Code Execution with Assistants

Code execution with AI Assistants allows an assistant to run code as part of its reasoning and output generation, instead of relying purely on natural-language reasoning. In Aimogen, this capability is exposed through the Assistants API v2 and is tightly controlled, optional, and scoped.

This is a power feature. It should be enabled deliberately.

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What Code Execution Means #

When code execution is enabled for an assistant, the assistant can:

• run small programs or scripts
• manipulate structured data
• perform calculations
• transform datasets
• validate or normalize inputs
• generate derived outputs programmatically

The assistant still responds with text, but part of its reasoning happens through actual code, not simulated logic.

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What Code Execution Is Not #

Code execution is not:

• arbitrary server access
• shell or OS-level execution
• PHP execution inside WordPress
• database access
• file system browsing
• persistent background processing

It runs in a sandboxed execution environment provided by the AI provider, not on your WordPress server.

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Enabling Code Execution #

Code execution is enabled per assistant.

Path:
Aimogen → AI Assistants → Edit Assistant → Tools

You must explicitly enable code execution.
If it is disabled, the assistant cannot execute code, regardless of instructions.

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How Code Execution Is Used #

Code execution is invoked by the assistant internally, not by users directly.

Typical flow:

1. assistant receives structured input
2. assistant decides code is needed
3. code runs in the sandbox
4. results are returned to the assistant
5. assistant formats the final response

Users only see the final output, not the raw code unless you design it that way.

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Common Use Cases #

Code execution is useful when tasks involve:

• numerical calculations
• data aggregation
• sorting and filtering
• statistics
• table generation
• format conversion
• validation of AI-generated data
• logic that would be fragile in pure text

Examples:

• calculating totals or averages
• generating comparison tables
• validating CSV-like data
• transforming scraped content into structured formats
• checking constraints before producing output

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Code Execution vs AI Reasoning #

AI reasoning:

• probabilistic
• language-based
• good for explanation and synthesis
• bad at exact math and strict logic

Code execution:

• deterministic
• exact
• repeatable
• reliable for logic and calculations

The two are complementary. Code execution removes entire classes of AI error.

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Code Execution in Chatbots #

When used in chatbots:

• code execution happens transparently
• responses may be more accurate
• calculations are reliable
• complex logic can be enforced

This is useful for:

• pricing calculators
• eligibility checks
• rule-based recommendations
• technical assistance

Chatbot personas and workflows still apply.

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Code Execution in OmniBlocks #

In OmniBlocks, assistants with code execution enabled can:

• process structured outputs from previous blocks
• compute derived values
• validate data before passing it forward
• act as logic-heavy transformation steps

This often replaces large prompt-based logic with small, reliable code steps.

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Scope and Limits #

Code execution:

• runs only when the assistant decides it is needed
• is limited in runtime and resources
• cannot persist state between runs
• cannot access WordPress internals
• cannot call external APIs unless explicitly allowed by the provider

It is designed for short, bounded tasks.

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Cost and Performance Considerations #

Using code execution:

• may slightly increase latency
• may increase API cost
• often reduces retries and errors
• can replace multiple AI calls with one reliable step

For logic-heavy workflows, it usually reduces total cost.

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Safety and Predictability #

Because execution is sandboxed:

• it cannot damage your site
• it cannot access sensitive systems
• failures are contained

If execution fails:

• the assistant falls back to text reasoning
• an error may be logged
• the workflow can continue or terminate gracefully

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Designing Assistants That Use Code Well #

Good practices:

• enable code execution only when needed
• keep assistant instructions explicit
• let code handle logic, AI handle explanation
• avoid asking AI to “simulate” math
• combine with OmniBlocks for structure

Poor practice:

• enabling code execution “just in case”
• using code for trivial text tasks
• assuming code execution replaces workflows

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What Code Execution Does Not Replace #

It does not replace:

• OmniBlocks execution logic
• triggers and workflows
• placement rules
• chatbot personas
• business rules enforcement
• security validation

It is a tool, not a system controller.

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Common Mistakes #

• enabling code execution without a use case
• expecting access to WordPress or PHP
• relying on code execution for long tasks
• assuming results persist across runs
• mixing business logic into assistant instructions

Code execution should be boring, not clever.

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Best Practices #

Use code execution for precision, not creativity. Let assistants calculate, validate, and transform data programmatically, then let AI explain or contextualize the results. Keep execution bounded, purposeful, and test assistants thoroughly in the backend Playground before using them in production.

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Summary #

Code Execution with Assistants in Aimogen allows AI assistants to run sandboxed code for precise, deterministic tasks such as calculations, data transformation, and validation. Enabled per assistant and executed in a controlled environment, it complements AI reasoning rather than replacing it. Used correctly, code execution dramatically improves accuracy, reliability, and scalability in logic-heavy chatbots and OmniBlocks workflows without compromising safety or control.