AI agents that  actually ship revenue

Where AI projects stall before production, and what we do about it

Eight problems account for most of the AI work that demos well and never ships. Each one has a fix we run as standard.

• A demo mistaken for a productAn impressive first prompt is not a system. Production needs evaluation, guardrails, fallbacks and somebody on call.
• No baseline to compare againstNobody measured how the task is done today, so nothing can be shown to be better. We measure the current process first.
• Quality asserted, never measuredChanges shipped on the basis that the output looked good. An evaluation set exists from the first prototype.
• Retrieval over messy source materialAnswer quality is capped by content quality. Contradictory and outdated documents stay contradictory once indexed.
• Agents with no action boundaryA model given tools and trusted to be sensible. Every action gets a constraint, an audit trail and a human handoff path
• Prompt injection unaccounted forUser content and retrieved documents treated as trustworthy instructions. Input validation and output filtering are part of the build.
• Token costs found on the invoiceUsage that looked trivial in testing and is not at production volume. Cost tracking goes in with the tracing.
• Drift nobody is watchingBehaviour changes under a model update and nobody notices for a month. Monitoring and a runbook ship with the system.