question

[AshrafGalibShaik]

how can i make an agntic flow with an ML model?

[Vivek-736]

Instead of just calling a model once (input → output), you let the system:

Plan → decide what needs to be done.

Act → use tools, call APIs, or query your ML model.

Reflect → check the result and refine.

Loop until it achieves the goal.

This is the style used in frameworks like LangChain, LlamaIndex, or Haystack Agents.

⚙️ How to Make One with Your ML Model

1. Wrap Your ML Model as a Tool

Whatever your ML model does (e.g., classification, prediction, summarization), expose it as a callable function.

Example in Python:

def classify_text(input_text: str) -> str:
# call your ML model here
prediction = model.predict([input_text])
return prediction

2. Define Agent Steps

Add logic that decides:

When to call your ML model.

When to call other tools (search, database, API).

When to stop.

This can be rule-based or controlled by a language model.

3. Use an Orchestration Framework (Optional but powerful)

LangChain Agents → LLM decides when to call your ML tool.

Haystack Agents → structured pipelines with retrievers + models.

Custom loop → just Python code that iterates until conditions are met.