Imagine asking an AI system, “Book me a flight, update my calendar, and notify my manager,” and watching it carry out everything by itself. That is the power of agentic AI, capable of functioning as self-reliant assistants that use tools, workflows, data, and large language models (LLMs) to complete tasks. As businesses evolve beyond basic chatbots, the trend of “tool stacking” has accelerated in 2025.
This article from PIT Solutions explores how developers can now integrate RPA bots, retrieval systems, LLMs, and other tools into seamless workflows using AI orchestration frameworks.
What Is AI Tool Stacking In Enterprise AI Solutions?
When different AI services such as LLMs, knowledge bases, APIs, and automation scripts are combined to create intelligent workflows, the process is called AI tool stacking.
By 2025, instead of manually coordinating each service, developers automate generative models using layers that dynamically interact among models, data pipelines, RPA bots, and rules engines based on the workflow’s needs.
When a complex objective is received, a central LLM (such as GPT-4, Claude, or Gemini) employs frameworks like LangChain or Microsoft AutoGen to break it into smaller steps and then distribute calls to retrieval systems, RPA bots, or custom APIs. The LLM acts as the brain or conductor making the decisions, while the tools handle the specific tasks as per the requirements.
Key Components of an Agentic AI Stack
Agentic AI systems are composed of several layers that work together:
Large Language Models (LLMs): These form the brain of the AI system. Models like GPT-4, Claude, Gemini, or open-source options such as Mistral and LLaMA 3 can interpret natural language, understand intent, and trigger appropriate actions through function or tool calls. Modern LLMs now support such capabilities natively, making it easier to link them with external tools.
Orchestration & Agent Frameworks: This layer manages how all processes are coordinated, errors corrected, and agents connected. Frameworks such as LangChain, CrewAI, AutoGen, and Haystack enable multi-step or multi-agent workflows. One example is LangChain 2025, where the roles of planner, executor, communicator, and evaluator are combined under one layer to handle tool routing, memory, parallel execution, and error recovery. Platforms like Vertex AI Agent Builder and Google’s Agent Development Kit (ADK) provide managed enterprise solutions for these functions. Agent collaboration can follow standards such as the Agent2Agent protocol, while custom behaviors are implemented through ADK (through Python SDK).