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Agentic Pipelines

Agentic pipelines have additional capabilities compared to traditional pipelines. They can have conditional branches or tools the LLM can call to gain additional capabilities. However, these pipelines remain predictable and repeatable.

Overview​

Unlike AI Agents, agentic pipelines contain a predefined sequence of steps executed in a deterministic way. You can think of them like flowcharts—you define exactly what happens in each step and the pipeline follows that path every time it runs. Even if there are conditional branches or tool calls, you define when and how they happen. Agentic pipelines are best for use cases when you know the optimal path and want to avoid the complexity of an AI Agent.

In deepset AI Platform, you can build agentic pipelines with conditional routing.

Conditional Routing​

In this setup, a Generator (an LLM) is paired with a ConditionalRouter component. The LLM analyzes incoming data and classifies it, routing the input to the most appropriate pipeline branch.

How It Works​

  1. The LLM receives the input data and based on it decides which branch to take.
  2. The LLM sends the data to ConditionalRouter, which forwards it along the appropriate route.

Example Use Case​

The LLM decides whether a query can be answered directly or needs additional data from a local database. It then sends the query to the ConditionalRouter, which forwards it along the appropriate route the LLM indicated.

When To Use It​

These types of systems are best if:

  • You need to handle different input types with specialized processing for each type.
  • You want predictable, transparent behavior with minimal complexity.

Pros​

Systems with conditional routing are:

  • Clear
  • Controllable
  • Transparent
  • Predictable
  • Easy to debug

Cons​

  • Limited flexibility compared to agent-based systems
  • No decision loop or iterative improvements