ai system prompt and workflow architect

The problem

Vague system prompts lead to hallucinated citations, unauthorized tool use, and unpredictable agent behavior. Standard prompting often fails to account for edge cases, evidence standards, and clear escalation paths needed for production-grade reliability.

What it does

• Architects structured system prompts with explicit roles, missions, and priority hierarchies.
• Defines rigorous evidence policies for material claims to prevent hallucinations.
• Maps end-to-end workflows including state transitions, retries, and human-in-the-loop gates.
• Generates comprehensive test suites covering adversarial inputs, tool failures, and boundary cases.
• Designs structured output schemas and tool interface definitions with strict preconditions.

Why this beats prompting it yourself

Most developers write prompts as a list of "don'ts" and personality traits. This skill applies engineering principles to prompt design, treating the LLM as a state machine with deterministic routing, explicit failure handling, and audit trails that work reliably in a codebase.

Use cases

• Designing a customer support agent that knows exactly when to hand off to a human.
• Building a data-extraction pipeline with strict evidence requirements and source citations.
• Creating a tool-calling assistant that requires confirmation before executing high-risk side effects.
• Drafting a legal or compliance review agent with hard boundaries on its authority.

Known limitations

Does not provide the actual execution runtime or hosting infrastructure. The output is a design specification and prompt text that must be integrated into your application code.