Why architecture matters here
Agent prompt architecture matters because prompts are programs. Sloppy prompts cause sloppy agents. Well-structured prompts with clear roles, tools, safety, and response formats produce agents that behave consistently.
Cost is per-token. Long system prompts multiplied by every turn cost real money; balance detail vs concise.
Reliability comes from versioning + eval like code. Change a word; run eval; measure.
The architecture: every layer explained
Walk the diagram top to bottom.
System prompt. Role, rules, persona. Frames all subsequent behavior.
Tools registered. Schemas + precise descriptions with examples. LLM picks based on these.
Memory injection. Retrieved relevant memories + facts inserted contextually.
Response format. Structured output (JSON schema) or specific format markers.
Safety rules. Refuse patterns; escalation triggers.
Few-shot examples. 3-5 example interactions demonstrating desired behavior.
Chain-of-thought hint. "Think step by step before answering" for complex reasoning.
Budget signals. Cost + latency budget shared with agent so it can adapt.
Versioning + eval. Prompts in git; eval per version; regression gates.
Multi-model portability. Provider-agnostic templates; abstract provider-specific quirks.
End-to-end agent prompt flow
Trace a prompt. Support agent system prompt: ``` You are Aria, a customer support agent for [Company]. Your goal is to resolve customer issues empathetically and accurately. Rules: - Always refuse to speculate about medical/legal advice; escalate to human. - If uncertain about a policy, use the lookup_policy tool. - Respond in JSON: {answer: str, escalate: bool, confidence: "high"|"medium"|"low"}. Tools available: - lookup_policy(topic: str) → policy text - create_ticket(summary: str, priority: "low"|"med"|"high") → ticket ID - get_order(order_id: str) → order details Examples of desired behavior: [example 1: policy question → lookup_policy → answer] [example 2: order question → get_order → answer] [example 3: medical → refuse + escalate] Think step by step before responding. ``` User asks about their order. Agent gets order via tool, formats answer. Structured output parses cleanly. Medical question: agent recognizes pattern; refuses + sets escalate=true. Prompt versioned in git. Eval suite tests all these behaviors. New version tested before deploy.