AI Agents in Production: Failure, Cost, Control | CalQuity Labs

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Guides in this collection.

Understanding the problemYour AI agent needs a safe way to stopEach failure class must choose refusal, deterministic fallback, bounded partial output, or a human queue instead of forcing every uncertain exit into one shape.10 min readRead
Comparing the approachesChatbot, copilot, workflow, or AI agent?Classify systems by the external state they may change and increase permission, approval, and recovery controls with consequence.8 min readRead
Comparing the approachesWhat must an AI support handoff preserve?A handoff is complete only when verified identity, conversation, evidence, attempted actions, tool results, unresolved work, and risk flags reach an owned queue.9 min readRead
Judging the evidenceWhich customer support requests should you automate first?Start with requests that have an approved answer, accessible records, bounded authority and a staffed fallback.6 min readRead
Building and operating itHow to govern AI agent traces used for evaluationTraces kept for evaluation are a second production dataset carrying the same access, retention and consent obligations as the first. Teams govern the primary store and leave the trace store open, so the copy made for measurement becomes the weakest point.9 min readRead

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