Tool Fallbacks: HITL and Clarification
Explore how to design AI workflows that manage conflicting customer data through safe fallback rules, human review gates, and structured handoff packets ensuring reliable escalation and no guesswork.
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A support run can look successful until the state shows otherwise. The lookup_customer tool returns customer_id=null and status=“unknown_customer”, and a second source reports account_status=“active” for the same email, so the workflow cannot safely choose a template or promise an outcome.
That is where a safe stop matters. Instead of guessing, the loop deliberately terminates with needs_human=true, sets a stop_reason, and produces a handoff_packet that preserves what happened and what to do next. This lesson stays on control flow and handoff artifacts.
To see the failure arrive, trace how the state changes step by step and note where the null field and the conflict first appear:
The key behavior is that the workflow does not collapse into retries or fabricate details. When the state includes missing required fields or conflicting flags, the next state sets next_action=“escalate” and records the exact evidence that triggered escalation, so a human can act without rereading logs.
The conflict in this lesson is not staged inside a tool. It comes from two real systems of record that drifted apart: the customer changed ...