Context Engineering

Definition

Context Engineering is the systematic approach to designing and structuring the input context provided to Large Language Models (LLMs) to maximize their effectiveness, accuracy, and reliability in generating outputs.

The practice emerged from the recognition that LLMs operate on explicit information only—they cannot intuit missing business logic or infer unstated constraints. Context Engineering addresses this by making tacit knowledge explicit, machine-readable, and version-controlled.

While ASDLC focuses on software development, Context Engineering is domain-agnostic:

• In Design: Design system tokens and Figma layer naming conventions fed to UI agents
• In Law: Briefs restricting paralegal agents to specific case law precedents
• In SDLC: The AGENTS.md file steering agents toward implementation patterns

Anywhere agents operate, context is the constraint that turns raw intelligence into specific value.

Martin Fowler observes: “As I listen to people who are serious with AI-assisted programming, the crucial thing I hear is managing context.”

Anthropic’s research confirms this. Engineers cite the cold start problem as the biggest blocker:

“There is a lot of intrinsic information that I just have about how my team’s code base works that Claude will not have by default… I could spend time trying to iterate on the perfect prompt [but] I’m just going to go and do it myself.”

Context Engineering solves cold start by making tacit knowledge explicit, machine-readable, and version-controlled so agents can act on it without prompt iteration.

Key Characteristics

The Requirements Gap

“Prompt Engineering” is often a misnomer. It is simply Requirements Engineering applied to a non-human entity that cannot intuit missing business logic. Human developers ask clarifying questions when requirements are vague (“What happens if the payment fails?”). AI models build something based on probability. Errors generally surface only when the system breaks in production.

Core Attributes

1. Version Controlled: Context exists as a software asset that lives in the repo, is diffed in PRs, and is subject to peer review.
2. Standardized: Formatted to be readable by any agent (Cursor, Windsurf, Devin, GitHub Copilot).
3. Iterative: Continuously refined based on agent failure modes and tacit information discovered by Human-in-the-loop (HITL) workflows.
4. Schema-First: Data structures defined before requesting content generation to ensure type safety and validation.
5. Hierarchical: Information organized by importance—critical instructions first, references second, examples last.

ASDLC Usage

In ASDLC, context is treated as version-controlled code, not ephemeral prompts.

Context vs Guardrails:

A distinction exists between Guardrails (Safety) and Context (Utility). Currently, many AGENTS.md files contain defensive instructions like “Do not delete files outside this directory” or “Do not output raw secrets.” This is likely a transitional state. OpenAI, Anthropic, Google, and platform wrappers are racing to bake these safety constraints directly into the inference layer. Soon, telling an agent “Don’t leak API keys” will be as redundant as telling a compiler “Optimize for speed.”

Relationship to Patterns:

• Specs — Specs are context engineering in document form. The Blueprint and Contract sections are structured context optimized for agent consumption.
• Context Gates — Checkpoints where context is validated, injected, or filtered before agent action.
• OODA Loop — Context Engineering is engineering the Orient phase. The quality of agent decisions depends on context quality.
• 4D Framework — The “Description” dimension maps directly to Context Engineering: transferring goals, constraints, and data structures to the agent.

Applied in:

• AGENTS.md Specification — The practical application of context engineering in repositories.
• Model Context Protocol — The standard for serving context to agents.

[!NOTE] Research Validation (InfiAgent, 2026): File-centric state management outperforms compressed long-context prompts. Replacing persistent file state with accumulated conversation history dropped task completion from 80/80 to 27.7/80 average, even with Claude 4.5 Sonnet. This validates treating context as a reconstructed view of authoritative file state, not as conversation memory.