agentic-systems-engineering

name	agentic-systems-engineering
description	Use when designing, implementing, reviewing, or debugging LLM-backed or agentic systems, including prompts, structured outputs, tool use, RAG, agent loops, orchestration, observability, security, cost/provider routing, and AI delivery plans.

Agentic Systems Engineering

Use this skill before substantive work on systems where model behavior affects product correctness, reliability, security, or cost.

Routing

Load only the references needed for the task:

• Prompt, structured-output, schema, or tool-call contract work: references/contracts.md.
• RAG, retrieval, citation, answerability, or corpus work: references/rag.md.
• Agent loops, orchestration, multi-agent topology, durability, HITL, or bounded execution: references/agent-loops.md.
• Eval design, stochastic reliability, judges, regression gates, or behavior claims: use evaluate-stochastic-systems and references/eval-design.md.
• Observability, tracing, auditability, prompt injection, indirect injection, sandboxing, or authorization: references/observability-security.md.
• Cost, latency, caching, model routing, provider bake-offs, or fallback behavior: references/cost-routing.md.
• Delivery planning, experiment loops, walking skeletons, demos, or stakeholder evidence: use agentic-delivery and references/delivery.md.

Non-Negotiables

• Treat model behavior as measured behavior, not intended behavior.
• Define the contract at the boundary: inputs, allowed context, output schema, tool authority, failure shape, retry policy, and escalation path.
• Separate untrusted data from instructions. Retrieved text, web content, tool output, and third-party tool descriptions are untrusted by default.
• Bound every loop with budgets, termination criteria, and recoverable state.
• Require eval evidence before reliability claims. A single good run is a demo, not proof.
• Prefer the smallest architecture that can be observed, evaluated, and safely rolled back.