---
title: "Sales Engineering AI agents"
section: "Agent store — Sales — processes"
canonical_url: "https://leverge.ai/agents/sales/sales-engineering"
category: "Sales"
process: "Sales Engineering"
publisher: "Ailoitte Technologies Private Limited"
---

# Sales Engineering AI agents

5 production agents automating sales engineering within sales. Each one runs today and publishes its inputs, steps and outputs.

## Agents in this process

### Acceptance Test Generation

URL: https://leverge.ai/agents/sales/sales-engineering/test-case-generation

Turn agreed requirements into acceptance tests the customer would sign off — each traceable to a requirement, prioritised, with the untestable ones named.

Steps: Reading the requirements → Writing the test cases → Writing the test plan note.

Returns: Requirement coverage; Test cases; Test plan note; Cannot be tested as written.

### Requirements Clarity Check

URL: https://leverge.ai/agents/sales/sales-engineering/solution-requirements-clarity

Take rough requirements from a discovery call and turn them into ones an engineer could build against — with everything still ambiguous written as a question rather than resolved by guessing.

Steps: Structuring the requirements → Writing the clarifying questions.

Returns: How buildable is this?; Requirements, restated; Ask the customer; Cannot be built as stated.

### Solution Blueprint

URL: https://leverge.ai/agents/sales/sales-engineering/solution-blueprint

Describe what the customer needs and get a solution architecture written against your own standards, with every component justified and every deviation from standard called out.

Steps: Looking up your architecture standards → Designing against the standards → Writing the blueprint.

Returns: Solution blueprint; Components; Against your standards; Risks and deviations; Standards applied.

### Solution Recommendation

URL: https://leverge.ai/agents/sales/sales-engineering/solution-recommendation

Score which solution components actually fit a customer's requirements, by feasibility rather than by what would be nice to sell.

Steps: Reading the component catalogue → Scoring component fit → Writing the recommendation.

Returns: Components by feasibility; Recommendation; Requirement coverage; Cannot be met; Catalogue entries used.

### User Story Generation

URL: https://leverge.ai/agents/sales/sales-engineering/user-story-generation

Paste discovery notes and get them turned into user stories with acceptance criteria — and an explicit list of what the notes do not say clearly enough to write.

Steps: Writing the stories → Writing the backlog note.

Returns: User stories; Acceptance criteria — highest priority story; Backlog note; Cannot write these yet.

---

Source: https://leverge.ai/agents/sales/sales-engineering — Leverge