Building AI agents in Tines Stories — tips and tricks for advanced builders

Robust AI agents in Tines Stories are presented as systems that require efficient token use, narrow task scope, appropriate human oversight, resilient error handling, and continual performance verification. Recommended practices include trimming inputs and tool outputs, enforcing structured response schemas, selecting only necessary tools and skills, and dividing complex workflows among specialized agents to reduce cost, improve accuracy, and simplify debugging. Human-in-the-loop designs can allow agents to triage, draft responses, classify documents, and support self-service while escalating uncertain, sensitive, or high-risk decisions for review. Reliability measures include configuring retries and timeouts, monitoring token and credit thresholds, using metadata for custom safeguards, and explicitly routing failures. Because agent outputs are non-deterministic, the post recommends challenger agents, decision logging in Tines Records, randomized and targeted human-review sampling, honeypot tests, and feedback loops that analyze overrides and edge cases to refine prompts and skills over time.

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