5 Sales Engineering AI Agents, Live in Production.

Pre-sales engineering is scarce capacity spent largely on translation. Discovery produces notes, transcripts and half-formed requirements in the customer’s language, and someone has to convert them into something specific enough to architect against. Because that conversion is slow, it is often skipped: the solution is shaped from an experienced guess, and ambiguity is resolved by assumption rather than by asking. Those assumptions do not disappear — they resurface after signature as scope disputes, delivery overruns and the uncomfortable discovery that what was sold and what can be built are different things. The same scarcity means engineers are pulled into pursuits that were never viable, spending their best hours on deals the business could not have delivered.

These agents do the translation and leave the architecture judgement with the engineer. Requirements Clarity Check takes rough requirements from a discovery call and restates them into buildable form, writing everything still ambiguous as a question rather than guessing at it. User Story Generation turns discovery notes into user stories with acceptance criteria and an explicit list of what the notes do not say clearly enough to write. Solution Recommendation scores which components genuinely fit the requirements by feasibility, and names what cannot be met at all. Solution Blueprint then writes the architecture against your own standards, justifying each component and calling out every deviation from standard rather than burying it. Acceptance Test Generation closes the loop by turning agreed requirements into tests the customer would sign off, each traceable to a requirement, with the untestable ones named instead of quietly dropped.