Over the past year, we developed and refined a proprietary Agentic Engineering Workflow - a structured methodology for AI-assisted software delivery. To see what the technology can really do, we put it to a rigorous test. The results: 76% faster delivery, with no manual code written.
The Background
Most engineering teams today are using AI tools. And most of them are seeing modest gains - faster autocomplete, less time on boilerplate. Useful, but not the massive change the industry has been talking about.
Adding an AI tool to an existing workflow doesn’t change the workflow. You get a faster version of the same process, not a better one. We think the real opportunity is in rethinking how software gets built from the ground up. So we built a methodology around that idea, and tested it properly.
The Dreamix Management System (DMS) is a production enterprise application we have maintained and developed for six years. The system has over 2,000 code commits from more than 50 contributors, and runs on a standard enterprise stack: PostgreSQL, Java backend with Spring Boot and Angular frontend.
To test the Dreamix Agentic Engineering Workflow against a credible baseline, we ran a structured internal experiment: two engineers independently implementing the same mid-to-large feature across all application layers using two different approaches. One would use commercial state of the art AI tooling, and the other would use the Dreamix agentic development workflow. There was one main rule: no manual code writing.
The Workflow
The workflow addresses the specific failure points of ad hoc AI-assisted development - poor prompting, inadequate context, and unstructured execution. It does so through three pillars: purpose-built prompt engineering, curated context management, and multi-phase workflow orchestration with human review gates.
The key design principle is role assignment. At each phase, the agent is assigned a specific role. Left unconstrained, AI agents may conflate stages and drift from their tasks - but an agent with an assigned role calibrates its behaviour to match what that stage of development requires. Each role had specific task execution guidelines based on our 20 years of experience building successful software products.
The methodology itself is tool-agnostic - applicable with Claude Code, Cursor, Windsurf, Copilot, or open-source alternatives for clients with stricter data governance requirements.
The Results
Both approaches delivered the complete feature to production. The same feature, estimated at 12.5 developer-days using traditional methods, was completed in approximately 3 days by using the Dreamix agentic engineering workflow.
Bain's Technology Report 2025 places realistic AI productivity gains at 10 to 15% for basic code assistants and 25 to 30% for teams that pair AI with end-to-end process transformation. We achieved more than that.
| 76% Reduction in development time 12.5 days to 3 days | Key findings: Feature delivered to production in both approachesNo manual code written by either engineerDreamix Workflow achieved ~85% of commercial tool quality at lower cost |
Even a conservative 30% gain, as most industry benchmarks project, would justify full adoption. We delivered more than that. If you’re interested in embedding agentic engineering in your organisation, we’re here to help. Let us know and let’s unlock the benefits of agentic AI together.
