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Expressions are how you reference and transform data inside step configurations. Anyreach supports two expression flavors:
FlavorSyntaxWhen to use
JSONata{{ ... }}Deterministic transformations: addressing fields, filtering arrays, basic math, type checks
AI${ ... }Free-text classification, extraction, generation: anything an LLM does well
You can use both inside the same step (different fields), but you cannot mix them in the same expression — see Condition step for the constraint.

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JSONata Expressions

JSONata is a query language for JSON. The minimum you need:
Want to…Write…
Get a top-level input field{{ n3.customer_email }} or {{ input.customer_email }}
Get a step’s output field{{ n3.body.id }}
Compare values{{ n3.score > 50 }}
Boolean logic{{ n3.score > 50 and n3.tier = 'gold' }}
Filter an array{{ n3.contacts[tier = 'gold'] }}
Map an array{{ n3.contacts.email }} (returns an array of emails)
Default value{{ n3.name ? name : 'friend' }}
String concat{{ n3.first_name & ' ' & n3.last_name }}
See JSONata cookbook for more.

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AI Expressions

${...} sends the contents (with {{...}} interpolations resolved first) to an LLM and replaces the expression with the response. 
"summary": "${a one-sentence summary of {{n3.transcript}}}"
"intent": "${classify the intent: billing, support, or sales. transcript: {{n3.transcript}}}"
"is_qualified": "${is the lead qualified? answer yes or no. transcript: {{n3.transcript}}}"
For AI to behave reliably, be specific about the response format. “answer yes or no” is much better than “is the lead qualified?”. See AI expressions for more.

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Pure vs interpolated

A field whose entire value is one expression is pure: 
"customer_id": "{{ n3.body.id }}"   → pure JSONata
"summary":     "${a summary of: {{n3.transcript}}}" → pure AI
A field with surrounding text is interpolated (the whole thing becomes a string): 
"message": "Hello {{ n3.body.first_name }}, welcome!"  → string

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Why this matters

Pure expressions preserve types — if n3.body.id is an integer, customer_id is an integer. Interpolated expressions always produce strings. This affects HTTP request bodies (where APIs may require integer IDs, not strings) and Output schemas (where validation enforces types).

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Evaluation order

When a step is about to run, the platform evaluates expressions in its config:
  1. JSONata is resolved first, top to bottom, against the current ctx
  2. AI is resolved next, with {{...}} interpolations already replaced
This means you can reference upstream steps inside AI prompts: 
"intent": "${what is the user's intent in this transcript? {{`Last turn`.body.text}}}"
The {{...}} is replaced with the actual transcript before the LLM sees the prompt.

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Where expressions are evaluated

Most fields in most step types support expressions. A few notable places:
StepExpression-evaluated fields
HTTP APIurl, headers (values), query_params (values), body (recursively)
Conditioneach condition’s condition
Outputeach output_data value
Actioneach operation_input value
Codenot in code itself — the code reads ctx directly
Code steps don’t use expressions inside the source code — your Python reads ctx natively.