prompt architecture

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 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.

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.

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

Managing context before LLM generation implies structured prompt design, though not explicitly discussed in visible excerpt

Describes scratchpad pattern and how prompts must be structured to enable agentic context management

Assumes stable context window across sessions. Claude Code violates this assumption based on telemetry setting. Breaks the model-as-stable-thing assumption.

Where retrieved context is positioned in the prompt (early vs. late) is a prompt architecture decision

Role/goal/backstory pattern is a concrete prompt architecture example for constraining agent behavior through context

Post frames context engineering as broader discipline than prompting, includes storage/retrieval/compression decisions prompting doesn't cover

The feature addresses how to structure prompts with environmental context. The fact that it captures both screenshot and extracted text suggests a pattern for building multi-modal context into prompts

Lack of clear system prompts defining agent autonomy boundaries and verification requirements is likely root cause of the supervision overhead described.

Absolute Requirements checklist pattern shows structured prompt layering (pre-task acknowledgment, post-task verification)

Agent designs necessarily structure how task definitions, tool descriptions, and previous outputs are composed into prompts sent to reasoning models—fundamental prompt architecture decision.

Harnesses manage what information is in the prompt at each step; poor prompt architecture (context clarity) is a common agent failure mode.

MCP defines how external context is injected into prompts/conversations. Complements prompt architecture patterns by standardizing the 'context injection' layer.

The /tree structured output suggests implicit prompt design for parseable responses that reduce semantic redundancy without information loss.

Living specs function as a system-level prompt architecture where all agents share a reference spec (analogous to system prompts) to maintain alignment.

The failures suggest system prompt inadequately communicates tool capabilities and composition rules

Each agent in a multi-agent system needs its own specialized prompt architecture tailored to its role - validates importance of deliberate prompt design.

Structured template approach (not freeform summary) suggests system prompt design should define expected context fields for consistency across agent loops.