Building Software with AI — Beyond Code Generation: A Walkthrough of Agiloop

Matthew Chartier — Fri, 19 Jun 2026 14:54:03 +0000

Over the past few years, AI has become remarkably good at writing code. Give a modern coding assistant a well-defined task and it can often produce working software in minutes. Yet after decades of building software, I’ve found that writing code is rarely the biggest challenge. The harder part is transforming a rough idea into clear requirements, deciding what to build first, validating that it solves a real problem, and then learning from the results once it’s in the hands of users.

While AI has dramatically accelerated implementation, much of the product development lifecycle still relies on a collection of meetings, documents, spreadsheets, backlog grooming sessions, and educated guesses. Teams spend significant time trying to answer questions like: What should we build? How should it work? Did we build the right thing? What should we do next?

A couple years ago, my team and I started asking a different question: What if AI could help with the entire journey—not just writing code, but also discovering requirements, generating specifications, measuring outcomes, and continuously improving the product over time?

That question eventually led us to build Agiloop.

As a co-founder of Agiloop, I’m obviously not a neutral observer. This article isn’t intended to be an independent review or a product pitch. Instead, I want to share the workflow we’ve been building, explain the problem we’re trying to solve, and walk through a practical example of how an idea can move from a simple concept to running software using AI assistance throughout the entire lifecycle.

The Idea

Let's imagine you're a consultant, entrepreneur, or developer who has identified a recurring problem.

In this example, we'll use a simple concept: a web application that helps small businesses collect and organize customer feedback. Users should be able to submit feedback, categorize it, vote on existing suggestions, and allow administrators to review trends over time. Let’s call it FeedbackHub.

It's not a groundbreaking idea. In fact, that's the point.

Most software projects don't begin with a 50-page requirements document. They start with a conversation, a frustration, an observation, or a note scribbled on a whiteboard. The challenge isn't having the idea. The challenge is transforming that idea into something clear enough to build and iterate upon.

Traditionally, this process involves a series of meetings, requirement gathering sessions, architecture discussions, backlog creation exercises, and project planning activities. Depending on the organization, this can take days or weeks before a single line of code is written.

This is where the Agiloop workflow begins.

Step 1: INVENT — Turning Ideas into Specifications

Whether you’re creating a new application or enhancing an existing one, Agiloop begins with a conversation. Rather than requiring users to complete lengthy forms or manually create user stories, Agiloop conducts an interactive interview designed to uncover goals, requirements, workflows, and constraints. Acting as both a business analyst and systems architect, the AI guides the discussion with questions designed to uncover goals, users, workflows, constraints, technical requirements, and the criteria that will ultimately define success.

As the conversation progresses, Agiloop builds a structured understanding of the project. The result is more than a transcript. Agiloop generates a prioritized feature backlog, detailed functional and technical specifications, user stories, acceptance criteria, dependency analysis, implementation estimates, and recommended feature groupings. Instead of starting development with a vague concept, teams begin with a shared understanding of what needs to be built and why.

For organizations already using existing planning tools, these artifacts can be pushed directly into systems such as Jira, Azure DevOps, Trello, or even exported for further review.

The goal is not to eliminate human decision making. The goal is to dramatically reduce the time required to move from an idea to a build-ready plan. At this point, we have not generated any code. What we have generated is clarity. And clarity is often the most valuable output of the entire process.

Step 2: IMPLEMENT — Turning Specifications into Software

Once the requirements are defined and approved, the next challenge is execution.

This is where most AI development tools begin. You provide a specification, a prompt, or a task, and the system generates code. Agiloop takes a somewhat different approach.

Rather than treating code generation as a single event, IMPLEMENT treats software delivery as a managed process. Features move through a lifecycle that includes generation, verification, review, and deployment.

When a feature is selected for implementation, Agiloop uses the specifications, user stories, acceptance criteria, architectural context, and project settings generated during INVENT to create a build plan. AI agents then generate the required code, tests, documentation, configuration, and supporting artifacts. But generating code is only part of the job.

IMPLEMENT also performs automated verification to ensure the generated software actually builds and runs. Depending on the project's configuration, additional validation steps can be performed before changes are proposed for review.

The generated changes are delivered through familiar source control workflows using pull requests or merge requests, allowing development teams to maintain the review and approval processes they already trust. Code is delivered to your repository.

For teams that want human oversight, reviews remain firmly in the loop. For teams that want maximum automation, Agiloop can continue through deployment after approval.

The important distinction is that the goal isn't simply to generate code. The goal is to deliver working software.

Step 3: INSPECT — Measuring Whether It Actually Works

Traditionally, software teams celebrate when a feature reaches production. Unfortunately, users rarely care that a feature was completed. What matters is whether it delivers value.

Did customers use it?
Did it improve adoption?
Did it reduce support requests?
Did it increase engagement?
Did it achieve the business outcome that justified building it in the first place?

These questions are often surprisingly difficult to answer.

Many organizations rely on a mixture of analytics platforms, dashboards, spreadsheets, and intuition to determine whether a feature succeeded. INSPECT was created to make those answers easier to find.

As applications generate activity, Agiloop collects operational and usage metrics that can be viewed at both the project and feature level. Teams can define goals in plain language and monitor progress against those objectives over time.

For example, with our FeedbackHub app, a team might define a goal such as:

Increase adoption of customer feedback voting to 75% of active users within three months.

Once the goal is established, INSPECT continuously measures actual usage and reports progress. Instead of simply knowing that a feature was deployed, teams gain visibility into whether the feature is producing the desired outcome.

This creates an important shift. Success is no longer measured by output. Success is measured by impact.

Step 4: ITERATE — Closing the Loop

The most successful products are rarely built correctly on the first attempt—they evolve

Teams observe user behavior, learn from real-world usage, refine assumptions, and continuously improve the product over time. This feedback cycle is where many development processes begin to break down. Data exists in one system; feature requests exist in another; roadmaps live somewhere else. Insights often remain trapped inside meetings and conversations. ITERATE is designed to connect those pieces.

By analyzing application performance, feature adoption, usage patterns, and project goals, Agiloop can recommend enhancements, refinements, and entirely new features for consideration. These recommendations flow directly back into the backlog where they can be reviewed, prioritized, and sent through the same INVENT and IMPLEMENT process. The result is a continuous cycle:

Idea → Specification → Implementation → Measurement → Improvement

Instead of treating software development as a series of disconnected projects, Agiloop treats it as an ongoing learning process. And because every phase shares the same context, each iteration becomes more informed than the last.

Final Thoughts

The software industry is in the middle of a significant shift.

Over the past few years, we've watched AI dramatically accelerate software implementation. Tasks that once required days of effort can now be completed in hours or even minutes. Yet many of the challenges that slow teams down still exist long before the first line of code is written—and long after the code is deployed.

Requirements remain unclear. Priorities change. Features are released without measurable goals. Valuable insights become disconnected from future planning.

What excites me most about AI isn't its ability to write code. It's its ability to help connect the entire product lifecycle. That's the idea behind Agiloop.

Rather than focusing exclusively on implementation, Agiloop was designed to help teams move from idea to specification, from specification to working software, from software to measurable outcomes, and from outcomes to continuous improvement.

Whether you're an entrepreneur validating a new concept, a consultant helping clients define requirements, a startup trying to move faster, or an established development team looking to streamline delivery, the goal is the same: spend less time managing process and more time creating value.

If you'd like to explore the platform yourself, Agiloop is free to use throughout the INVENT, INSPECT, and ITERATE phases. You can interview with the AI business analyst, generate specifications, create feature backlogs, estimate work, define goals, and explore recommendations without charge.

The IMPLEMENT phase—the actual software generation process—is usage-based and priced according to feature complexity. Agiloop calculates a complexity score (story points) for each feature and consumes credits as work is performed. This provides transparent, upfront pricing, allowing you to understand the cost of implementing a feature before any code is generated.

Organizations can choose to use Agiloop’s managed AI infrastructure for code generation or provide their own Anthropic API key. This flexibility allows teams to balance convenience, cost, and existing AI investments while taking advantage of the same implementation, verification, and delivery workflow.

To help new users explore the platform, every new user account receives 50 free credits upon signup.

Whether you decide to try Agiloop or simply borrow some of the ideas discussed in this article, I believe the future of software development extends beyond code generation alone. The real opportunity lies in connecting discovery, implementation, measurement, and improvement into a continuous, AI-assisted workflow.

We're still early in that journey, but it's an exciting time to be building software.
Try Agiloop: https://agiloop.ai

Matt Chartier is a full-stack AI architect, software entrepreneur, and co-founder of Agiloop. Over a 40-year career, he has built and scaled platforms serving millions of users, led engineering teams through acquisition, and helped organizations transform ideas into production-ready software. His work focuses on applying AI across the entire software development lifecycle.