Core Concepts

How Neo interacts with your code, data, and AI/ML workflows

How Neo Approaches Work

Neo is an execution-focused AI agent designed to operate inside your development environment. Instead of responding with isolated suggestions, Neo treats each request as a task that may involve multiple steps, files, and iterations.

When you give Neo a task, it plans the work, runs the required steps, and adjusts based on real results such as metrics, logs, and errors.

Agent Characteristics

Neo operates with a core set of behaviors that define how it works inside your project.

Context aware
Neo reads your project structure, code, and data during a session so it can make informed decisions instead of starting from scratch each time.
Self correcting
Neo detects errors in code execution, data processing, or model training and attempts reasonable fixes before continuing, while surfacing issues for review.
Iterative
Neo improves results based on feedback from validation metrics, evaluation plots, and runtime behavior. This allows it to refine feature sets, model parameters, or pipeline steps across iterations.
Transparent
Neo records what it does as it works. Plans, logs, generated code, metrics, and artifacts are written to your project and visible in the VS Code output panel so you can review or intervene at any time.

How Tasks Flow Through Neo

This section describes what happens internally when you send a task to Neo.

Submit a Task

Describe your goal in natural language using the Neo panel in VS Code.

Build a classification model to predict customer churn

Project Analysis and Planning

Neo inspects your repository and data to understand the environment before writing or running code.

Scans project structure and existing files
Identifies datasets, schemas, and formats
Creates a step-by-step execution plan

Pipeline Execution

Neo executes the planned steps inside your VS Code environment using your local setup and dependencies.

Data Load → Validation → Feature Engineering → Training → Evaluation

Results and Iteration

Neo generates concrete outputs and uses them to decide what to do next.

Model files such as Pickle or ONNX
Metrics, plots, and evaluation reports
Logs explaining decisions and adjustments

What Neo Can Access

Neo only works with what you explicitly allow inside your environment.

Local project files and folders opened in VS Code
Datasets referenced in your instructions or configuration
Existing code and scripts in the repository
External resources such as databases or cloud storage when credentials are provided

Neo does not silently modify files. All changes are written to disk and visible for review.

You can stop or adjust a task at any time during execution.

Supported Data Types

Neo can work with the following data sources:

Category	Formats
Tabular Data	CSV, Excel, Parquet
Images	JPG, PNG
Text	TXT, JSON
SQL Databases	PostgreSQL, MySQL
Cloud Storage	S3, GCS, Azure Blob
Audio	WAV, MP3, FLAC

AI and Machine Learning Capabilities

Neo supports both modern AI workloads and traditional machine learning tasks. The exact workflow and level of automation depend on your project structure, data quality, and available compute.

Tabular ML
Classification, regression, clustering
Computer Vision
Image classification, object detection, segmentation
Natural Language Processing
Text classification, named entity recognition, summarization
LLM fine tuning
LoRA based fine tuning and instruction tuning
Generative and agentic AI
Retrieval augmented generation (RAG) pipelines and autonomous agents

What Neo Is Not

Neo is not a conversational chatbot that only suggests code snippets.
Neo is not a black box that hides execution or decisions.
Neo does not replace your judgment or take control of your repository.

Neo is designed to assist with execution while keeping you in control of code, data, and outcomes.

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