rather than imports?
4. What would you expect a useful initial architecture review to deliver: a dependency map, a keep/consolidate/remove list, and a staged refactoring plan with measurable acceptance tests?

The repositories are private, so I have not included source code, client material or internal reports here. I can prepare sanitized examples and diagrams for a focused technical discussion. This is a request for architectural feedback and review-scoping advice, not for someone to implement the project through this thread.

I’m seeking independent human engineering judgment.

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[Achillesfeel]

Really appreciate you taking the time to explain this. You've actually touched on one of my biggest concerns.

I've already had to rebuild this twice, so I'm trying to be extremely careful about what I start pulling apart. The last thing I want is to remove something I thought was just supporting infrastructure, only to discover half the system depends on it.

Your point about separating the core logic from the harness makes a lot of sense, but I'm interested in how you'd actually approach that in an existing system.

Would you start by mapping out all the dependencies, or would you focus on establishing tests around the current working behaviour before touching anything?

And with multiple repositories interacting, how would you decide which components to decouple first? Is there a particular way you'd identify what's genuinely essential to the architecture versus complexity that's just accumulated during development?

I'm also curious what you'd consider sufficient evidence that a component is truly independent and safe to separate.

I'm not against putting in the work to restructure things. I just want to make sure I'm doing it for the right reasons rather than creating another rebuild for myself.

Really appreciate the insight. This is exactly the kind of perspective I've been missing.

[Achillesfeel]

One thing I’d really like your thoughts on is how I’ve designed the systems to work together.

I’ve always pictured the project as a complex series of electrical relays, almost like a light switch for different tools. Each system has its own job, and the idea is that one can trigger another when it’s needed, rather than having everything running together all the time.

I’ve built a fair bit around that concept, but I don’t think I’ve fully worked out how to take advantage of what the architecture could actually do.

From your experience, does that way of thinking make sense from a software architecture perspective? Is there an established design pattern for this kind of approach?

And more importantly, how would you go about unlocking the full potential of a system built this way without making the connections between components too complicated?

This has been a passion project I’ve wanted to build for years, so I’m genuinely interested in understanding whether I’ve stumbled onto a useful architectural approach or whether there’s a better way to structure it.

[christoferpedrosanchezzuzunaga-eng]

Definitely start by locking down current working behavior with tests before touching or reorganizing a single line of code (often called "characterization testing"). If you try to map and refactor dependencies first, you're flying blind without a safety net to tell you when something silently breaks.

A practical step-by-step approach that avoids a third rebuild:

1. Write black-box tests around inputs and outputs first: Don't test the internal functions yet. Just write 3 to 5 tests that feed known files or payloads into your current CLI/pipeline and assert that the generated document, log, or exit code matches expected outputs. Once those pass, you have an automated alert for regression.
2. Decouple the leaf nodes first: Look for components that only consume data and have zero dependents (e.g. document exporters, log formatters, or notification scripts). Decoupling from the bottom up is far safer than trying to touch the core dispatcher early on.
3. The litmus test for separation: A component is truly ready to be decoupled when you can instantiate it in a fresh virtual environment or test script by passing simple primitives (strings, dicts, or paths) without it importing your project's internal settings, global variables, or adjacent modules.

If pulling something apart requires passing more than 3 or 4 shared context objects, that's usually accumulated complexity rather than essential architecture—keep those parts together until their shared state can be turned into a simple JSON or CLI argument contract.

[christoferpedrosanchezzuzunaga-eng]

That electrical relay analogy is spot-on—you haven't invented a random workaround, you've naturally arrived at Event-Driven Architecture (EDA).

The established pattern for what you're describing is the Publish-Subscribe (Pub/Sub) or Event Bus pattern:

How to keep it simple: Instead of tool A calling tool B directly (which creates a tangled web of connections), have tools emit a discrete event payload (like {"event": "document_indexed", "file": "path.json"}).
Other tools simply listen for events they care about and execute independently, without knowing who triggered them.

In Python, you can start without heavy message brokers by using a lightweight local dispatcher (like Python's built-in queue module or a simple EventEmitter class). It keeps each tool isolated, easy to test, and ready to swap out whenever needed.

[mehmetl33t]

Here is a pragmatic, senior-architect perspective on how to scope this review effectively without getting lost in repository sprawl:

1. The Minimal Review Pack

To give a reviewer high leverage without them having to read through every line across repos:

• One End-to-End Sequence/Trace Diagram: Pick your most representative pipeline (e.g. Ingest Document -> Audit -> Web Presentation). Show the data flow, boundary transitions, and serialization points.
• Component Inventory Matrix (1 Page): A simple table with columns: Component Name | Repo | Runtime (Python/Node) | Responsibilities | Inbound Calls | Outbound Dependencies.
• Interface Contracts: Provide the JSON schema or data contracts exchanged between boundaries rather than internal helper implementations.
• Golden-Path Smoke Test: A single deterministic integration test or reproducible CLI command run (with sample input and expected output) that proves the core system functions.

2. The End-to-End Workflow Slice vs. Warning Signs

Starting with one representative vertical slice (decoupling pure business logic from I/O, storage, CLI flags, and agent adapters) is generally the cleanest approach (Hexagonal / Ports-and-Adapters).

• Green Light: When logic is tangled with CLI argument parsing or disk I/O, pulling core decision logic into pure, side-effect-free functions pays off immediately.
• Warning Signs to Pivot: If the shared dependency is a distributed mutable state or tightly coupled database schema across all repositories simultaneously, fixing a single workflow in isolation can cause contract drift. In that case, stabilize the shared schema/wire contract first before refactoring individual workflow engines.

3. Handling "Invisible" Dependencies (CLI calls, file paths, serialized keys)

Static import analysis won't catch subprocess invocations or dynamic filesystem couplings. To safely establish that a component is safe to touch or deprecate:

• Centralize String Registries / Schemas: Extract raw CLI arguments, environment variable names, and file path templates into a single typed configuration schema (e.g., Pydantic model in Python, Zod schema in JS).
• Filesystem & Subprocess Auditing: Temporarily wrap subprocess.run and filesystem write helpers with structured loggers in test environments to record all dynamic calls.
• Deprecation Tombstones: If a CLI flag or serialized key might still be invoked by an external agent, add a warning logger with fallback instead of removing it abruptly.

4. What a High-Value Initial Review Should Deliver

A successful review should not result in an abstract theoretical rewrite. It should deliver:

1. Bounded Context Map: Highlighting which parts are core domain logic versus ephemeral tooling/adapters.
2. Triage Matrix: Categorizing modules into Keep, Consolidate (merge redundant wrappers), or Retire.
3. Seam Definition & Staged Migration Plan: Defining clear boundaries and ordering the refactor into thin, test-verified PR slices so the system never breaks during cleanup.

[Achillesfeel]

@christoferpedrosanchezzuzunaga-eng Dude, THANK YOU!

I've been wracking my brain for 3 weeks trying to figure out how to properly utilise the relay system I've been imagining, but everything I tried just added another ridiculous amount of layers.

To clarify the inverted triangles, I pictured each one as a stage where different tools branch out to do their jobs, then converge into a single final output. That output becomes the shared base and the input for the next stage, where another set of tools branches out and eventually produces the next output.

Almost like a series of electrical relay sections, where tools can be switched in and out without everything being directly connected to everything else.

I've got 466 tests across the project now, and tbh I'm exhausted from it.

Your explanation has genuinely given me a eureka moment. I think I've finally got a direction for simplifying what I've been trying to achieve.

Would you recommend defining and locking down those output-to-input contracts first, then gradually introducing event-driven communication where it makes sense? My biggest worry is turning the relay system into another layer of scaffolding!

Seriously appreciate your insight.

[christoferpedrosanchezzuzunaga-eng]

Looking at your EFSM map, the architecture is actually very clean—having those dedicated "Release / Handoff" stages between each pipeline is the best design choice you could make.

To your question: definitely lock down the handoff data contracts first.

If the output of one pipeline (like OCR) is just a clean, well-defined JSON file that the next pipeline (like LLM Library) reads, connecting the "relays" stays dead simple. You won't need complex scaffolding or heavy message brokers—just one stage finishing its work and passing a clean data package to the next.

Also, having 466 passing tests already is an incredible safety net. Take it one handoff at a time, you've already built the hardest part, dude!

[Achillesfeel]

Would you be okay with taking this conversation to a private chat? I'm actually considering deleting this thread, as I've probably shared a bit more than I should have publicly. Given the industry I'm working in and where I live, there's potentially some proprietary value in what I've developed, and I'm starting to think I could build a genuine business around it. I'd really appreciate continuing the conversation and getting your perspective, just without putting too much of the project out in the open. Would you be comfortable with that?

[christoferpedrosanchezzuzunaga-eng]

Completely agree, Cameron. When working with regulated IP and commercial pipelines, keeping architecture discussions private is the smart move.

I'd be glad to continue the conversation in private, dude. You can reach out directly to my email: christoferpedrosanchezzuzunaga@gmail.com, or let me know if you prefer LinkedIn.

Feel free to clean up this thread whenever you're ready!

[Achillesfeel]

( I got the help i needed from seniors )