Originally published at orquesta.live/blog/from-prompt-to-production-inside-orquesta-process
The journey from a simple prompt to production-ready code can seem like an enigmatic process. Yet at Orquesta, we’ve demystified this sequence into a streamlined workflow that integrates AI assistance directly into your local development environment. Here’s a detailed walkthrough of how this works, from prompt creation to code deployment, all executed within your own infrastructure.
Writing a Prompt
It all starts with a prompt. Teams or individual developers write these prompts, which are essentially commands or requests for specific code functionalities. These prompts can be as simple as "Create a REST API for user authentication" or as detailed as "Implement a microservice to handle order processing with error logging and retry mechanisms."
This flexibility in prompt writing allows developers to focus on describing the desired outcome without delving into the minutiae of code syntax or architectural decisions upfront.
The Role of the AI Agent
Once a prompt is submitted, the local AI agent powered by Claude CLI springs into action. Unlike cloud-based sandboxes, Orquesta runs on your local machine, ensuring that your code never leaves your secure environment. This is crucial for maintaining data privacy and control over proprietary code.
The AI agent analyzes the prompt and, based on the execution mode chosen, determines how best to proceed. In Auto mode, the agent autonomously decides whether to use direct SSH commands, Claude CLI for code generation, or Batuta AI for more complex, autonomous operations.
Claude CLI: Code Generation
When the Claude CLI is engaged, it interprets the prompt and begins generating the necessary code. This is not an isolated process; every line of code is streamed back to the Orquesta dashboard in real-time, allowing developers to monitor progress and intervene if necessary.
Here's an example of how the Claude CLI transforms a prompt into tangible code:
# Prompt: Create a simple HTTP server in Python
import http.server
import socketserver
PORT = 8000
Handler = http.server.SimpleHTTPRequestHandler
with socketserver.TCPServer(("", PORT), Handler) as httpd:
print(f"Serving at port {PORT}")
httpd.serve_forever()
Every step of this process is transparent, giving developers confidence in what the AI is doing and how it's interpreting their prompts.
Testing and Quality Gates
Before any code is committed, Orquesta’s quality gates come into play. The AI simulates the changes and runs necessary tests to ensure that the new code meets predefined standards. This includes syntax checks, runtime tests, and adherence to any specific coding standards outlined in the project's CLAUDE.md file.
If the code passes all checks, it’s then up to the team lead or designated reviewer to sign off on the changes. This human oversight ensures that all generated code is production-ready and aligns with the team's goals.
Committing the Code
With approval granted, the AI seamlessly commits the code to the repository. This is done via real git commits, maintaining a full audit trail of changes. Developers can see exactly what was altered, providing complete transparency and accountability.
Here’s a typical commit message generated by the AI:
feat: Implement HTTP server using Python's http.server module
- Created a simple HTTP server
- Added basic request handling
Deployment
Once committed, the next step is deployment. If configured, Orquesta can autonomously handle deployments via Batuta AI, which uses a ReAct loop to execute SSH commands necessary to deploy changes to production environments.
Batuta AI’s ReAct loop functions in a cycle of thinking, acting, observing outcomes, and repeating the cycle until the deployment is successful. This automation reduces human error and speeds up the time to production.
Real-Time Monitoring and Feedback
Throughout this entire lifecycle, Orquesta’s Agent Grid provides a bird’s-eye view of all active agents. Developers can monitor each agent’s terminal output in real-time, seeing every step from code generation to deployment.
This level of monitoring not only provides confidence but also enables quick troubleshooting should anything go awry.
Conclusion
Orquesta transforms the development process, making the path from prompt to production a seamless and efficient experience. By leveraging local AI agents, real-time monitoring, and autonomous execution modes, teams can focus more on innovation and less on the mechanical aspects of coding.
This holistic approach to development orchestration ensures that we maintain control, enhance productivity, and streamline processes without sacrificing security or transparency.
