Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentsProduction Agent Architect
Architect, scaffold, and harden production-grade AI agents with battle-tested patterns and systematic evaluation.
$6
production-agent-architect
Example session with this skill installed
Design a high-reliability plan-and-execute agent for automated research with strict cost controls and Pydantic validation. Summarize the target specs and guardrails.
- Read your context and instructions
- Compiled the production-agent-architect
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
Connects securely to your tools. The creator never sees your data.
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.
- Requires existing API keys for LLM providers.
- Not a standalone runtime; generates code and patterns for external frameworks.
- Local execution depends on user-provided Python environment.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill goes through the trust checks below.
Security scanned
Checked by our 8-point scan before listing
2 uses
Times used to date
Money-back guarantee
Doesn't do what its page promises? You get your money back.
Trust & safety
Security scan
Security scan passed 6 months ago
Requires
Python