Agentic AI Platform
- AI
- Agentic Systems
- Automation
- Enterprise AI
Overview of the Client
The client wanted to implement artificial intelligence capabilities into the company's workflows, but faced a challenge: creating and configuring AI agents required technical expertise and the involvement of developers. This significantly slowed down AI experiments and limited its use to engineering teams only.
At the same time, various departments within the company needed tools for creating research AI agents, performing information searches, and working with internal knowledge. Therefore, the client needed a solution that would allow employees without a technical background to independently create and use AI agents, integrating them into workflows and the corporate knowledge base.
Challenge
As interest in using AI within the company grew, the problem of scaling such solutions arose. Even if individual AI tools are already in use, creating new agents or configuring AI processes requires the involvement of developers and lengthy configuration.
This significantly limits the speed of AI implementation in different departments. Each new task — whether it is researching information, working with documents, or analysing data — requires the development of a separate solution.
The company needed a universal solution that would allow it to quickly create AI agents for various tasks and integrate them into workflows without complex development.
Primary Objectives
The key objectives of the project included:
- developing a chat interface through which users can create and configure AI agents.
- the ability to design AI processes and workflows without programming.
- providing AI agents with access to internal and external data sources for searching, analysing, and processing information.
- developing tools for managing the deployment and configuration of AI agents.
- integrating AI agents into the company's existing workflows and corporate knowledge bases.
Project Overview
As part of the project, a platform was developed that allows AI agents to be created through a simple chat interface without the need to write code. Users can describe tasks in dialogue, after which the system automatically generates the agent's logic and the corresponding AI process.
LangGraph was used to orchestrate workflows, Crawl4AI was used to collect data from external sources, and Docling was used to process documents. The platform's infrastructure was deployed using Docker, which provides isolated agent deployment and simplifies system management.
Location: Europe
Sector: Enterprise Software / AI Platforms
Period: 5 months
Solution
To address the client’s requirements, we developed an agentic platform that allows users to build and operate AI agents without coding.
The system provides a conversational interface where users can define tasks, configure agent capabilities, and manage workflows. The platform automatically translates these instructions into agent logic and execution pipelines.
Agents can access documents, search internal knowledge bases, and gather information from external sources. They can also perform automated research tasks by collecting and analysing information from multiple sources. The platform also includes tools for deployment management, file control, and search capabilities.
By combining agent orchestration frameworks with document and web data processing tools, the solution enables users to design complex AI workflows while interacting with the system in natural language.
Platform Features
- Chat-based creation and configuration of AI agents.
- No-code workflow design for AI-driven automation.
- Integration with internal documents and knowledge bases.
- Web data collection and research capabilities.
- Deployment and lifecycle management of AI agents.
- File management and document processing tools.
- Search functionality across internal and external data sources.
- Support of external and local LLM and embedding models.
Technology Stack
To build the agentic AI platform, we used the following technologies:
Agent Workflow Orchestration
- LangChain
- LangGraph
Web Data Collection
- Crawl4AI
- Tavily
Document Processing
- Docling
- NumPy
- Pandas
Infrastructure & Deployment
- Docker
- Docker compose
Related Cases
- AI Chatbot
- LLM
- LangChain
- Java
- Objective C
- ERP Software Development
- AI
- LLM
- Automation
Core Team
- AI Engineer: Developed the agent architecture and implemented AI agent capabilities.
- Machine Learning Engineer: Configured and optimized agent workflows and data processing pipelines.
- Backend Developer: Built the platform backend and implemented the chat-based agent creation interface.
- Solution Architect: Designed the overall platform architecture and technology stack.
- QA Engineer: Tested the platform to ensure reliable agent behavior and workflow execution.
Impact
The developed platform significantly simplified the process of adopting AI within the organization. Teams can now create AI agents, design workflows, and deploy AI-powered tools without programming knowledge.
The chat-based interface lowers the barrier to AI adoption, allowing employees to integrate AI into research tasks, document processing, and internal workflows.
As a result, the client gained a flexible environment for experimenting with AI solutions and scaling AI usage across different departments without increasing technical complexity.