Designing Agent Schemas That Actually Work: A Technical Deep Dive

The #1 Schema Mistake

Developers create schemas like this:

Template: customers
├── name (text)
├── status (select)
├── value (number)
└── notes (textarea)

Then they wonder why their agent:

• Writes to the wrong fields
• Misinterprets "value" as importance instead of revenue
• Doesn't know what "status" options mean
• Produces inconsistent outputs

The problem: No descriptions.

Agents aren't psychic. They need explicit context about what each field means, what values are valid, and when to use them.

The Description Field Is Everything

Same schema, properly described:

Template: customers
├── name (text)
│   └── "Company name. Use official legal name,
│        not casual references."
│
├── status (select: prospect, active, churned, paused)
│   └── "Customer lifecycle stage.
│        prospect = no purchase yet
│        active = paying customer
│        churned = cancelled subscription
│        paused = temporarily suspended"
│
├── annual_contract_value (number)
│   └── "Annual contract value in USD.
│        For monthly contracts, multiply by 12."
│
└── relationship_notes (textarea)
    └── "Free-form notes about the relationship.
         Include: key contacts, special arrangements.
         Exclude: data that belongs in specific fields."

Now the agent understands:

• What each field stores
• What values mean
• What format to use
• What to include and exclude

The "Explain to New Employee" Test

Write descriptions as if onboarding a smart new hire who knows nothing about your business.

If a new employee could understand it and make correct decisions, an agent can too.

Field Types: When to Use What

Text

Use for: Short, single-line content (names, titles, IDs)
Don't use for: Long content, structured data, options from a list

Textarea

Use for: Free-form multi-line content (descriptions, notes, summaries)
Don't use for: Data that should be structured or searchable

Number

Use for: Quantities, amounts, scores, rankings
Include in description: Unit (USD, hours, count), range expectations, calculation notes

Select

Use for: Single choice from fixed options
Critical: Document what each option means, not just list them

Reference

Use for: Linking to other entries
Example: Customer → associated Contacts, Feature → related Customer Segments

Reference Fields: Building Connected Knowledge

References are how agents reason across your data.

Without references:

• Agent knows Customer A
• Agent knows Feature X
• Agent can't connect them

With references:

• Feature X references Customer Segments it serves
• Customer A is in Segment "Enterprise"
• Agent reasons: "Customer A would benefit from Feature X"

Design principle: If you ever say "this relates to that," create a reference field.

Common Anti-Patterns

Anti-Pattern 1: The Junk Drawer

Bad:
Template: General Notes
├── content (textarea)
└── created_at (date)

Agents can't categorize or reason about undifferentiated notes. Create specific templates.

Anti-Pattern 2: Vague Field Names

Bad:
├── data (json)
├── info (textarea)
├── type (select)
├── value (number)

Even with descriptions, vague names lead to confusion. Name fields by their actual content.

Anti-Pattern 3: Missing Context

Bad:
├── mrr (number)  // What currency? Monthly or annual?
├── stage (select)  // Sales stage? Product stage?
├── score (number)  // Score of what? 1-10? 1-100?

Always include units, scales, and context in descriptions.

The Agent Comprehension Test

Before deploying a schema, test it:

1. Create 5-10 sample entries
2. Query the agent: "Describe what the [Template] template is for and what each field contains."
3. Verify understanding: Does the agent correctly explain your schema?
4. Test operations: "Create a new [entry] for [scenario]." Does it populate fields correctly?
5. Test retrieval: "Find [entries] where [condition]." Does it query correctly?

If the agent struggles, improve your descriptions.

The Investment Pays Off

Good schema design takes 2-3x longer upfront. But:

• Agent errors drop 80%+
• You never re-explain field meanings
• New templates follow established patterns
• Your knowledge base becomes self-documenting

One hour of schema design saves hundreds of hours of agent babysitting.