š From Idea to AI Agent on AWS: How to Use PRD, TRD, and OpenSpec to Build Real Projects
Turn your AI agent ideas into structured, maintainable projects using Product Requirements Documents, Technical Requirements Documents, OpenSpec, and AWS services
Introduction: Great AI Projects Start Before the First Line of Code
Have you ever had an exciting idea for an AI agent but struggled to turn it into a working application?
With tools like Claude Code and Cursor, writing code has become faster than ever.
However, as projects grow, familiar challenges begin to appear:
- AI coding assistants lose track of important requirements.
- New features introduce unexpected issues.
- Architecture decisions become inconsistent.
- Documentation falls behind implementation.
- Developers spend more time fixing problems than building features.
The challenge isn't always the AI model itself.
Often, the real problem is starting development without clearly defining what needs to be built.
In this article, I'll share a practical approach to transforming ideas into AI agent projects using three building blocks:
PRD + TRD + OpenSpec
We'll also explore how this methodology can support the development of AI agents on AWS.
š” 1. Start With an Idea, Not With Code
Imagine you want to build an AI agent that monitors AWS infrastructure costs, identifies unusual spending patterns, and automatically generates optimization recommendations.
That's a good starting point.
But several questions remain unanswered:
What exactly should the agent monitor? Which AWS services should it access? How often should it run? What actions can it perform? How will you measure success?
Instead of immediately generating code, the first step is to transform the idea into clear requirements.
This is where a PRD becomes valuable.
š 2. PRD: Turn Your Idea Into Product Requirements
A Product Requirements Document (PRD) defines what you're building, why it matters, and what success looks like.
For our AWS FinOps agent, a PRD might include:
- Objective: Detect cost anomalies and identify optimization opportunities.
- Target users: Cloud engineers, DevOps teams, and FinOps professionals.
- Core features: Cost analysis, anomaly detection, recommendations, and notifications.
- Business rules: Never modify production infrastructure without approval.
- Success criteria: Deliver accurate, actionable, and verifiable cost insights.
You can use an AI assistant to help create this document.
Example prompt
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Act as a Product Manager specializing in AI agents.
I want to build a FinOps AI agent on AWS that
monitors cloud costs, identifies anomalies,
and generates optimization recommendations.
First, ask clarifying questions about my idea.
Then create a PRD including:
- Project objectives
- Target users
- Core features
- Business rules
- MVP scope
- Acceptance criteria
Do not generate code yet.
At this stage, your idea becomes a clearly defined product concept.
šļø 3. TRD: Turn Requirements Into an AWS Architecture
Once the PRD is defined, the next question is:
How are we going to build it?
A Technical Requirements Document (TRD) translates product requirements into engineering decisions.
For our FinOps AI agent, a possible AWS architecture includes:
| AWS service | Responsibility |
|---|---|
| Amazon Bedrock | AI model inference and reasoning |
| Amazon Bedrock AgentCore | Managed agent runtime and integrations, when needed |
| AWS Lambda | Execute functions and query cost data |
| Amazon EventBridge | Schedule automated analysis |
| AWS Step Functions | Orchestrate the workflow |
| Amazon CloudWatch | Logs, metrics, and monitoring |
| AWS IAM | Permissions and access control |
Not every project needs all these services. A good TRD should justify each architectural choice based on complexity, security, scalability, and cost.
Example prompt
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Act as an AWS Solutions Architect.
Based on the FinOps AI agent PRD,
create a Technical Requirements Document.
Include:
- Proposed AWS architecture
- AWS services and technical justification
- Agent execution workflow
- External API integrations
- IAM permissions and security controls
- Logging and observability
- Estimated infrastructure costs
- Testing and deployment strategy
Prioritize simplicity, security,
and cost efficiency for the MVP.
Do not implement the code yet.
Now your AI coding assistant has both a product definition and a technical blueprint.
āļø 4. OpenSpec: Turn Specifications Into Development Tasks
This is where things get interesting.
We have a PRD describing the product and a TRD defining the architecture.
But how do we keep the AI coding assistant aligned with these documents throughout development?
One approach is to use OpenSpecĀ , an open-source tool designed for Spec-Driven Development.
OpenSpec helps organize software changes through structured proposals, specifications, technical designs, and implementation tasks.
Step 1: Initialize OpenSpec
With Node.js and npm installed:
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npm install -g @fission-ai/openspec@latest
cd my-finops-agent
openspec init
Step 2: Explore the solution
Inside a compatible AI coding assistant, such as Claude Code:
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/opsx:explore
Ask the assistant to review your PRD and TRD, identify missing requirements, and evaluate technical decisions.
Step 3: Create an implementation proposal
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/opsx:propose Build the AWS FinOps AI agent
based on PRD.md and TRD.md,
starting with the MVP.
OpenSpec can help organize artifacts such as:
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openspec/
changes/
finops-agent/
proposal.md
design.md
tasks.md
specs/
finops/
spec.md
These files document what needs to change, how it should be implemented, and which tasks need to be completed.
Step 4: Implement the solution
After reviewing and approving the proposal:
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/opsx:apply
The AI coding assistant follows the specifications and implementation tasks to build the project.
Instead of relying on one enormous prompt, development becomes a sequence of smaller, reviewable changes.
Step 5: Validate and archive
After implementation, review the code, run tests, validate security controls, and verify the AWS infrastructure.
Once the change is complete:
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/opsx:archive
This helps preserve an organized history of project changes and specifications.
Note: Available commands and their behavior may vary depending on the OpenSpec version and AI coding assistant integration.
āļø 5. Why Combine This Methodology With AWS?
Building an AI agent involves more than generating model responses.
Production applications may require external integrations, secure authentication, reliable execution, monitoring, and scalability.
AWS provides managed services that can help address these requirements.
For example:
Amazon Bedrock provides access to foundation models through managed APIs.
Amazon Bedrock AgentCore offers capabilities for running, integrating, and observing agents.
AWS Lambda enables serverless execution of application logic.
AWS Step Functions helps coordinate multi-step processes, including retries and approval workflows.
Amazon CloudWatch provides operational visibility into applications and infrastructure.
By combining these services with structured specifications, developers can make architecture decisions earlier and implement features in a more controlled way.
šÆ 6. Why This Approach Matters
The biggest benefit of PRD, TRD, and OpenSpec isn't generating more documentation.
It's reducing ambiguity.
A structured workflow provides:
- Clear requirements: Everyone understands what needs to be built.
- Better technical decisions: Architecture choices are documented and reviewable.
- More consistent AI-assisted development: Coding assistants can reference explicit specifications.
- Easier collaboration: Developers and AI agents can work from shared project context.
- Controlled evolution: New features can be introduced through smaller, testable changes.
- Improved maintainability: Decisions and requirements remain accessible as the project grows.
This approach doesn't guarantee bug-free software.
It provides a better foundation for catching mistakes, reviewing changes, and maintaining alignment between business goals and technical implementation.
š Conclusion: Stop Keeping Your AI Ideas in Your Head
We now have powerful AI tools capable of generating applications faster than ever.
But faster code generation doesn't automatically mean better software.
The real opportunity is combining AI-powered development with clear engineering practices.
Here's the workflow I recommend:
š” IDEA: Define the problem you want to solve.
š PRD: Transform the idea into product requirements.
šļø TRD: Design the technical architecture.
āļø OpenSpec: Organize specifications and implementation tasks.
š¤ AI Coding Assistant: Implement, test, and improve the solution.
āļø AWS: Run, integrate, secure, and monitor your AI agent.
You don't need to start with a complex architecture.
Start with a simple idea, define your requirements, build an MVP, and improve it incrementally.
The best way to bring your AI ideas to life isn't to start writing code immediately. It's to make those ideas clear enough for both humans and AI agents to understand and build.
š¬ How do you start your AI projects? Do you jump straight into coding, or do you define requirements and architecture first?
Share your experience in the comments!
#AWSBuilders #OpenSpec #SpecDrivenDevelopment #AIAgents #AmazonBedrock #AWS #ClaudeCode #SoftwareArchitecture
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