realfast's Delivery Methodology
Superagile
Play the gameSuperagile is realfast’s delivery methodology for AI-native teams.
A Superagile team should use AI to deliver projects faster over time, while the quality of its work keeps improving.
Superagile draws on Lean and Agile/XP and adapts both to agentic development by acting on a simple principle:
The only proof of successful AI Transformation is teams that deliver incredible quality on impossible timelines.
Kaizen and Kasoku
Powered by AI-driven continuous learning.
Kaizen (改善)
Continuous incremental improvement
Each delivery should leave the product better and the system easier to change.
Kasoku (加速)
Continuous incremental acceleration
Each delivery should increase the team's ability to deliver the next useful change sooner.
Our platform makes the learning compound.
Every proven improvement becomes shared context, workflows, tools and checks for the next project. Every action and event is traced and ingested to create a context graph that tells us who did what, and why. Every project decision runs through a robust, AI assisted workflow that creates transparency and aligns key stakeholders to minimize unnecessary friction.
AI multiplies output. Superagile turns it into delivery.
More requirements. More code. More reviews, commits, CI runs and tickets. Our platform keeps that work visible and flowing. Queryable context gets new people contributing sooner. Lean, Reinertsen and XP metrics expose bottlenecks. We test improvements, prove what works and make the next project faster.
What this demands
Keep capable people accountable for requirements, design and review. Give agents explicit context and verify their work.
Deliver, verify, improve. Ship a small useful change. Verify what shipped. Fix the design or process weakness that slowed it down. Keep the context and checks so the next person or agent benefits. Measure useful work delivered, time taken and the quality of the result.
Experiment, verify, adopt. Run tight, rapid experiments with new AI capabilities. Start with a hypothesis, a baseline and a small test on real delivery work. Measure the effect on speed and quality. Verify the result, then turn proven gains into repeatable delivery practices. Keep iterating with Lean Startup's Build–Measure–Learn loop.
Try an experiment
You're managing an ERP implementation. Choose your team, decide who gets AI agents, and set aside time for refactoring.
Run the project, change one choice, and compare the result. Can you finish a month early with more than 10% of the budget left?