Moving fast is useful. Moving fast with structure is what makes it sustainable.

You need to have structure when you are moving fast, else it becomes chaos.

Coding agents have made coding faster, but they did not give teams the structure to move fast.

A software factory gives you that structure. It gives you a way to work with agents and review their work early in the cycle - PRD, HLD, design - before they go too far and create a mess downstream.

You can have accountable owners for each agent and each stage. They can review what agents are doing, nudge them when needed, and stay in control at every stage.

One can argue that coding is cheap now, so we can always refactor the code later. But refactoring is not just about rewriting code. Once something has moved downstream, you may have migrations, dependencies, and customers already using it.

Code can drift and can take entire different shape in few weeks if not taken care of. Its ok for vibe coded apps but if you are talking but serious scalable production code than investing 5–10% extra time early can save you days of refactoring and customer migration effort later, I think that is worth it.

reddit.com

u/Prudent-Fortune3420 — 22 hours ago

▲ 2 r/SoftwareFactory_+1 crossposts

Pillars of autonomous agentic system.

With the rise of agents and coding agents, every platform needs to be thought through from first principles - where the agent is a first-class citizen and actively does work on the platform.

We need to build platforms where humans and agents can work together. Over time, as models keep getting better, more and more work will be done by agents, while humans move toward overseeing them.

So how should we think about the journey from a platform where humans and agents work together to a world where most of the work is done by agents?

I think there are three foundational pillars for building an autonomous system.

Context

Enough has been said about context, so I don’t need to over-explain this.

But humans and agents need to operate on the same context layer. That will only happen when platforms start capturing more decisions, traces, workflows, and outcomes as part of the system itself.

Over time, this becomes the organization’s memory.

Governance

Agents need to be first-class citizens on the platform, and they need access control similar to humans.

What can they access vs. what can they not access?
Which team are they part of?
Who can delegate to them?
What tasks can be delegated to them?
What boundaries and rules do they need to follow?
What reasoning capability can they use?
Which model should they use for a given task?
How much can they spend on reasoning for that task?

As more work moves to agents, governance becomes the control plane for how agents safely operate inside the organization.

Self-Learning and Evolution

The platform needs to have a constitution and principles for how it evolves.

How and when can it add more agents for specific tasks?
How and when can it add new skills?
How can it validate that the platform is behaving correctly and completed the task correctly?
How should communication between agents happen?

For a system to learn, it should be able to verify whether something is working or not, capture new context it sees while working on a task with humans, and add it back to the context layer so that the next task does not start from zero, but with the needed context.

In each layer, we will need human oversight. But the degree of human involvement will vary based on task complexity, risk, and confidence in the system.

If we build on these three fundamental principles, we can actively move toward systems where agents do most of the work, with humans stepping in only when needed.

We don’t start with autonomous systems. We move toward them is a step in this direction where we can build software autonomously as longs we have context, governance and learning loop.

Software factory is step in that direction where agents will ship production ready software.

reddit.com

u/Prudent-Fortune3420 — 2 days ago

▲ 2 r/SoftwareFactory_+2 crossposts

Retrieval Debt Is the New Technical Debt. Software Factories Help Reduce It.

Retrieval Debt is about effective context engineering for code changes. Agents can only change code safely when the codebase is structured so the right files, contracts, tests, and decisions are easy to retrieve.

The original Retrieval Debt essay names a problem every team using coding agents runs into. Agents do not operate on your whole codebase. They operate on the context they can retrieve for one code change.

The exact quote.

>Retrieval Debt is how much context an agent must load to safely understand, change, and verify a single unit of behavior. Lower is better. Always.

That definition matters because the context window is a scarce resource. Good context engineering is not just stuffing more into the prompt. It is making the codebase easier to retrieve, understand, change, and verify for a specific task.

Context engineering starts with code structure.

If behavior is scattered across services, names are vague, contracts are hidden, and tests live far away from the thing they protect, agents burn context just finding the shape of the problem. That is Retrieval Debt.

Effective context engineering starts by structuring the code in the right way. Clear domain names, strong module boundaries, single ownership for business rules, explicit API and data contracts, local tests near the behavior, and decision records connected to the code they explain.

The cleaner the structure, the less context an agent needs to load to make a safe change. The agent can find the right place, understand why it exists, edit the smallest surface, and verify the behavior without guessing.

Then break the work into focused tasks.

Even with good code structure, large requests should not be handled as one giant agent pass. A high-quality code change needs the work broken down so every agent gets the context window for one bounded job.

That means turning a request into the right sequence. Understand the existing structure, design the change, define contracts and acceptance criteria, implement a focused task, verify it, then learn from the result. This is how agents make the most of the context window instead of carrying the whole codebase in every run.

The software factory workflow.

The right operating model is not one agent doing HLD, LLD, implementation, testing, and review all at once. A software factory gives you a team of agents, each using the right context for the right part of the code change.

Remember the shared engineering brain brings in repo structure, architecture decisions, prior fixes, ownership, tests, and verification history.
Plan the Architect agent turns the request into high-level design with boundaries, affected modules, tradeoffs, risks, and rollout shape.
Coordinate and build the Lead agent turns the design into low-level tasks while coding agents implement bounded changes with focused context windows.
Verify QA and Code Review agents validate behavior, review the diff, check tests and evidence, and prepare the change for release.
Learn decisions, evidence, failures, and fixes update shared context so future agents retrieve better context next time.

That split keeps every agent focused. The Architect agent reasons about structure. The Lead agent shapes executable tasks. Coding agents spend their context windows on the right implementation surface. Review and QA agents challenge the change before it ships.

Where a software factory helps.

A software factory is the layer that makes effective context engineering repeatable. It gives your team a coordinated group of agents that can understand code structure, plan the change, build it, review it, and verify it using shared engineering context instead of starting from scratch every time.

It also gives teams a way to ship with quality and velocity. Structure the task, route each step to the right agent, preserve shared memory, validate changes in a sandbox, and package evidence before release.

This is how teams make coding agents useful in real production systems. Prinevo is built around that idea. A software factory where agents work on your engineering brain, make focused code changes, show proof before anything ships, and get better with every run.

The bottom line.

Retrieval debt is not only an indexing problem. It is a context engineering and code structure problem.

For large codebases, the answer is not a bigger prompt and hope. The answer is code structured for retrieval, tasks structured for focused agent work, and a software factory that routes the right context to the right agent at the right time.

The teams that build this layer will make agents cheaper to run, easier to trust, and better at autonomously delivering production-ready changes with both quality and velocity.

u/Prudent-Fortune3420 — 4 days ago

▲ 1 r/Development

👋 Welcome to r/SoftwareFactory_ - Introduce Yourself and Read First!

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u/Prudent-Fortune3420 — 5 days ago