prompt architecture

Demonstrates five-element prompt structure (exact restatement, success criteria, banned intermediates, trap listing, adversarial verification) as architectural pattern.

The entire post is a real-world prompt architecture: problem statement → discovery rules → state management → build order → error handling. Shows layered constraint design.

Treats prompt engineering like design systems with reusable components, constraints, and versioning

The standardized five-section agent prompt template is a direct implementation of deliberate prompt architecture for maintainability.

The guidance on preamble, phase fields, assistant items, and outcome-oriented structure are all core prompt architecture decisions

Describes sophisticated multi-level prompt structure: startup-injected skills (preloaded context) vs task-triggered contexts (context: fork).

The emphasis on 'write descriptions like you're explaining to someone who's never seen it before' is about prompt clarity. Clear descriptions are a form of prompt engineering that scales.

Markdown structure (Level 1) and .md file organization (Levels 1-2) are prompt architecture decisions that improve clarity and parsing efficiency

Moves beyond traditional prompting to encompass the entire context window as an architectural surface, not just the instruction text.

Directly contradicts the single-elaborate-prompt paradigm; suggests multiple smaller, targeted prompts within a loop structure are more effective.

Anti-Pattern #1 directly contradicts the assumption that larger, more detailed prompts lead to better outcomes. Author shows monolithic prompts cause reinterpretation and failure.

The author is describing how prompt structure (what you specify first vs. last) shapes agent behavior—this is core prompt architecture

Core argument is about knowing 'what prompt is constructed, from what context, under what conditions'—this is prompt architecture design. Author argues frameworks hide these decisions.

YAML/Markdown agent configuration files function as persistent prompt/role architecture across sessions

Moves beyond technical structure to information sequencing and narrative framing as architectural choices

Proposes 6-component universal template for 2026, positioning this as evolution beyond traditional prompt engineering. Suggests architecture as primary concern.

References SKILL.md and /goal as architectural prompting patterns, showing how context structure enables agent behavior.

The /goal directive pattern suggests structured prompt design that creates context boundaries and reduces token overhead

Shows architectural pattern for separating human-readable and machine-readable context within a single artifact

Context Constitution is essentially a formal prompt architecture for agents—defining which values and beliefs should be embedded in system prompts

Write strategy encompasses prompt design and context structuring; Isolate relates to separating concerns in prompt architecture

The procedural memory (system prompt structure, tool registries, guardrails) reveals that system prompts should be modular and versioned, not monolithic.

The paper implicitly explores how to structure prompts to include repository context. This validates that prompt structure (not just model capability) determines code generation quality.

Tool descriptions as semantic prompts; MCP represents evolution of how tool information is packaged and progressively disclosed in context

Skills and Subagents represent evolution beyond static prompts toward dynamic, composable context architecture.

CLAUDE.md conventions being ignored due to insufficient thinking shows that well-designed prompts fail if the model lacks reasoning budget to apply them—architecture alone insufficient.

The 'harness' is effectively a structured prompt/context pattern that enables generalization. The quotient set principle is a sophisticated form of prompt architecture.

The harness design is essentially prompt/task architecture—how you structure the problem definition for the model. The insight that identical decomposition = identical token trajectory is about archit

Different harnesses structure prompts differently; this is a concrete example of how prompt architecture choice affects results.

How you structure information fed to models is fundamentally a prompt architecture question

Intent detection layer is a form of prompt architecture—structuring how the system interprets user input to reduce ambiguity and command syntax overhead.

If systems 'mediate agency' through how information is framed, then prompt structure is not decoration—it's the mechanism of agency mediation. This extends prompt architecture from 'best practices' to

/goal is a structured way to architect prompts with embedded completion conditions, replacing ad-hoc multi-turn instruction sets with a declarative completion pattern.

Documentation and skill files are structured prompting at the organizational level. They define the agent's context and behavioral boundaries, similar to system prompt design but at workflow scale.

State machine rules and CHECK_STATUS routing form explicit prompt architecture pattern embedded in workflow

Best practices section and prompt library reference indicate structured approach to composing prompts with managed context

The /grill-with-docs suggestion is a specific prompt pattern (system instructions that make the LLM adopt an adversarial stance). This is prompt architecture in action.

Phase 3 'Learn Prompting for Agents' discusses system vs user prompts, role-based examples, constraints—core prompt engineering for agent context.

Prefix caching requires explicit cache-control breakpoints in prompt structure—a prompt architecture decision that feeds serving-stack optimization.

Article positions monolithic system prompts ('God Prompt') as failed approach, proposing multi-agent separation instead. This is methodological contradiction—same model, different architectural choice

agents.md is a practical implementation of system prompt/context design that persists across sessions rather than being reset per-query

ACE moves away from static prompt engineering toward dynamic strategy representation—a fundamental shift in how context is structured

Article emphasizes that agent effectiveness depends on 'clear, well-structured prompts' and that orchestration patterns should be 'driven by clear prompts.'

MCP provides structured context to prompts; understanding MCP improves ability to design effective system prompts

Context Engineering as framed here requires architectural decisions about prompt structure and information presentation.

Harness design determines what gets 'passed over the boundary'—what information structures reach the model—analogous to prompt architecture decisions

Harness design is a level above individual prompts - it's the meta-architecture that decides what gets into prompts and when.

Static prompt architecture becomes dynamic/evolving; agents reshape their own prompts and context structures over time.

Martin Fowler article on context engineering for coding agents introduces 'context interfaces' as formal architectural contracts, evolving prompt architecture thinking.

AGENTS.md as shared knowledge base, skills site, orchestration tool—all exemplify prompt as infrastructure rather than one-off artifact