Building AI agents in Tines Stories — tips and tricks for getting started

AI agents in Tines Stories should be used selectively for tasks requiring reasoning, judgment, language generation, or analysis, while predictable work such as structured parsing, routing, formatting, and fixed decision logic should remain deterministic to reduce cost, errors, and verification needs. Reliable agents depend on clear separation between persistent system instructions and per-run prompts, specific and well-structured directions, relevant examples where needed, and real upstream data that limits guessing and hallucination. Configuration should match the task, using smaller models and low temperature settings for simple, consistent work, larger models for complex reasoning, and structured output schemas to support dependable downstream automation. Agents can be extended through custom tools, reusable stories, templates, MCP servers, and domain-specific skills, but tool descriptions and permissions should be carefully scoped. Security practices include least-privilege tool access, redacting sensitive data before it reaches prompts, limiting prompt-injection impact through narrow agent roles and validation or human approval, and auditing agent inputs, outputs, and tool calls. AI should be avoided for deterministic lookups, finite if/else workflows, and situations requiring perfectly identical outputs, with builders encouraged to begin with a narrowly defined use case and refine it over time.

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