ce-agent-native-architecture
everyinc/compound-engineering-plugin
Build applications where agents are first-class citizens using atomic tools and prompt-defined features.
What is ce-agent-native-architecture?
Agent-native architecture treats agents as core application components rather than add-ons. Features are outcomes achieved by agents operating in loops with tools, not traditional code functions. Use this skill when designing autonomous systems, creating MCP tools, implementing self-modifying applications, or building products where agent capability and user capability must achieve parity.
- Design agent-native systems following five core principles: parity, granularity, composability, emergent capability, and improvement over time
- Create atomic primitive tools and MCP tool designs with dynamic capability discovery and CRUD completeness
- Implement execution patterns including completion signals, partial completion handling, and context limit management
- Define system prompts that specify agent behavior and judgment criteria without hardcoding features
- Inject runtime context into agent prompts to enable dynamic state awareness and decision-making
- Establish action parity between user UI actions and agent tool capabilities
How to install ce-agent-native-architecture
npx skills add https://github.com/everyinc/compound-engineering-plugin --skill ce-agent-native-architectureHow to use ce-agent-native-architecture
- 1.Identify which aspect of agent-native architecture you need help with using the intake menu (design, tools, execution, prompts, etc.)
- 2.Read the corresponding reference file(s) from the routing table
- 3.Apply the patterns and checklists to your specific context
- 4.Use the architecture checklist to validate your design against core principles and anti-patterns
- 5.Iterate on system prompts, tool design, and context injection based on agent behavior
Use cases
- Design a new autonomous agent system from scratch using architectural patterns and checklists
- Create MCP tools for agents with proper granularity and dynamic capability discovery
- Build self-modifying applications where agents safely evolve their own behavior via git
- Implement mobile agent applications with checkpoint/resume and background execution patterns
- Migrate existing code-driven applications to prompt-native, agent-first architectures
- Architects designing autonomous agent systems
- Developers building MCP tools and agent integrations
- Product teams implementing agent-first features
- Engineers migrating legacy systems to agent-native patterns
- Teams building self-modifying or self-improving applications
ce-agent-native-architecture FAQ
Agent-native treats agents as first-class citizens where features are prompt-defined outcomes achieved through tool loops, not hardcoded functions. Traditional integration adds agents as a layer on top of existing code. Agent-native requires parity between user and agent capabilities.
Use the action-parity discipline: map every user UI action to an equivalent agent tool, ensure tools are atomic primitives, and validate that agents can compose tools to achieve the same outcomes users achieve through the interface.
Start with atomic primitives that achieve parity with user actions. Graduate to domain tools only when primitives become inefficient or when domain-specific logic reduces prompt complexity. Reference `from-primitives-to-domain-tools.md` for decision criteria.
Use git-based evolution patterns where agents can modify their own prompts and context files with guardrails. Store agent state in version-controlled files, implement approval workflows, and maintain audit trails of agent-driven changes.
Dynamic context injection feeds runtime application state into agent prompts, enabling agents to make decisions based on current conditions. Use `context.md` files and similar patterns to make app state visible to agents without hardcoding it into prompts.
Full instructions (SKILL.md)
Source of truth, from everyinc/compound-engineering-plugin.
name: ce-agent-native-architecture description: Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
<overview> ## Agent-Native ArchitectureAgent-native applications treat agents as first-class citizens. Features are outcomes achieved by an agent with tools operating in a loop, not functions written in code. The same architecture that powers Claude Code can power apps far beyond coding.
Five core principles:
- Parity — Whatever the user can do through the UI, the agent can achieve through tools.
- Granularity — Tools are atomic primitives; features are prompt-defined outcomes. To change behavior, edit prose, not code.
- Composability — New features = new prompts, not new code. Atomic tools + parity make this possible.
- Emergent Capability — The agent accomplishes things you didn't explicitly design for. Open-ended requests reveal latent demand.
- Improvement Over Time — Apps get better through accumulated context (e.g. a
context.mdfile) and prompt refinement, without shipping code.
For deep coverage of how these principles translate into architectural patterns, read references/architecture-patterns.md.
</overview>
- Design architecture - Plan a new agent-native system from scratch
- Files & workspace - Files as universal interface, shared workspace patterns
- Tool design - Primitive tools, dynamic capability discovery, CRUD completeness
- Domain tools - When to add domain tools vs stay with primitives
- Execution patterns - Completion signals, partial completion, context limits
- System prompts - Define agent behavior, judgment criteria
- Context injection - Inject runtime app state into agent prompts
- Action parity - Ensure agents can do everything users can do
- Self-modification - Enable agents to safely evolve themselves
- Product design - Progressive disclosure, latent demand, approval patterns
- Mobile patterns - iOS storage, background execution, checkpoint/resume
- Testing - Test agent-native apps for capability and parity
- Refactoring - Make existing code more agent-native
- Review / checklists - Architecture checklist, anti-patterns, success criteria
Pick a number or describe what you want. Wait for the response before proceeding. </intake>
<routing> | Response | Read | |----------|------| | 1, "design", "architecture", "plan" | `references/architecture-patterns.md`, then apply the checklist in `references/checklists.md` | | 2, "files", "workspace", "filesystem" | `references/files-universal-interface.md` and `references/shared-workspace-architecture.md` | | 3, "tool", "mcp", "primitive", "crud" | `references/mcp-tool-design.md` | | 4, "domain tool", "when to add" | `references/from-primitives-to-domain-tools.md` | | 5, "execution", "completion", "loop" | `references/agent-execution-patterns.md` | | 6, "prompt", "system prompt", "behavior" | `references/system-prompt-design.md` | | 7, "context", "inject", "runtime", "dynamic" | `references/dynamic-context-injection.md` | | 8, "parity", "ui action", "capability map" | `references/action-parity-discipline.md` | | 9, "self-modify", "evolve", "git" | `references/self-modification.md` | | 10, "product", "progressive", "approval", "latent demand" | `references/product-implications.md` | | 11, "mobile", "ios", "android", "background", "checkpoint" | `references/mobile-patterns.md` | | 12, "test", "testing", "verify", "validate" | `references/agent-native-testing.md` | | 13, "refactor", "existing", "migrate" | `references/refactoring-to-prompt-native.md` | | 14, "review", "audit", "anti-pattern", "checklist", "success criteria" | `references/checklists.md` |After reading the reference, apply those patterns to the user's specific context. </routing>
<reference_index>
Reference Files
Core patterns:
references/architecture-patterns.md— Event-driven, unified orchestrator, agent-to-UI; full coverage of the five principlesreferences/files-universal-interface.md— Why files, organization, context.mdreferences/mcp-tool-design.md— Tool design, dynamic capability discovery, CRUDreferences/from-primitives-to-domain-tools.md— When to graduate primitives to domain toolsreferences/agent-execution-patterns.md— Completion signals, partial completion, context limitsreferences/system-prompt-design.md— Features as prompts, judgment criteria
Disciplines:
references/dynamic-context-injection.md— Runtime context injectionreferences/action-parity-discipline.md— Capability mapping, parity workflowreferences/shared-workspace-architecture.md— Shared data space, UI integrationreferences/product-implications.md— Progressive disclosure, latent demand, approvalreferences/agent-native-testing.md— Testing outcomes, parity testsreferences/checklists.md— Architecture checklist, anti-patterns, success criteria
Platform-specific:
references/mobile-patterns.md— iOS storage, checkpoint/resume, cost awarenessreferences/self-modification.md— Git-based evolution, guardrailsreferences/refactoring-to-prompt-native.md— Migrating existing code </reference_index>
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