Works with the AI tools you already use
Production Agent Design
A production-grade blueprint for designing, hardening, and shipping stable, autonomous AI agents.
Free
See it in action
You say
Sample Input A) Walk me through designing an agent that handles automated customer support ticket resolution
Sample Input B) Design a tool for my agent to update CRM records.
Sample Input C) My agent keeps looping on the same tool call. What's wrong?
Your agent does
Sample Output A) I will guide you through the phased design workflow.
Phase 1: Design. We must persist an inspectable plan before the agent acts. For memory, ticket history lives in a long-term store (retrieved per ticket), while the active resolution runs in short-term context with a scratchpad for multi-step cases. Confirm this architecture to proceed.
Sample Output B) Surfacing Section 2 (Tool Design). Four things for a CRM update tool:
- One job per tool — separate get_contact and update_contact, not manage_contact(action=...).
- Schema-validate inputs and outputs; reject malformed data at the boundary.
- Idempotency — updates are mutating, so require an explicit confirm argument or two-step commit.
- Error messages that teach recovery: "Field
emailinvalid: expected RFC 5322, gotuser@" — the agent's retry should be informed by your error.
Sample Output C)Two sections apply. Section 7: add loop detection (flag repeated calls with identical arguments) and hard budgets that halt on retry-count breach. Section 5: a verifier/critic step before re-calling catches the agent guessing on low confidence. Diagnose first — is it identical args (state not changing) or drifting args (bad plan)? The fix differs.
About this skill
Production Agent Design by Super Logic AI
Building AI agents that work in a lab is easy. Building agents that survive production without silently looping, leaking data, or blowing through context budgets is hard. Production Agent Design is a portable Agent Skill (SKILL.md) encoding the design decisions that make LLM agents reliable enough to ship- neurosymbolic architecture, explicit planning, plan-anchored checkpoints, schema-validated tools, verifier gates on irreversible actions, and eval-first golden tasks. Drops into Claude Code or any skill-aware runtime.
What it does
- Enforces a phased design workflow from initial architecture to pre-ship hardening.
- Standardizes tool design with strict schema validation, idempotency, and error recovery protocols.
- Implements multi-layer memory management policies to prevent context drift and state loss.
- Provides a rigorous pre-ship sanity checklist covering security, observability, and human-in-the-loop triggers.
Why this beats prompting it yourself
Generalized prompts lack the discipline required for production systems. This skill codifies specific engineering patterns like plan-anchored checkpoints and symbolic guardrails that move logic out of fragile prompts and into reliable code structures.
Use cases
- Designing a multi-step agentic workflow for complex CRM or ERP updates.
- Debugging agents that experience long-horizon drift or repetitive tool-calling loops.
- Hardening an autonomous agent against prompt injection and cross-tenant data leakage.
- Architecting MCP servers with efficient tool budgets and compressed context management.
Known limitations
Requires an existing development environment or agent framework to implement the suggested code-level guardrails. Does not provide the underlying hosting infrastructure.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
Super Logic AI builds production AI automation for small and mid-sized businesses- not demos. We work through Logic.IO, a five-part framework covering Launch, Operate, Grow, Inform, and Care. Every engagement starts with an audit and ends with documentation, training, and a system your team actually runs.
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