AI assistants now write a large share of the code in many teams. Yet delivery often doesn’t get faster. In Google’s DORA 2024 research, as AI adoption grew, individual productivity went up, while software delivery stability fell by an estimated 7.2% and throughput by 1.5%. Writing code was never the main bottleneck. Knowing what to build is.

That is why I created Rondo, a team methodology for building software with AI. It works together with Context Driven Development (CDD), a spec-driven way of working with AI assistants.

The problem: knowledge, not code

Requirements and communication break projects far more often than code does. In the NaPiRE study of 228 companies in 10 countries, the most common critical problems were incomplete or hidden requirements (48%) and communication flaws with the customer (41%). AI assistants don’t fix that. Without context, they simply guess faster. (What doesn’t work in classic methodologies)

Rondo: rotating specification sessions

Rondo organizes analysis, knowledge and implementation around specification sessions. People with business, functional and technical knowledge meet, agree on the goal, scope and rules of a change, and write them down as a specification.

Four dynamic roles (owner, functional context expert, technical expert and CDD operator) move between teams as the sessions require, instead of sitting in fixed teams. While one session prepares the next area, CDD operators implement the specifications that are already closed, with AI assistants. We call this the phase shift. (How Rondo works)

CDD: context an AI can actually use

CDD defines four lasting artifacts:

• specs/: what we intend to build, with its goal, scope, rules and constraints
• CHANGELOG: what was actually built
• tests: proof that it works
• README: the current state of the system

Every specification has the same nine sections, from TL;DR to Control Gates and Expected Outcome. The goal is reproducibility: a different modern AI assistant should be able to rebuild the software from these artifacts alone.

Structure, not volume

Research supports giving AI precise, structured context rather than simply more of it:

• On SWE-bench (ICLR 2024), a model resolved 4.8% of real GitHub issues when given exactly the files the change touched, versus 1.96% with automatically retrieved context.
• Lost in the Middle (TACL 2024) showed that accuracy drops by more than 20% when key information sits in the middle of a long context.
• A 2026 ETH Zürich study found that generic repository overviews for coding agents don’t improve results but raise cost by over 20%, while specific instructions are followed well.

More in research in the context of CDD and research in the context of Rondo.

Is Rondo right for your team?

Ask four questions:

1. Do many teams need the same experts?
2. Do people work on several projects at once?
3. Does AI already write a large share of your code?
4. Is knowledge of the system held in the heads of a few people?

If you answer “yes” to at least two, Rondo and CDD are worth a closer look. (Is Rondo right for you?)

Rondo doesn’t require abandoning Scrum or Kanban. It changes how knowledge flows and how teams are composed. From our pilot: the hardest part wasn’t the tooling, but switching from traditional methods to high-velocity, AI-assisted software delivery.

Where to start

• Leaders: the 7-minute path for leaders: why it pays off, how it works and how it compares with other methodologies.
• Practitioners: the cookbook covering CDD artifacts, the specification structure, Rondo roles and rituals, and a step-by-step adoption plan.

If you’d like help adopting Rondo and CDD in your organization, get in touch.