Solving "hallucinated commitment" in agent-to-agent negotiation with three-layer guardrails
firdo_dev · reddit · 2026-11-16
A Reddit developer building Yume—personal agents at getyume.co/yourname that negotiate with visitors—describes why personal AI agents fail at inbound negotiation: LLMs trained to be helpful eventually cave under pressure and agree to terms the human never authorized.
Their three-layer fix:
- Deterministic policy boundaries: rates, minimum budgets and non-negotiables live outside the prompt as structured constraints; the agent can explain pricing but can't generate binding quotes below the floor.
- Proposal state vs. execution state: agents only draft deals (scope + rate + slot); nothing is booked until the human owner taps approve, even if two agents converge in four turns.
- Context compression: instead of static Linktree buttons and a PDF media kit, the agent dynamically pulls only relevant portfolio proof and availability for each visitor question.
The author asks the community how they handle authorization boundaries: strict state machines around tool calls, or verifier models.
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