Analysis 12 min read

# Why Context Alone Doesn’t Prevent Architectural Drift

Context engineering improves recall. It does not enforce architectural constraints. Better retrieval, larger windows, and richer memory layers help agents remember more — but architectural drift is usually not caused by forgetting. It is caused by local optimization, inconsistent enforcement, and the absence of deterministic architectural constraints. The missing layer is governance.

By Theo Valmis·May 2026

## The industry is solving the wrong half of the problem

The infrastructure conversation in AI-assisted development has converged on a clear direction: larger context windows, persistent memory layers, retrieval pipelines, agent handoffs, orchestration runtimes, and long-running sessions. These are real advances, and they solve a real problem — agents forgetting things.

But teams adopting these systems still encounter architectural inconsistency. Duplicated abstractions. Bypassed repository patterns. Policy violations that context retrieval did not prevent. Review queues that grow faster than governance can keep pace.

The problem is not that context engineering fails. The problem is that it was never designed to solve what architectural drift actually is.

Better recall does not guarantee architectural correctness. These are different problems, and they need different infrastructure.

## What context engineering actually solves

Context engineering is a genuine capability advance. Retrieval pipelines surface the right information at the right moment. Persistent memory layers reduce repeated instruction. Session continuity gives agents historical awareness. Cross-agent coordination becomes tractable when context is shared.

These improvements produce measurable benefits: fewer local hallucinations, better task continuity, reduced prompt repetition, and improved coherence across long-running sessions. That is the right problem to solve if recall is your bottleneck.

But context engineering answers the question: did the agent have access to the right information? It does not answer: was the agent constrained from violating the architecture? Those are different questions, and retrieval infrastructure can only answer the first one.

## Why drift still happens

Architectural drift enters AI-assisted systems through several distinct mechanisms. Each has a different root cause. None of them is solved by better recall.

### A. Local optimization drift

Agents optimize for task completion, passing tests, taking the shortest path, and following nearby examples. These are rational local objectives. They are not architectural objectives.

An agent with perfect context can still introduce a new HTTP client, duplicate an abstraction, or bypass a repository layer — because the context described the preferred pattern but did not deterministically constrain the deviation. The agent saw the right information and optimized locally anyway.

### B. Context dilution over time

Long-running sessions accumulate noise. Stale intent, conflicting instructions, partial decisions, and superseded guidance all enter the context window as sessions extend. Architectural constraints become statistically weaker as context grows.

Context windows are accumulators, not prioritization engines. Without a governance layer that maintains deterministic constraint precedence, older or more authoritative constraints can be outweighed by recency.

### C. Delegation drift

In orchestrated systems, one agent plans, another implements, another reviews, and tools mutate state independently. Architectural intent that was clear at the planning stage fragments as it passes through execution surfaces. Each handoff is an opportunity for constraints to fail to propagate.

This is the governance propagation problem: architectural decisions need to travel with the workflow, not just exist in the initiating agent’s context window.

### D. Retrieval is not enforcement

A retrieved architecture decision record can still be ignored. It can be outweighed by a more recent instruction, overridden by a local optimization, diluted by noise, or simply contradicted by a different part of the context window.

RAG surfaces information. Information is not constraint. Governance determines whether violations are allowed to proceed — and that is a binary enforcement question, not a retrieval quality question.

## The missing layer: architectural governance

Context engineering and architectural governance are different infrastructure layers. They solve different problems and they fail in different ways.

Layer | Purpose | Failure mode
--- | --- | ---
Context engineering | Recall and continuity | The agent didn’t have the right information
Governance | Constraint and verification | The agent had the information but violated the constraint anyway
Verification | Deterministic enforcement | The output was never checked against the constraint

Governance systems encode architectural intent as machine-evaluable constraints. They define invariants. They enforce constraints at execution surfaces. They validate outputs before they can reach the codebase. And they propagate rules across every agent and CI surface in the workflow.

Governance is architecture made executable.

The missing tier — governance sits between orchestration and verification

## Why orchestration makes this worse

As AI systems evolve toward agent swarms, long-running execution, asynchronous delegation, and autonomous remediation loops, the drift problem does not stay linear. It compounds.

Each additional agent is a new execution surface where architectural constraints can fail to propagate. Each asynchronous handoff is an opportunity for governance to drop out of the loop. Each layer of orchestration adds distance between the original architectural intent and the eventual output.

Context was present — governance was absent

Human review queues grow faster than governance can be applied manually. The answer is not faster review — it is enforcement at the point of generation.

## What systems actually need

The infrastructure gap is not about better memory or longer context windows. Systems operating at agent velocity need a different class of infrastructure:

- Executable architectural intent — decisions encoded as machine-evaluable constraints, not documentation
- Provenance-aware enforcement — every verdict traceable to the originating architectural decision
- Deterministic validation — same constraint, same codebase state, same verdict, every run
- Governance propagation — constraints that travel with the workflow across agents, tools, and CI
- Verification contracts — predefined checks that run automatically before any output reaches the codebase

The future is not agents with larger memories. It is systems with enforceable architectural integrity.

## Practical implementation patterns

Governance infrastructure does not require a full platform rewrite. The insertion points are specific:

Governance constraints can be inserted at each of these surfaces

- ADR-driven governance — compile architecture decision records into machine-evaluable constraint records
- Pre-execution validation — check constraints before tool calls modify the codebase
- CI architectural gates — every PR runs against the compiled constraint set before merge
- Scope-aware enforcement — constraints apply to specific file patterns, not the whole repo indiscriminately
- Policy propagation — constraints shared across every agent on the codebase, not per-session
- Provenance traces — every enforcement verdict is traceable to its originating decision

## Conclusion

Context engineering is becoming foundational infrastructure for AI-assisted development. Larger windows, persistent memory, and retrieval pipelines are not optional — they are table stakes for operating at agent velocity.

But recall alone does not preserve architecture. As orchestration complexity grows, systems will increasingly require governance layers that make architectural intent enforceable, portable, and verifiable across every execution surface.

The next evolution after context engineering is architectural governance.

## Frequently asked questions

What is architectural drift in AI-assisted development?+
Architectural drift is the gradual accumulation of deviations from the architectural decisions that define how a system should be built. In AI-assisted development, drift accelerates because agents optimize for local objectives — task completion, passing tests, shortest path — without the architectural constraint context that would prevent each local optimization from compounding into system-wide inconsistency.
Why doesn’t context engineering prevent drift?+
Context engineering solves the recall problem — making sure the agent has access to the right information. But drift is not primarily a recall problem. Agents can have perfect context and still violate architectural constraints through local optimization, context dilution, delegation handoffs, or the absence of deterministic enforcement. Retrieval surfaces information; governance determines whether violations are allowed.
How is governance different from retrieval?+
Retrieval makes information available. Governance makes constraints enforced. A retrieved architecture decision record can still be ignored, overridden, or diluted by conflicting context. A governance constraint produces a binary verdict — either the output complies with the architectural intent or it is blocked. Same constraint, same codebase state, same verdict, every run.
Is this anti-context engineering?+
No. Context engineering and governance are complementary layers. Larger windows, persistent memory, and retrieval pipelines reduce recall failures. Governance and verification reduce constraint violations. Both are necessary. The argument here is not that context engineering is insufficient in some absolute sense — it is that context engineering was never designed to solve the constraint enforcement problem, and treating it as if it does leaves architectural drift unaddressed.
Related governance concepts

- Governance Infrastructure

  The platform layer that encodes, distributes, versions, and enforces decisions across agents and CI.

- Verification Contracts

  Predefined checks that prove architectural intent survived the agent run.

- Governance Propagation

  Identical compiled constraints fan out to every agent, IDE, hook, and CI surface.

- Architectural Drift

  The gradual divergence between a system’s actual state and its documented architectural intent.

Related operational essays

- Memory Is Not Governance

  Context persistence cannot enforce architectural invariants. Recall is not constraint.

- Why RAG Fails for Architectural Governance

  Retrieval surfaces information. It does not enforce constraints. The failure modes are different.

- Why Prompt Memory Fails at Scale

  As sessions grow, prompt-based context cannot maintain deterministic architectural constraint priority.

- Agent Skills vs Architectural Governance

  Skills package reusable capability. Governance encodes enforceable constraints. These are different layers of the same stack.