LLMs, Code Reasoning, and Tool Use | Generated by AI
Great question! You’re essentially asking how something like Anthropic’s Claude (and similar LLMs) internally handles:
- Code execution or code-related reasoning
- JSON schema checking/structured output validation
- Tool-use and routing between tools
Let me break it down in detail.
1. Code reasoning & execution
Claude (and other frontier LLMs) don’t actually execute code directly inside the model—they simulate code reasoning by predicting tokens of code or logic steps.
- In normal text mode, Claude can “write” code by continuing a pattern, but it doesn’t internally run it.
- When used with a backend (like Anthropic’s Workbench, or OpenAI’s Code Interpreter), the system can catch those code snippets and then actually run them in a sandboxed environment.
- The results can then be fed back to the model as context, so it can chain reasoning like:
→ Generate code → Execute externally → Receive output → Decide next step.
So the intelligence is in the loop: LLM generates → external runner executes → LLM interprets results.
2. JSON schema checking
Many APIs (Claude, OpenAI, etc) now support structured output mode, where you can attach a JSON schema. Here’s how it works internally:
- You pass a JSON schema definition with your request.
- The LLM is guided to output only valid JSON that conforms.
- If it tries to output invalid JSON, the system detects it (using a validator like jsonschema) and asks the model to “repair” the output.
- Under the hood, this is often a constrained decoding or grammar-based decoding mechanism—basically, restricting token generation so only
"valid next tokens in the schema"are allowed.
This ensures the output is syntactically and semantically correct JSON, which is critical for tool-use automation.
3. Tool-use & routing
Tool-use is handled via something like a router layer between the LLM and a tool execution environment:
- Routing logic: The model is prompted to decide which tool to call (or to respond directly).
- Schema enforcement: Each tool has an input schema. The LLM is required to produce valid JSON for that tool.
- Execution adapters: The orchestration layer (outside the model) takes the LLM’s JSON, validates against schema, executes the tool, and then feeds results back as context.
- Chained reasoning (ReAct-style): The LLM “thinks aloud” (might produce reasoning steps hidden from user), then issues tool calls when necessary, progressively arriving at a final answer.
For example:
{
"tool": "web_search",
"query": "latest research on quantum error correction"
}
→ Router sees "tool": "web_search", validates schema, runs web search, puts results back into model’s context, and the model continues reasoning.
Anthropic’s Claude, OpenAI’s GPTs, LangChain, and others all support this pattern.
🔑 Summary
- Claude code: Doesn’t run code internally; generates code → sandbox executes → results returned → iterative reasoning.
- JSON schema checking: Uses constrained decoding / schema validation to ensure valid structured output.
- Tool use routing: LLM selects tool calls in JSON, orchestration engine validates & executes them, feeds results back in loop.
👉 Would you like me to draw a diagram showing the flow (LLM ↔ tool router ↔ schema validator ↔ execution engine) so it’s clearer visually?