A near-match answer is how tenders are lost
If the library returns approximate matches, scoring becomes a serious problem. For example, a question about data residency answered from the general security paragraph leads the evaluator to treat the answer as not responsive. You learn that only when the results arrive.
Similarity is not enough to decide whether an answer addresses the question. You have to read both the question and the answer, and in some cases conclude that a well-written library answer is actually about a different subject.
The most important questions are the ones with no truthful answer in the library. Those questions are the bid team's job. A system that hides them behind a confident near-match has made the bid worse and has also looked like it helped.
If the matter involves security, compliance, or contractual commitment, it stays under review, no matter how certain the match looked, because that is where the liability lies.
How the agent is built in Agent Studio
Answers live in Notion and are reached through an MCP connection, so the agent searches the library rather than receiving a copy that could go stale.
The system prompt defines what counts as a responsive answer in your domain, and states that certain answer categories must always be reviewed by a human, regardless of confidence.
Prompt skills carry the boilerplate limits: the claims you may make, the claims you must never make, and the wording your legal team has already approved.
The flow does the mechanical work. Document Upload takes the RFP. PDF4me extracts it. A Generate Object node splits the questions into a structure. A Loop node then processes each question, and the Agent Chat node returns a match verdict for each one.
The output classifies items into three groups: taken from the library, adapted with a note on what changed, and not addressed. The Filter node then sends the unanswered group to Linear or Jira as bid work.
No submission happens until review is complete. Approved new answers then go back to the library, so the same gap does not appear in the next tender.
What this agent is built from
- MCP connections: live search over the answer library rather than a stale copy.
- System prompt: what counts as responsive and the categories that always require review.
- Prompt skills: approved phrasing and the claims you may never make.
- Document Upload (Data) plus PDF4me (Data): take the RFP and extract its contents.
- Generate Object (AI Core): pulls the questions out as a structure for the loop to walk.
- Linear (Integration): receives the unanswered questions as bid work. Jira fits the same slot.
- When an ActionFlow is enough: If the questionnaires are short and standard, with questions that repeat exactly, a lookup workflow is more reliable.
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