AI Automation Project Structure

Project structure diagram

Use the button below to view the full AI automation project structure diagram inside this page.

View diagram

What this framework is

This is a clean folder structure for AI automation projects. It separates configuration, workflows, agents, prompts, tools, models, tests, logs, and reports so the project is easier to build and maintain.

Why teams use it: as projects grow, mixing everything in one folder becomes hard to scale. This structure isolates concerns and keeps development predictable.

Beginner view (simple meaning of each part)

Folder

Simple meaning

Example

config/

Project settings

Env values, API keys, logging config

data/

Input/output data files

CSV, JSON, expected outputs

src/

Main project code

Workflows, agents, tools

workflows/

Step-by-step automation flow

chat_workflow.py, rag_workflow.py

agents/

AI helpers with specific role

research_agent.py, data_agent.py

chains/

Linked AI steps

chain_factory.py

prompts/

Prompt templates

system prompts, few-shot templates

tools/

Extra capabilities

search, scraper, calculator

models/

Model connectors

OpenAI/Anthropic/Gemini clients

utils/

Shared helpers

parsing, file handling, logging

tests/

Code verification

unit + integration tests

reports/

Run summaries

metrics and HTML reports

logs/

Error/activity records

runtime and debug logs

scripts/

Runnable tasks

run_pipeline.py, cleanup scripts

notebooks/

Experiments

prompt_testing.ipynb

resources/

Reference assets

PDFs, docs, KB files

Developer-level architecture

Configuration layer: centralize env, provider keys, retry/timeouts, model selection.
Domain layer: keep workflows orchestration-focused; move reusable logic to agents/chains/tools.
Model abstraction: use model adapters + model factory so providers can be swapped by config.
Prompt management: keep prompts versioned outside business logic for rapid iteration.
Quality layer: deterministic tests with fixtures, plus report generation for accuracy/latency/cost.
Observability: structured logs per workflow step and tool call for easier debugging.

Typical request flow

Workflow receives request and context.
Relevant agent/chain is selected.
Prompt templates and model config are loaded.
Tools are called if needed (search/RAG/DB/etc.).
Model output is parsed and validated.
Results are returned and saved in logs/reports.

README-ready folder-by-folder documentation

ai-automation-project/
├── config/
├── data/
├── src/
├── tests/
├── reports/
├── logs/
├── scripts/
├── notebooks/
├── resources/
├── .env
├── .gitignore
├── requirements.txt
├── Dockerfile
├── docker-compose.yml
└── README.md

.env: local secrets and runtime environment values.
requirements.txt: Python dependency lock list for reproducible setup.
Dockerfile/docker-compose.yml: containerized and multi-service execution.
README.md: project overview, setup, usage, testing, deployment notes.

Best practices for this architecture

Do not hardcode model/provider keys in source files.
Keep prompts out of workflow business logic.
Avoid putting orchestration decisions inside tools.
Write unit tests for tools/utils; integration tests for workflows.
Track quality with reports (accuracy, latency, token cost).
Move notebook prototypes to `src/` when stable.