How to Design a Maintainable Codebase for AI Coding Agents

Why codebase design matters for AI coding agents

A maintainable codebase for AI coding agents is one that lets an unfamiliar agent find the right context, make a narrow change, and verify that change without loading the whole system into working memory. As AI writes much larger volumes of code much faster, defects can now propagate through a codebase at a pace that used to require an entire team. The core problem is not that coding agents are incapable; it is that they need legible structure, reliable constraints, and feedback loops that let them verify their own work. [6] [7] [16] [20]

That shifts the engineering problem. Instead of asking only whether humans can maintain the system, we also have to ask whether an unfamiliar agent can explore it, form a correct local model, make a narrow change, and validate that change without disturbing unrelated parts of the application. OpenAI describes this as increasing “application legibility,” while Anthropic emphasizes progressive disclosure: the agent should discover relevant context incrementally rather than drown in a giant undifferentiated prompt. [6] [7]

“Give Codex a map, not a 1,000-page instruction manual.” — OpenAI

In short, a maintainable codebase for AI coding agents starts with structure: changes should stay local, side effects should remain contained, boundaries should be explicit, and the codebase should stay simple enough that an agent can build a correct local model without exploring the whole system. This article focuses on those structural properties. The next article covers the supporting systems around that structure: documentation, executable guardrails, and verification loops.

The approaches below are not the only way to achieve a maintainable codebase. The point is to compare design choices by the way they affect a few underlying characteristics that matter for both humans and agents.

What makes a codebase maintainable for AI coding agents?

"Everything in software architecture is a trade-off. [...] If an architect thinks they have discovered something that isn’t a trade-off, more likely they just haven’t yet identified the trade-off."

DevIQ - Laws of Software Architecture

There is no architecture without trade-offs, and those trade-offs are not purely technical. Different teams will tolerate different levels of duplication, indirection, centralization, or coordination overhead. The useful question is not “what style is right?” but “how does a given style improve or degrade the following properties?”

Characteristic	Why it matters for AI coding agents	What goes wrong when it is weak
Locality	Lets an agent make a change without loading half the system into context	Features become scattered across modules, helpers, and side effects
Blast radius	Limits unintended consequences of a change	Small edits trigger broad regressions or risky refactors
Boundary integrity	Makes contracts and responsibilities legible	Agents infer the wrong contract from local evidence
Navigability	Helps an unfamiliar agent find the right files and entry points quickly	Agents waste context budget exploring irrelevant parts of the repo
Rebuild/test scope	Enables narrow verification loops and fast self-correction	Every change requires broad builds or slow end-to-end validation

A quick diagnostic is simple: can a new coding agent find the relevant behavior, change one bounded area, and run the smallest meaningful verification loop without guessing? If not, the codebase is asking for more hidden context than an agent can reliably hold.

Locality

Locality describes how many places need to be touched to complete a concrete task. In a healthy codebase, most changes stay narrow and do not require edits across unrelated modules. Parnas’s classic formulation is still the right mental model: modules should be organized around decisions likely to change, and those decisions should be hidden from the rest of the system. [1]

“Each module is then designed to hide such a decision from the others.” — Parnas

That matters even more for agentic engineering, AI-assisted software engineering, and AI coding agents. If a feature is scattered across shared helpers, orchestration layers, utility packages, and cross-cutting side effects, the agent must keep more of the system in context and is more likely to miss one dependency or “fix” the wrong abstraction. By contrast, when code is aligned with cohesive business capabilities, many single-service or single-slice changes can remain local to that area. [4] [5] [6]

A practical consequence is that locality is not just about folder layout. It depends on whether the ownership boundary, the semantic boundary, and the build-and-test boundary line up well enough that the smallest sensible unit of work is also the smallest sensible unit of change. [3] [5]

Blast radius

Blast radius is the size of the unintended side effects of a change. A small blast radius means a change remains local; a large one makes refactorings and fast iteration much riskier than they should be. Protected Variation is a useful lens here: likely points of change should sit behind stable interfaces so that clients are not forced to move with every implementation decision. [2]

“Identify points of predicted variation and create a stable interface around them.” — Larman

For coding agents, blast radius is partly an architectural issue and partly a workflow issue. The architecture determines how far a change can leak; the harness determines how much the agent is allowed to touch before it proves safety. OpenAI’s agent-first repo uses mechanically enforced dependency rules, while recent agent-harness work recommends filtered tools, isolated worktrees, and explicit progress checkpoints so the system can recover from bad turns instead of amplifying them. [6] [8] [19]

This is also why migration patterns such as branch by abstraction or legacy displacement matter in an agentic setting: they create seams that allow large changes to proceed gradually instead of forcing a broad, one-shot rewrite. [14] [15]

Boundary integrity

Boundary integrity is about how clear the borders are between modules, components, or services. Good boundaries rely on explicit interfaces and clearly separated responsibilities, while heavy coupling usually signals structural weakness. Fowler’s bounded-context framing is especially useful here: large systems rarely support one perfectly unified model, so the healthier move is to define local models and the translations between them explicitly. [3]

“Strict boundaries and predictable structure.” — OpenAI

That principle becomes more important with agents because agents reason from partial observations. If one module reaches directly into another team’s tables, state, or side effects, the agent may infer the wrong contract from local evidence. Mechanically enforced layer rules, explicit provider interfaces, and dependency-direction checks reduce that ambiguity and make the architecture legible in a way prompts alone cannot. [6] [20]

Boundary integrity also improves failure handling. When responsibilities are explicit, verification can target the contract at the boundary rather than revalidating an entire downstream chain of accidental couplings. [2] [4]

Navigability

Navigability is the speed with which a new person can orient themselves in the codebase. This is especially important for AI coding agents, because they can only inspect a small part of the system at a time. Anthropic describes the ideal as progressive disclosure: the agent should be able to retrieve just enough context for the next decision, then refine its mental model as exploration continues. [7]

“Agents can assemble understanding layer by layer.” — Anthropic

That makes searchability and repository-local knowledge first-class engineering concerns. OpenAI argues for treating a short AGENTS.md as a table of contents rather than an encyclopedia, while Google’s engineering guidance stresses both code search and documentation designed to help unfamiliar contributors get something real running quickly. [6] [9] [11]

Recent work on code agents also suggests that architecture understanding itself is an active-exploration problem. Agents build beliefs from partial observations, revise them as the environment changes, and then exploit those beliefs to perform downstream tasks. In practice, that means good navigability is less about clever naming alone and more about giving the repo enough maps, conventions, and discoverable entry points that those beliefs converge quickly and correctly. [18] [20]

Rebuild/test scope

Rebuild and test scope determine how narrowly a change can be verified. The better the structure, the more often only the affected parts need to be built or tested, which keeps feedback fast and iteration practical. Google’s testing guidance is blunt: larger system-scale tests were often slower, less reliable, and harder to debug than smaller tests. [10]

“Slower, less reliable, and more difficult to debug than smaller tests.” — Software Engineering at Google

For agentic engineering and AI coding agents, narrow verification is not a convenience; it is the difference between an agent being able to self-correct within its loop and needing a human to arbitrate every change. Fowler’s recommendation to shift validation left applies directly here: agents produce better code when they can gauge quality themselves. [16] [17]

That requires both architectural seams and tooling seams. Hermetic builds make local validation trustworthy by isolating the build from the host machine; test-impact analysis narrows execution to the subset of tests relevant to the change; and evaluation loops let teams check outputs against unit tests, latency targets, or style rules without broad manual review. [12] [13] [17]

How do you design a codebase that AI coding agents can work in safely?

The following approaches are useful not because they are fashionable, but because they improve one or more of the characteristics above. Each one also carries real costs, so the goal is not ideological purity but better trade-offs.

Domain-oriented modules with vertical slices

Domain-oriented top-level modules mean the first thing you see in src/ reflects business capabilities, not technical layers. The tree should "scream" the domain (billing, identity, catalog, publishing) rather than framework concerns (controllers, services, utils). This is close to Screaming Architecture: the structure tells you what the system does before it tells you which tools it uses. Technical concerns still exist, but they live inside each domain boundary instead of becoming the global organizing principle.

Vertical slices are easy to misunderstand. In the narrow sense used by Jimmy Bogard, a slice is not just a business noun like user or billing. It is a single request or use case: changePassword, getUserProfile, refundPayment, importCustomers. The slice is the unit of change, validation, and verification. [22]

For AI-heavy codebases, the most robust default is usually a hybrid: first-level folders by domain or business capability, then vertical slices inside each domain. For example:

src/
  user/
    changePassword/
      command.ts
      validator.ts
      handler.ts
      test.ts
    viewUserDetails/
      query.ts
      handler.ts
      test.ts
  billing/
    refundPayment/
      command.ts
      handler.ts
      test.ts

That structure keeps the top-level map readable while still giving each concrete change a small working set. The domain folder answers “who owns this concept?”, while the slice answers “what behavior is changing?”

That hybrid tends to score well on the five characteristics above:

Locality: the domain narrows where to look, and the slice narrows what to change.
Blast radius: shared rules stay in the domain boundary instead of being copied across unrelated features.
Boundary integrity: folder names tell you whether you are looking at an owned concept or a specific behavior.
Navigability: a new person can first pick the business area, then the use case.
Rebuild/test scope: each slice usually maps to a small test cluster or request path.

The trade-offs are still real. Shared technical concerns can become harder to standardize, and features that cut across multiple slices still need coordination. But for AI-assisted development, the domain-plus-slice hybrid is usually safer than either a flat slice list or recursive domain nesting alone. [5] [23]

Coupling and cohesion

Coupling and cohesion describe whether code that belongs together actually lives together, and whether unrelated things are kept apart. High cohesion means a module has a clear purpose; low coupling means that purpose does not depend on hidden knowledge spread across the rest of the codebase. Parnas’s and Larman’s arguments point in the same direction: hide volatile design decisions and protect clients from expected variation. [1] [2]

In an agentic repo, this has a second-order effect on context quality. Cohesive modules make retrieval cleaner because the files an agent opens are more likely to belong to the same concern; loosely coupled modules make the agent’s inferred contract more stable because fewer unrelated files silently participate in the behavior. [6] [7]

The most dangerous forms of coupling are the ones that defeat local reasoning. A file may look self-contained, yet its behavior depends on conventions, side effects, or assumptions defined elsewhere. In practice, the worst offenders are not ordinary call relationships but hidden forms of dependency that make changes propagate unpredictably. [25] [28] [30]

Global-state coupling is one of the most severe forms. A module reads or mutates shared process-wide state—configuration registries, caches, feature flags, singletons, ambient context, mutable globals—and therefore depends on facts that do not appear in its interface. The result is that behavior cannot be understood from the module and its direct inputs alone. This is damaging in any codebase, but especially in an agentic one: retrieval may surface the file that performs the work without surfacing the file that quietly sets the state it relies on. [24] [25] [19] [20]

Temporal coupling is similarly corrosive. Here, correctness depends on operations occurring in the right order: initialize this before calling that, call begin before commit, populate a cache before reading from it, attach middleware before issuing requests. The dependency is real, but it is encoded in sequence rather than in types or explicit contracts. Temporal coupling often survives code review because each individual step looks valid in isolation. Failures appear only when a caller omits or reorders one of the hidden preconditions. [26]

Control coupling becomes severe when one module passes flags, modes, or discriminator values that instruct another module which branch of behavior to execute. At small scale this seems harmless; at larger scale it means the caller must know too much about the callee’s internal decision structure. A boolean like is_preview or skip_validation, or a mode string like "fast" versus "safe", often signals that one abstraction is doing several jobs at once. The interface stops describing a capability and starts exposing internal policy. [24] [29]

Semantic coupling through shared conventions is worse still because it is hard to see. Two modules may appear decoupled at the syntax level while depending on naming schemes, magic string formats, directory layouts, error-message text, undocumented JSON shapes, or implied units and meanings. Nothing in the type system or function signature may reveal the dependency, but changing the convention still breaks the system. This is one of the most common failure modes in generated code, where separate files independently “agree” on a format that was never explicitly defined anywhere. [25] [30]

Content coupling is the most obviously severe. One module reaches into another’s internals, depends on private fields, patches hidden structures, or relies on incidental implementation details instead of a published interface. This creates brittle software because the client is no longer coupled to behavior alone; it is coupled to representation. Even small internal refactors become externally breaking changes. If low coupling means clients depend only on what a module promises, content coupling is its direct opposite. [24]

A related category is cross-cutting policy coupling: authorization, logging, retries, transactions, validation, or feature-flag decisions spread across many modules instead of being centralized behind stable boundaries. This makes the “real” behavior of the system emerge from the combination of many files, each carrying a fragment of the rule. Such systems are hard to test, hard to change safely, and hard to retrieve correctly because no single file states the whole policy. [27] [8]

What makes these forms of coupling especially costly is that they expand the hidden context required for correct change. In a healthy design, modifying a behavior should require understanding a small, explicit neighborhood of code. In a badly coupled design, the true neighborhood is larger than it appears. Humans compensate with experience; agents compensate by opening more files. Both become less reliable as more of the contract lives offstage. [25] [28] [30] [19] [20]

The practical priority, then, is not to eliminate all dependency. It is to eliminate the dependency forms that smuggle knowledge across boundaries: ambient state, sequencing requirements, mode flags, unowned conventions, and reliance on internals. These are the couplings that most directly destroy cohesion, widen change impact, and reduce the quality of reasoning that a codebase permits. [24] [25] [30]

The trade-off is that stronger interfaces can increase friction at boundaries. A team that aggressively decomposes everything may gain cleaner contracts but lose flow through excess coordination.

Avoiding unnecessary complexity

Avoiding unnecessary complexity means resisting abstractions, indirections, and generalizations that do not solve a current problem. Complexity is not only a readability issue; it expands the amount of context required to make safe changes. OpenAI’s experience with agent-first development makes this very concrete: a giant manual, a sprawling abstraction layer, or a maze of special cases all crowd out the relevant signal. [6] [7]

“You must pick your battles in design.” — Larman

A common source of unnecessary complexity is premature abstraction: introducing interfaces, wrappers, or generic mechanisms before the underlying variation is real. This often feels like prudence, but it can turn one simple path into several layers of indirection that every future reader must traverse. In practice, an abstraction earns its keep only when it removes repeated change or protects a volatility that has actually shown up. [2] [24] [25]

Unnecessary complexity also accumulates through special cases: feature flags, compatibility branches, fallback paths, and one-off exceptions that survive long after the original need has passed. Each exception may be locally reasonable, yet together they widen the amount of context required to understand the normal path. For agents especially, this is costly: every additional branch, wrapper, or tool surface competes with the core signal needed to complete the task. [26] [7] [27]

For agentic engineering, unnecessary complexity has a direct token cost. Abstractions that are harmless for humans in a small codebase become expensive when an agent must retrieve, interpret, and reconcile them across many files and tool definitions. Simplicity is therefore not only a design preference but a context-management strategy: fewer layers, fewer exceptions, and fewer invented concepts leave more room for the information that actually determines behavior. [6] [7] [27]

That does not mean “never abstract.” It means introducing indirection where it protects meaningful variation, not where it merely advertises cleverness. If the abstraction does not reduce the cost of future change, it often raises the cost of navigation, testing, and debugging instead. [2]

Avoiding premature generalization

Generalization is different from abstraction. An abstraction reduces something to its essential form; a generalization consolidates multiple concrete cases behind a shared module, interface, configuration surface, or reusable mechanism. The risk is not merely that detail gets hidden, but that behaviors which only appear similar are forced to live under one shared structure before there is enough evidence that they truly belong together. [1] [2] [34] [35]

Avoiding premature generalization means refusing to introduce shared abstractions, flexible configuration layers, or generic building blocks before there is enough evidence that the behavior is truly shared. It is usually cheaper to extract later than to maintain the wrong generalization too early. [1] [2] [34] [36]

The failure mode is that early consolidation often creates a false common core. Code paths that only look alike at first begin to diverge under real use, and the shared layer then accumulates flags, branches, and configuration to preserve the illusion of uniformity. Instead of reducing complexity, the generalized module becomes the place where unrelated variation is forced to coexist. [34] [35]

This matters disproportionately in agentic codebases because speculative generalization expands the search space and obscures the source of truth. An agent confronted with a premature “platform” layer has to infer whether the true behavior lives in the feature code, the generic framework, the configuration surface, or all three. What looks like reuse to the designer often looks like ambiguity to the agent. [6] [7]

A safer approach is to let duplication survive a little longer while the real axes of variation become clear. Once repeated behavior is stable, extraction is more likely to produce a shared seam that matches reality instead of a generalized layer that has to be constantly patched to fit new cases. [34] [35] [36]

When extraction is actually needed, patterns such as branch by abstraction let teams move toward a shared seam gradually instead of forcing a big-bang migration. [14]

Avoiding premature optimization

Avoiding premature optimization means not distorting the structure of the system around performance concerns that are not yet real constraints. Once optimization becomes the driver too early, the codebase often becomes harder to understand, change, and verify long before the performance gain is actually needed. For agents, this usually manifests as extra caches, specialized execution paths, and implicit invariants that are poorly documented and hard to validate locally. [16] [17]

A useful test is whether the performance requirement is explicit enough to be checked. A latency ceiling, memory budget, throughput target, or infrastructure cost constraint can be measured and enforced; an imagined future bottleneck cannot. Premature optimization becomes less likely when performance concerns are expressed as budgets and profiling targets rather than as architectural folklore. [37] [38]

The main danger is not only extra code, but extra invariants. Caches must stay coherent, fast paths must remain behaviorally equivalent to slow paths, pooled resources must be released correctly, and concurrent execution paths must preserve assumptions that are rarely obvious from the interface. These hidden constraints increase the amount of context required for safe change, which makes them especially costly in agentic codebases. [6] [7] [37]

When performance is truly a first-class requirement, the right response is not speculative cleverness but explicit feedback loops: benchmarks, profilers, latency SLOs, regression thresholds, and tests for the critical path. That gives agents something concrete to optimize against while preserving a codebase whose structure still reflects the product rather than a guessed bottleneck. [17] [37] [38] [39]

That does not rule out performance-first design for real hot paths. It means that performance work should be driven by measured constraints and reinforced by explicit tests or budgets, not by speculative complexity. Evaluation loops against latency targets are a better fit for agents than architecture distorted in advance around imagined bottlenecks. [17]

Ownership-aligned boundaries

Boundary quality improves when the conceptual structure of the system lines up with ownership. When teams or maintainers have clear responsibility for a bounded area, conventions, documentation, and decision-making tend to stay more coherent inside that area, which also makes it easier for agents to orient themselves. The goal is not to fragment the system for its own sake, but to decompose it so that the unit of ownership, the unit of change, and the unit of understanding reinforce each other instead of pulling apart. [5] [21]

Ownership alignment is also a coordination strategy. When a boundary matches a team’s day-to-day responsibility, decisions about naming, documentation, interfaces, and change sequencing are more likely to stay consistent inside that area. When the boundary and the ownership model diverge, the software may still look modular on paper while the real work crosses teams, handoffs, and approval paths. That usually means the true cost of change is higher than the structure suggests. [5] [21] [48]

This is closely related to Conway’s Law: systems tend to mirror the communication structure of the organization that builds them. If a supposedly bounded part of the system requires constant coordination across unrelated owners, the boundary is probably wrong, incomplete, or socially unenforced. In that case, the unit of understanding in the code and the unit of collaboration in the organization are pulling in different directions. [48] [21]

For agentic engineering, ownership-aligned boundaries improve more than human workflow; they improve context quality. A bounded area with clear ownership is more likely to have coherent conventions, localized documentation, and a stable source of truth, all of which make it easier for an agent to determine where a change belongs and what assumptions it must preserve. [7] [17] [49]

The supporting systems around those boundaries matter too, but they deserve their own treatment. The next article covers repository-local documentation, behavior specs, executable guardrails, and verification loops.

FAQ about maintainable codebases for AI coding agents

What makes a codebase AI-agent-friendly?

A codebase is AI-agent-friendly when an unfamiliar coding agent can find the right context, make a narrow change, and verify the result without exploring the whole system. In practice, that means strong locality, small blast radius, explicit boundaries, good navigability, and narrow verification loops. [6] [7] [16]

Why are vertical slices useful for AI coding agents?

Vertical slices reduce the semantic search space. Instead of scattering a feature across technical layers and cross-cutting abstractions, they let the agent work mostly inside one cohesive behavior, especially when that slice sits inside a domain boundary. That improves locality, navigability, and testability. [3] [4] [5] [6] [22] [23]

How do you reduce blast radius for AI-generated code changes?

You reduce blast radius by putting likely points of change behind stable interfaces, enforcing dependency directions mechanically, and giving agents narrow workflows with isolated worktrees, checkpoints, and targeted validation. The goal is to make changes safer both structurally and operationally. [2] [6] [8] [19]

Why do AI coding agents need clear boundaries?

AI coding agents reason from partial observations. If boundaries are weak, the agent may infer the wrong contract from a local view of the code. Clear interfaces, bounded contexts, and explicit ownership reduce ambiguity and make the architecture more legible. [3] [6] [20]

Is this only relevant for teams using coding agents?

No. These characteristics also make codebases easier for humans to understand and maintain. AI coding agents simply make structural weaknesses visible sooner, because they rely more heavily on legible boundaries, concise context, and fast validation. [6] [7] [16]

Conclusion

A maintainable codebase for AI coding agents starts with structure. If behavior stays local, boundaries are explicit, ownership is legible, and unnecessary complexity is kept out, agents can build better local models and change software more safely. Those properties are valuable for humans too; agents simply make the cost of neglecting them visible much sooner. [6] [7] [16]

The next article builds on that structure by covering the surrounding support system: documentation, behavioral specs, executable guardrails, and narrow feedback loops. [6] [17]