Stable Evolution: Controlling Drift in AI-Assisted Programming
AI-assisted programming works. Anyone who has used modern coding agents knows the feeling: velocity jumps and ideas turn into running systems faster than ever before.
But after the first few weeks of use, a deeper truth emerges:
AI-assisted programming today optimizes for speed of localized progress, not for global and end-to-end correctness, repeatability, or long-term system health.
Some of these issues may improve over time. But what is fundamentally missing is a revised model for code development and deployment in an AI-assisted setting. As a result, a few observations recur consistently once AI-generated codebases grow beyond toy scale.
Implicitly, the dynamics described here mirror the original motivation behind test-driven development (TDD). TDD emerged as a response to systems that “worked” locally but failed under extension, reuse, or time. In an AI-assisted setting, this problem is amplified: code is produced rapidly, but its assumptions, boundaries, and failure modes remain unarticulated. By framing correctness as something that must be challenged before and during generation, rather than verified afterward, TDD offers a conceptual anchor for stabilizing AI-driven velocity. It converts vague intent into executable, repeatable constraints, counteracting the fragility that arises when correctness is inferred rather than specified.
1. Specification (in)completeness
AI coding agents thrive in the presence of ambiguity. When a spec has gaps, the model fills them. When requirements are underspecified, it makes assumptions. When tradeoffs are unclear, it picks one and moves on. The problem is not that the underlying model is “wrong” in its generation. The problem is that those assumptions are implicit. By the time the system “works,” you often don’t know which constraints were assumed, which cases were ignored, and which design decisions were provisional vs intentional The result is a codebase that runs, but whose specification exists only as a diffuse memory across 20+ conversational turns. Getting the spec right in the first go is hard. But letting the model silently invent one is worse.
2. Conversational evolution is the new reality, but not repeatable
AI-assisted programming is inherently evolutionary. You explore, refine, backtrack, and adjust.
But today’s evolution has a fatal drawback: it cannot be replayed. The same prompt sequence will not produce the same result. Small wording changes lead to divergent architectures. Model stochasticity makes reproducibility hard. And, context window limits erase early rationale. What we end up with is a system whose final state exists, but whose path does not. This breaks debugging, auditing, onboarding and trust. Human engineers rely on history: commits, diffs, design docs and rationale to make systems understandable over time.
3. Human as the adversarial tester
AI agents do not adversarially test their own assumptions. They do not naturally ask:
- “what breaks if this is extended?”
- “what if inputs are malicious?”
- “what if this is reused in six months?”
- “what if the abstraction boundary is wrong?”
Anything not made explicit becomes best-effort. This means the burden shifts entirely to the programmer: to construct hard test cases, to probe edge conditions, to challenge architectural shortcuts, and to stress assumptions the model never surfaced. AI-assisted programming only works safely when the human adopts an adversarial stance toward the output. This adversarial role assigned to the human closely embodies the core principle of test-driven development.
In TDD, the developer deliberately acts as an antagonist to the code—constructing failing tests, edge cases, and misuse scenarios before trusting any implementation. Similarly, because AI agents do not naturally surface or stress their own assumptions, the human must externalize skepticism through concrete tests that encode “what must not break.” Tests become the formal mechanism by which the human adversary pressures the system, forcing hidden assumptions into explicit, verifiable contracts. In this sense, adversarial human involvement is not merely a mindset but the practical re-instantiation of TDD as a control structure for AI-assisted programming
4. Context fatigue and false completion
After enough conversational turns (>20), an undesirable behavior emerges. The coding agent starts arguing that limitations are acceptable, suggests documenting gaps instead of fixing them, encourages “declaring victory”, and resists deep refactors. This is a structural problem with LLM based coding. Large conversational histories overwhelm both the model and the human. Earlier decisions fade, contradictions accumulate, and the easiest path forward becomes acceptance rather than correction.
5. Refactoring is not first-class
Code refactoring is a common and healthy practice, and assumes a stable representation of intent, a compact summary of design decisions, and clear invariants that must be preserved. Conversational coding agents have none of these. As a result, the code often degenerates into a combination of partial piece-wise representations, accidental rewrites, subtle regressions in previously tested code paths and loss of previously reviewed behavior. Without a persistent, formalized representation of “what this system is supposed to be,” refactoring degrades into laborious conversational evolution. That does not scale to large and complex projects.
6. Documentation mess
AI coding agents generate explanations constantly involving inline reasoning, design summaries, rationale paragraphs and ad-hoc docs. Most of this documentation is locally useful and temporally scoped, and hence quickly outdated. Hence, as the system evolves, docs drift out of sync, redundant explanations multiply, fixing one doc rewrites others, and previously reviewed content gets silently altered. Documentation becomes unstable, and not authoritative. It finally leads to a new type of garbage to be collected.
7. Persisted reasoning and learning traces
Finally, a key attribute missing in coding agents to date is “knowing” why we did what we did, such as explicit assumption tracking, persistent decision logs and contextual retrieval of most relevant parts, replayable and searchable evolution paths, human-in-the-loop learning traces, and a clean separation between generation and justification. Without these, AI-assisted programming remains fast and impressive, but fragile. With them, it becomes auditable, governable, refactorable, and trustworthy at scale, leading to the much needed stable evolution. And clean (re)documentation follows.
A Practical Approach: Consistency Barriers and Checkpoints
One way to limit conversational drift is to introduce explicit consistency barriers: periodic synchronization points where code, tests, and documentation are deliberately reconciled into a single, authoritative state. At such a barrier, the system must satisfy an explicit convergence condition: the code reflects the current specification, the test suite encodes the intended behavior, and the documentation describes the system as it actually exists—not as it once did or might become.
These barriers are analogous to checkpoints and synchronization barriers in distributed shared-memory systems. Between barriers, evolution may remain exploratory, stochastic, and conversational. At the barrier, however, ambiguity is intentionally collapsed, assumptions are surfaced, and inconsistencies are resolved. After crossing such a checkpoint, the system can be treated as if it were Markovian: future evolution is expected to depend only on the current synchronized state, not on the unbounded conversational history that produced it.
This shift matters because it localizes nondeterminism rather than attempting to eliminate it. By deliberately “forgetting” conversational history at well-defined points, while preserving intent in executable tests and specifications, we regain replayability, auditability, and refactorability without sacrificing velocity. AI-assisted programming stops being an ever-growing dialogue and instead becomes a sequence of controlled state transitions between verifiable system states.
In effect, consistency barriers function as a new primitive for AI-assisted development, defining when non-determinism must be brought back under control.
Summary and Next Steps
AI-assisted programming is a great step, but it is incomplete and not scalable to Enterprise needs till we treat:
- Specifications as first-class objects
- Evolution as something to be recorded, not forgotten
- Human-driven adversarial testing as mandatory
- Coding and documentation as a continuous refactoring process
- Reasoning traces as foundational artifacts
- Frequent checkpoints and consistent states as first-class citizens
More broadly, perhaps, it is just the beginning of an impending transformation of the theory and practice of software development for the AI era.
More From the Journal
Stable Evolution: Controlling Drift in AI-Assisted Programming
The Validation Imperative in Agentic Science
