Create an AI Agent That Writes, Executes, and Validates Python Code in Real Time

This tutorial shows how to power up an AI Agent with live Python execution and built-in result validation for tackling complex computational tasks. By pairing LangChain’s ReAct agent framework with Anthropic’s Claude API, it becomes possible to construct an end-to-end solution that writes Python scripts, runs them immediately, records outputs, maintains execution state, and checks results against expected properties or predefined test cases. This write → run → validate cycle supports creation of reliable data analyses, algorithm prototypes, and lightweight machine learning pipelines, offering confidence at each phase. It eliminates manual code reviews by verifying outputs on the fly and reduces development time, making prototypes more robust and reproducible.

First, install the necessary packages. Use pip to add langchain and langchain-core, which manage core agent orchestration, along with langchain-anthropic and anthropic for the Claude API integration. Make sure you run this in a compatible Python 3.8+ environment and set your API key via an environment variable. Confirm that these libraries load without errors before moving forward.

Next, import modules that anchor the agent’s functionality: os for environment variables, LangChain’s create_react_agent and AgentExecutor constructors, Tool for defining custom actions, and PromptTemplate for crafting chain-of-thought prompts. Add Anthropic’s ChatAnthropic client to bridge with Claude. Standard Python libraries—sys, io, re, json—handle I/O capture, pattern matching, and serialization, while typing supplies type hints to clarify function signatures and data structures.

The PythonREPLTool class provides a stateful in-memory Python shell. It intercepts arbitrary code—evaluations or statements—redirects stdout and stderr to internal buffers, and logs each run along with errors. After execution, it returns a structured summary with the code snippet, captured console output, exception traces if any, and the result. This detailed feedback ensures transparency for every snippet and makes debugging within the agent straightforward.

Building on PythonREPLTool, the ResultValidator class generates specialized Python routines that examine past outputs. It can verify numeric ranges, check the shape or type of data structures, or execute unit-test–style checks for algorithm correctness. The validator emits code that compares actual results against expected outcomes and reports a pass or fail status. This mechanism seals the loop on execute → validate by automating correctness checks after each code run.

Instantiate python_repl as the REPL tool, then attach ResultValidator to that instance. This configuration links code execution and validation in one session, ensuring variables and function definitions persist between runs. As you execute more snippets, the REPL history grows, giving the validator full access to previous outputs and context for comprehensive checks.

Convert python_repl and result_validator into LangChain Tool objects with descriptive names and detailed docstrings. The agent uses the python_repl tool to perform code execution steps and the result_validator tool to trigger validations. Clear descriptions help Claude choose the right tool at each reasoning step, guiding it through the code-and-check cycle.

Define a PromptTemplate that frames Claude as a two-step reasoning assistant. Each cycle includes a “Thought” line for planning, followed by a tool call. Placeholders for python_repl and result_validator names, along with usage examples, show Claude how to: analyze the task, call python_repl to execute code, call result_validator to confirm correctness, repeat as needed, and finally present a validated final answer. This structure enforces a disciplined write → run → validate workflow.

Create the AdvancedClaudeCodeAgent class to wrap all components into a single interface. Its constructor configures the Claude client with your API key, registers the two tools, and sets the custom prompt. It builds an AgentExecutor that drives iterative loops of think → code → validate. The run() method accepts user queries in plain language and returns Claude’s validated response. Additional methods—validate_last_result() for manual checks and get_execution_summary() for a high-level report on executed snippets—round out the API.

At runtime, instantiate AdvancedClaudeCodeAgent with your Anthropic credentials, then issue example tasks: prime number checks, sales data aggregation, algorithm implementation, and a simple ML pipeline. For each, the agent writes code, executes it via python_repl, verifies the results with result_validator, and outputs the final solution. After processing all queries, call get_execution_summary() to view total run count, successful validations, failed checks, and any error logs or tracebacks captured during execution. This demonstration showcases the live write → run → validate loop in a seamless AI-driven workflow.

Working with real-world data can be expensive, noisy, and restricted by privacy concerns. Synthetic data addresses these challenges and finds use in many domains:

• Large language models often incorporate AI-generated text into their training pipelines.
• Synthetic data supports fraud detection algorithms by providing simulated fraudulent patterns.
• NVIDIA recently unveiled Llama Nemotron Nano 4B, an open-source reasoning model optimized for high performance and efficiency in scientific computing, programming, and symbolic mathematics.
• Reasoning features play a core role in intelligent systems, and the OpenAI o1 release sparked interest in training reasoning models via large-scale reinforcement learning.
• Several websites do not offer affordable, user-friendly natural language interfaces, limiting conversational access to their content.
• Multimodal large language models (MLLMs) aim to merge visual information with logical reasoning in a single architecture.
• A recent tutorial walks through building a multi-tool AI agent with LangGraph and Claude, tailored for tasks ranging from mathematical computations to data processing.
• Large language models have demonstrated strong performance on coding challenges, but their program optimization potential remains underexamined despite early exploratory work.
• Another guide highlights Microsoft’s AutoGen framework, which lets developers orchestrate complex multi-agent workflows with minimal code using tools like RoundRobinGroupChat and beyond.
• Collaborative AI is an active research direction, and multi-agent systems driven by large language models are under review for cooperative problem solving.
• As organizations adopt AI assistants, evaluating their real-world performance—especially in voice interactions—remains critical, with current metrics focused on functionality over user experience.