AI Agent - Tools Frameworks, Agentic AI, KI-Agenten, Multi-Agent Systems

AI agents – The most important multi-agent tools and frameworks

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AI agents can work together and carry out actions independently. This enables them to achieve better results than normal generative AI solutions. We show the most important tools and frameworks for creating AI agents with and without coding.

Part 1: AI agent tools without coding

If you want to use AI agents without programming, there are already a number of tools to choose from, from open source to commercial. Agents can be clicked together with simple web interfaces. You define the logic and behavior by telling the agents what their tasks are and how exactly they perform them using system prompts or low-code-editors with programming-like building-blocks. The agents are also given capabilities (usually called “actions”), which can be used to search the Internet or access tools, for example. In a playground, you can then give the agent system a task and see how the agents work out the solution among themselves. The most important solutions are:

  • AutoGen Studio
  • Relevance AI
  • Microsoft Copilot
  • Agentforce
  • Other AI agent tools

AutoGen Studio

The open source tool AutoGen Studio is provided free of charge by Microsoft. It is really easy to create AI agents without code in a web interface. The sophisticated AutoGen agent framework is used in the background. You can try out initial showcases with a click, e.g. an agent team that researches current share prices on the web and uses them to create a chart. Agents can even execute code and thus evaluate data, create charts, perform complex calculations and much more. The most important LLMs can be connected, e.g. GPT (via OpenAI API or your own Azure instance), Claude and Google Gemini. You can also provide the agents with your own skills, which you code using Python. A Python environment and API keys for the LLMs you want to use are required for installation. This means that the solution can only be used with some tech skills for the installation, but it is mature and free of charge.

  • Website: https://autogen-studio.com/
  • Costs: free of charge (open source)
  • Distribution: very high ★★★
  • Special features: Easy to use UI, installation with Python

Relevance AI

Relevance AI has been developing AI agent systems since 2018. The web-based platform offers a no-code environment with a visual interface that even non-programmers can use to create complex AI agents within minutes. Hierarchies of agents can be put together. An integrated vector store enables fast response times. The platform focuses on data security and data protection through SOC 2 certification and GDPR compliance.

Microsoft Copilot

Microsoft’s vision is to support companies with AI agents in all processes and thus make AI a profitable part of the company for automation. Microsoft Copilot is the most important AI solution for many companies. This is because it is integrated into Word, PowerPoint, Outlook, Teams, Excel, Sharepoint and the Azure cloud solution. Copilot offers high security standards thanks to its integration into Microsoft’s world. Admins can release Copilot for each user and control rights granularly. Copilot agents can be easily created and linked to company documents. This means that important documents can be found quickly via Teams chat and company knowledge can be used. The other professional solutions Copilot Studio and Power Automate even make it possible to create complex agents that work with all company applications (e.g. Dynamics CRM, SAP, Salesforce) and other Copilots.

  • Website: https://www.microsoft.com/microsoft-365/copilot
  • Costs: approx. 30€/month per user (Microsoft 365 Copilot) Copilot Studio and Power Automate
  • Special features: Integrated into all important Microsoft tools and technical company ecosystem (Sharepoint, Azure), highest security standards, Copilots can interact with all tools via over 1,400 connectors.
  • Distribution: very high ★★★
  • Learn more: Keynote from Microsoft CEO Satya Nadella for Ignite 2024 on Microsofts AI Vision

Agentforce 2.0

CRM manufacturer Salesforce offers its own agent system, Agentforce, which enables helpful agents to support marketing, sales and customer service and collaboration. The marketplace contains predefined agents that can be used directly. Agentforce AI agents can work with tools from the Salesforce universe, e.g. Salesforce CRM, Tableau, Slack and others.

 

Other AI Agent Tools

  • AgentGPT – Create agents via prompt, 20 example agents, simple UI
  • SuperAGI – Agent tool specializing in marketing and sales
  • make.com – Popular automation tool with numerous connectors and AI agent capabilities

 

Part 2: AI agent frameworks for developers

For developers and AI specialists, programming frameworks are the best way to integrate AI agents into their own systems or create new tools. There are several mature AI agent frameworks to choose from. The most important solutions include the following frameworks:

  • AutoGen
  • LangChain
  • CrewAI
  • Semantic Kernel
  • Other AI agent frameworks

AutoGen & Magentic-One

AutoGen is developed by Microsoft as open source software and makes it possible to create agent systems using code. There are libraries for Python, C#/.NET-Framework, Java and Javascript. Since 11/2024, there has also been the further development Magentic-One, which is specially designed for multi-agent systems and provides more structure for the requirements of complex multi-agent systems thanks to an improved, modular architecture.

LangChain & LangGraph

LangChain is a popular open source framework with extensive components for LLM-supported applications. It supports all LLM providers and has a large, active community. LangGraph Platform (commercial) also provides a scalable platform for live requirements (own hosting in EU/US cloud possible). Agent workflows can be visualized in LangGraph Studio.

  • Website: https://www.langchain.com/
  • Cost: free of charge (open source)
  • Distribution: very high ★★★
  • Special features: High distribution, many components, professional tools available (LangSmith, LangGraph), Pydantic-compliant

CrewAI

CrewAI is a mature and widely used framework for the development and integration of AI agents in your own applications. The framework can be used free of charge, the enterprise version offers sophisticated tools and extensions, e.g. connectors to 700 applications, a no-code UI studio and integrated training and testing tools. There are many free online videos for learning.

  • Website: https://www.crewai.com/
  • Costs: Free version: free of charge (open source), enterprise version: price on request
  • Distribution: very high ★★★
  • Special features: High distribution, enterprise tools available
  • Learn more:

Semantic Kernel

Semantic Kernel is an open-source SDK from Microsoft that enables the integration of LLMs like OpenAI, Azure OpenAI, and Hugging Face with programming languages such as C#, Python, and Java. With the “Semantic Kernel Agent Framework“, you can enhance agent capabilities. You can create plugins that allow agents to perform actions like sending emails, web research, fetching CRM data, etc. Using planners, an LLM can autonomously create and execute plans to achieve specific goals (e.g., conduct web research first, then outline an article, then write the paragraphs). Semantic Kernel is a mature framework ready for enterprise use.

Example of a text creator agent in Semantic Kernel. The agent has functions like brainstorming, email creation, translation and more. The planner automatically selects the right functions depending on the task.
Example of a text creator agent in Semantic Kernel. The agent has functions like brainstorming, email creation, translation and more. The planner automatically selects the right functions depending on the task.

 

Other AI agent frameworks

  • AutoGPT – One of the first popular agentic solutions that allows you to create, deploy, and manage continuous AI agents that automate complex workflows
  • OpenAI Swarm – OpenAI’s open source framework for agents, currently still experimental
  • llama-agents – An open source agent framework based on the popular LlamaIndex framework
  • smolagents – A lightweight open-source agents framework by HuggingFace
  • Multi-Agent Orchestrator – An open-source agents framework by AWS for TypeScript and Python

 

What are AI agents?

Agent systems are seen as the next big AI wave and promise major advances in AI. The agent concept means that AI agents are specialized in certain tasks. They can also plan, execute and coordinate tasks independently. They differ from traditional AI systems in that they not only execute individual instructions, but also carry out complex processes independently.

The strength of such agents lies in their ability to combine different tools and services – from web research and data analysis to the generation of graphics or reports. The ability to coordinate several agents in a team enables them to handle even more complex tasks that go beyond the capabilities of a single agent. The decisive factor here is that AI agents not only process information, but also make context-related decisions and dynamically adapt their working methods to given goals.

Examples of AI agents

  • Market research agent: An AI agent can, for example, carry out market research by performing Google/Bing searches, collecting and analyzing the results, evaluating interim results and adapting its strategy accordingly. It can coordinate with other agents who take over parts of the task, such as checking the research or preparing it as Word or website text.
  • Research agent: Systematically searches scientific databases, extracts relevant research results and creates compact summaries on specific topics. Ideal for scientists and research teams.
  • Customer service agent: Analyzes customer queries holistically, accesses company databases and generates context-related solutions. It can diagnose faults, create repair instructions and complete service processes.
  • Financial agent: Monitors stock markets in real time, evaluates company balance sheets and generates data-based investment recommendations. It combines information from various sources to develop precise investment strategies.

Frequently asked questions about AI agents

What distinguishes an AI agent from a normal AI chatbot?

An AI agent (also “agentic AI” or “multi-agent AI”) goes far beyond the functionality of a chatbot. While a chatbot typically responds to direct requests, an AI agent can plan tasks independently, combine different tools and carry out complex processes autonomously. It works proactively, makes context-related decisions and dynamically adapts its strategy to achieve a defined goal. One example would be an agent that not only collects market data, but also analyzes and interprets it and generates a strategic report.

What are the prerequisites for using AI agents?

The use of AI agents varies depending on their complexity. For no-code platforms such as AutoGen Studio or Relevance AI, companies primarily need: basic digital knowledge, skills in selecting and setting up an agent tool, API access to AI models (OpenAI, Google Gemini, Claude), clearly defined tasks and, above all, time to implement the AI use cases and support them in productive operation. For advanced implementations with developer frameworks such as CrewAI or LangChain, Python programming skills and a deeper understanding of AI technology are required.

Do AI agents also make mistakes?

Yes, AI agents can certainly make mistakes. They are based on probabilistic models that use probabilities to make decisions. Professional frameworks therefore integrate mechanisms such as multiple agent validation, error checking and step-by-step verification to minimize error probabilities. However, AI agents could offer higher accuracy and reliability in more complex tasks due to their more advanced capabilities. Valid studies are still pending.

What are the particular safety risks of AI agents?

AI agents pose potential risks that need to be systematically addressed:

  • Unintentional data leaks due to unlimited web searches or tool access: it would be possible for an account to be locked due to too many agent logins. Or the agent decides to reset the password, thereby locking out users.
  • Possible errors in complex decision-making processes
  • Risks of bias in the underlying AI models (discrimination)
  • Potential misuse due to improper configuration

Professional solutions such as Microsoft Copilot or Salesforce Agentforce offer integrated security mechanisms such as granular access rights, data filters and compliance controls.

For which industries are AI agents particularly interesting?

AI agents show great potential in these areas in particular:

  • Sales and marketing (automated customer analysis)
  • Customer service (24/7 support with context-understanding solutions)
  • Finance (market analyses, risk assessments)
  • Research and development (data analysis, literature research)
  • Logistics (process optimization, resource planning)

How expensive are AI agents for small and medium-sized enterprises?

The costs vary greatly:

  • Free open source solutions (AutoGen Studio)
  • No-code platforms from €19/month (Relevance AI)
  • Enterprise solutions from €30 per user (Microsoft Copilot)
  • Individual developments with developer frameworks: variable costs

Small companies can start with free versions and scale up later.

Good sources – Learn more about AI agent systems

  • DeepLearning.AI – Python-based courses to try out agent frameworks directly, e.g. on OpenAI GPT, MemGPT, crewAI, Haystack, LangChain, AutoGen, LlamaIndex
  • Agents – A Google whitepaper with short LangChain tutorial

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