agent-prompt

System prompts for TD AI agents define role, constraints, and processing workflows. Structure yours with explicit role designation, operational constraints, step-by-step processes, context provisioning, and deterministic output formatting to ensure consistent agent behavior across configurations and scenarios.

An effective LLM agent system prompt includes role definition that establishes the agent's purpose, constraints that limit scope and enforce safety, workflow steps that guide processing logic, tool usage guidelines with cautions, and output format specifications to produce deterministic, structured results.

Constraints and tool usage are enforced through explicit prompt sections that specify boundaries, precautions, and when tools apply. Define which tools are available, their valid inputs, expected outputs, and failure modes. Pair this with role constraints that restrict the agent's autonomy and decision scope.

Yes. System prompt templates provide reusable patterns for role and constraint definition, workflow guidance, and output formatting. Apply template sections to your specific scenario—customer support, data processing, or analysis—then customize role designation, constraints, and tool configurations to match your use case.

Structure context by provisioning required information upfront—data schemas, available tools, and domain knowledge. Define output formats explicitly with examples or schema specifications. Separate context sections from workflow steps and output directives so the agent processes instructions deterministically and produces consistent, parseable results.

Precise system prompts enforce predictable agent behavior by eliminating ambiguity in role, constraints, and processing logic. Clear workflow steps, explicit tool usage rules, and deterministic output specifications reduce hallucination, ensure compliance with constraints, and make agent responses reproducible across identical inputs and configurations.