Introducing Intelligent Fields: Turn Every Conversation Into Structured Data

Today we’re launching Intelligent Fields, Maven AGI’s answer for capturing and organizing the data created in every customer interaction that can drive business decisions, upsell opportunities, product updates, and other critical strategic areas of your organization.

Your AI agent already reads every conversation. It resolves tickets, answers questions, and handles volume. But when the conversation ends, everything it learned disappears. No record of why the customer was frustrated. No flag that they mentioned leaving. No signal that the same issue has come up eleven times this week.

The transcript exists. The data doesn't.

The standard fix is manual tagging, custom fields in your helpdesk, agents filling them in. It works until volume spikes, new agents onboard, or the taxonomy drifts. Then compliance falls and the data becomes unreliable.

Here’s why manual tagging, the default answer most teams land on, doesn’t actually solve it.

The Manual Tagging Trap

Manual tagging has one fatal flaw: it depends on humans doing it consistently at scale. They don't. Volume spikes, new agents onboard, edge cases get skipped. Compliance falls and the data quietly becomes unreliable, often without anyone noticing until the reporting looks wrong.

But even perfect tagging hits a ceiling. Dropdowns capture categories. They don't capture context. The difference between a frustrated customer and a churning one isn't a tag. It's four sentences buried in the middle of a transcript.

What Actually Needs to Happen

Structured conversation data has to be extracted automatically, from the full transcript, using something that understands language, not keyword matching.

That's what Intelligent Fields does.

You define a field: a name, a type (text, yes/no, select, number, or multi-select), and plain-English instructions describing what the field should capture. The Maven agent evaluates it on every conversation, automatically. The result is stored, searchable, and tied to the conversation, with a confidence score and a rationale explaining the classification.

A Concrete Example: Churn Risk

Here's what it looks like in practice. You create a Churn Risk field, type Select, with three options: High, Medium, Low. The instructions are plain English: assign High if the customer mentions canceling, expresses frustration with billing, or has an unresolved issue across multiple exchanges.

That's the entire setup. Once published, the agent evaluates it on every conversation.

For a customer who wrote "I've been trying to get this fixed for a week and if it's not resolved today I'm canceling,” the field returns High, confidence 0.91, with a rationale: "Customer explicitly referenced canceling and has an unresolved billing issue across multiple exchanges."

That record is now queryable. Every conversation where Churn Risk = High in the last 30 days. Route it to a retention specialist in real time. Feed it to your CRM.

Setup Takes Minutes, Not Sprints

Everything lives in Agent Designer. Define a field in plain language, pick the type, publish. No code required. A full REST API is available for teams that want programmatic management.

What This Makes Possible

The support teams getting the most out of their AI agents aren't just using them to resolve tickets faster. They're using them to understand what's happening across every conversation, at a scale no human review process could match.

Intelligent Fields is how that becomes possible. Every conversation your agent handles becomes a structured data record. Every record is searchable. Every trend is visible.