OpenAI-native context engineering in practice: From architecture to evaluation

We’ve covered why context engineering is critical; let’s get practical about how to do it. This is a technical guide for AI engineers and architects to implement the Five-Layer Architecture (layers A through E), covering system identity, retrieval, state, session memory, tool hygiene, and eval-driven improvement. 

Context engineering has quickly emerged as the defining discipline for production-grade AI systems. While much has been written about why context matters, far less attention is paid to how it should be engineered in practice. This gap is precisely where most enterprise AI initiatives struggle, not because the models are weak, but because the surrounding context is brittle, unstructured, or unmanaged.

This article is a practical, engineering-first guide for AI architects and builders. We walk through the Five-Layer Context Architecture (Layers A through E), explaining how to design agent identity, retrieval, state, memory, tools, and evaluation as a cohesive system. The goal is simple: to move from prompt-level experimentation to reliable, auditable, production-ready AI.

Context engineering as a system, not a prompt

Large language models do not fail randomly. When they fail in production, the root cause is almost always contextual: missing constraints, polluted memory, overloaded prompts, or uncontrolled tool outputs. Context engineering addresses these issues by treating context as a first-class system artifact rather than an afterthought.

Instead of asking, “What prompt should we write?”, context engineering asks deeper questions:

• What information is it allowed to see?

• What does the agent believe about itself?

• What state persists, and what must be forgotten?

• How do we prove that changes actually improve behavior?

The Five-Layer Context Architecture provides a concrete answer to these questions.

Layer A: System identity and prompt optimization for LLM context engineering

Every enterprise LLM agent begins with a system identity. Before knowledge retrieval or tool execution occurs, the model must understand who it is, how it reasons, and which constraints it must never violate. Layer A defines this foundation.

At Fractal, agents are initialized with a Reasoning-First system instruction, ensuring the model internally validates assumptions and constraints before producing a final response. This approach dramatically reduces hallucinations and improves consistency in regulated or high-risk workflows.

Because static prompts degrade over time, Layer A also incorporates continuous prompt refinement using the OpenAI Prompt Optimizer. Instead of relying on manual rewrites, system instructions evolve automatically based on real transcript failures and evaluation feedback.

Layer A implementation highlights

• Strong system instructions defining role, tone, and reasoning behavior

• Enforced reasoning-first responses using GPT-5.2 Thinking

• Continuous prompt optimization based on real user interactions

• Improved output determinism through explicit constraint tightening

Layer A ensures that all downstream context engineering decisions rest on a stable, auditable identity layer.

Layer B: Knowledge retrieval architecture using vector stores and embeddings

Knowledge access is the most common source of LLM failure at scale. Injecting large documents into prompts leads to noise, latency, and degraded reasoning. Layer B replaces prompt stuffing with selective knowledge retrieval.

Fractal implements this layer using OpenAI Vector Stores with text-embedding-3-large, enabling semantic search across millions of documents while injecting only the most relevant snippets into the context window. This approach keeps LLM context precise and performant.

Importantly, enterprise knowledge retrieval must be governed. Layer B combines semantic similarity with metadata-aware filtering, ensuring that retrieved information matches product, region, and regulatory constraints.

Layer B implementation highlights

• Top-K semantic retrieval instead of bulk document injection

• Metadata filtering by SKU, geography, customer tier, or compliance domain

• Centralized governance via OpenAI Connector Registry

• Clear separation between data access and prompt logic

Layer B ensures that LLM agents reason over accurate, compliant, and relevant knowledge, not just similar text.

Layer C: Dynamic state management for context-aware AI agents

Enterprise AI agents must reason within a changing operational context. User entitlements, product ownership, and regulatory requirements can shift independently of the conversation itself. Layer C introduces Dynamic State as a dedicated context layer.

Dynamic State is injected as structured metadata at the start of every session and updated whenever conditions change. By isolating state from conversational memory, the agent adapts instantly without carrying forward stale assumptions.

This design prevents context contamination, one of the most common causes of incorrect or unsafe responses in long-running LLM interactions.

Layer C implementation highlights

• Structured user context (tier, permissions, entitlements)

• Explicit product state (owned SKUs, versions, configurations)

• Regulatory modes such as GDPR or region-specific handling

• Isolation of state from conversational memory for instant switching

Layer C allows LLM agents to remain context-aware without becoming context-bound.

Layer D: Session memory architecture using OpenAI agentKit

As interactions grow longer, memory management becomes the dominant scaling challenge. Raw chat history quickly becomes expensive and unreliable. Layer D introduces structured session memory using the OpenAI Agents SDK.

For extended workflows, Fractal leverages GPT-5.2 native compaction, compressing earlier conversation history into latent representations that preserve intent and constraints while dramatically reducing token usage.

Layer D also distinguishes between short and complex interactions by applying different memory strategies based on task complexity.

Layer D implementation highlights

Stateful sessions instead of raw message arrays

• Native memory compaction to control token growth

• Context trimming for short interactions with protected fields

• Structured summarization for complex workflows

• Built-in contradiction checks for noisy RAG data

• Normalized and capped tool outputs (“tool hygiene”)

Layer D ensures that LLM agents remember the right information for the right duration no more, no less.

Layer E: Tool use and action execution without context overload

Enterprise LLM agents must take action: checking tickets, triggering workflows, and interacting with operational systems. Without control, tool outputs can quickly overwhelm the context window. Layer E governs tool execution without context exhaustion.

Instead of injecting raw API responses into the prompt, Layer E distills tool outputs into concise semantic conclusions. This preserves reasoning quality while enabling reliable action.

Layer E implementation highlights

• Controlled tool invocation for operational tasks

• Distillation of tool outputs into semantic summaries

• Strict size limits on tool responses entering memory

• Separation of execution detail from reasoning context

Layer E enables agents to act effectively without sacrificing cognitive clarity.

Why the five-layer context architecture matters for LLMs

Together, these five layers form a complete context engineering framework for enterprise AI. They transform large language models from probabilistic text generators into predictable, auditable systems that can be safely deployed in production.

By separating identity, knowledge, state, memory, and action and continuously evaluating each layer teams can scale AI agents with confidence rather than guesswork.

Eval-driven context engineering: Closing the feedback loop

In production systems, architecture alone is insufficient. Every design choice must be continuously validated. Fractal addresses this through Eval-Driven Development (EDD), treating evaluation as a core runtime capability rather than an offline exercise.

Historical conversations are replayed as test cases using the OpenAI Evals API, allowing teams to measure next-turn correctness under different context policies. This replay mechanism acts as a regression suite, ensuring that improvements in one area do not silently break another.

Long-running workflows are audited for entity and constraint retention using automated graders. Critical anchors such as ticket IDs and error codes are continuously checked to prevent contextual drift. Tool calls are traced end-to-end using OpenAI Model Tracing, verifying not just outcomes, but procedural correctness.

Summarization quality is evaluated using an LLM-as-a-Judge approach, with flagship reasoning models enforcing strict rubrics for faithfulness, relevance, and conciseness. These evaluations feed directly back into prompt optimization, completing a closed improvement loop.

Pressure Testing and Token Threshold Monitoring

The most dangerous failures are silent ones, when an agent appears functional but has forgotten a critical constraint. To prevent this, continuous monitoring of token usage, context eviction, and memory pressure using native OpenAI telemetry and OpenTelemetry tracing is important. When protected anchors are lost, automated alerts trigger deeper audits or summarization retries, restoring integrity before user impact occurs.

Final thoughts: From prompts to production

Context engineering is not a one-time setup. It is an iterative engineering discipline. Successful teams start small, validate each layer independently, and scale deliberately adding retrieval depth, memory complexity, and tooling only when supported by evaluation.

The reward is substantial. Instead of opaque, fragile systems, you gain agents that are explainable, controllable, and resilient. Context engineering is how AI systems mature, from clever demos into dependable enterprise infrastructure.