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    Production Agent Architect

    by Roy Yuen

    Architect, scaffold, and harden production-grade AI agents with battle-tested patterns and systematic evaluation.

    Updated Jun 2026
    135 views
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    $6

    · or 30 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Design reliable ReAct agents with strict guardrails and loop detection.
    • Scaffold multi-agent systems with explicit handoff and state management.
    • terminal automation included
    • Ready for Cursor
    • Instant install

    Sample input

    Design a high-reliability plan-and-execute agent for automated research with strict cost controls and Pydantic validation. Summarize the target specs and guardrails.

    Sample output

    ARCHITECTURE: Plan-and-Execute [Planner] -> Task List -> [Executor] -> [Verifier] -> Done GUARDRAILS ENABLED:

    • Max Iterations: 10
    • Schema Validation: Pydantic (Strict)
    • Cost Limit: $0.10/session METRICS: 92% Succes Rate | 4.2 Avg Steps LOGGING: Full trace enabled via LangSmith

    Screenshots

    About This Skill

    Build Reliable, Production-Grade AI Agents

    Designing an agent that works in a demo is easy; building one that survives production is a different challenge. This skill provides a professional framework for architecting, scaffolding, and hardening AI agents and multi-agent systems. It moves beyond simple prompting to implement robust software engineering patterns for LLM-based applications.

    What it does

    • Architects complex workflows: ReAct, Plan-and-Execute, Reflexion, and multi-agent orchestration.
    • Generates production-ready scaffolds using Python, LangChain, CrewAI, AutoGen, or custom loops.
    • Implements critical guardrails: max iteration limits, schema validation, cost tracking, and loop detection.
    • Designs sophisticated memory systems and state management solutions.
    • Builds systematic evaluation suites to move past 'vibe-based' testing to quantifiable metrics.

    Why use this skill

    Most AI agents fail in production due to infinite loops, tool-calling hallucinations, or lack of observability. This skill automates the implementation of industry-standard design patterns that solve these issues. It ensures your agents are deterministic where needed, cost-effective, and easy to debug by treating agentic logic as a structured system rather than a black box.

    Supported Patterns & Tools

    • Frameworks: LangChain, CrewAI, AutoGen, LlamaIndex, and Pure Python implementations.
    • Patterns: Tool-calling routers, self-critique/verification cycles, and role-based handoffs.
    • Infrastructure: Structured logging, LangSmith/Helicone tracing, and Pydantic validation.

    Use Cases

    • Design reliable ReAct agents with strict guardrails and loop detection.
    • Scaffold multi-agent systems with explicit handoff and state management.
    • Migrate brittle prompts into structured, verifiable agentic workflows.
    • Implement systematic evaluation suites to measure agent success rates.
    • Add observability and cost-tracking to existing LLM implementations.

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell

    Allowed Hosts

    example.com
    api.openai.com

    File Scopes

    production-agent-architect/**

    Compatible with SKILL.md-compatible agents including Claude Code, Cursor, and OpenClaw.

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