Build AI Agents with KNIME
Build, deploy, and govern AI agents based on all your available data that execute complex tasks with as much or little interaction as you want.
Easily deploy context-aware agents
Integrate your data, build your AI agents, then deploy, monitor, and govern them all with one intuitive tool.
Create custom AI agents easily with manageable building blocks
- Use visual workflows to build tools that can be used by agents to access information and perform tasks.
- Build agents of varying levels of flexibility and complexity that have access to internal and external tools and data sources.
- Enhance tools with GenAI capabilities such as summarizing documents, analyzing sentiment, and checking images using models from OpenAI, Azure, and other providers.
- Future-proof your agentic AI automations with flexible tools that keep working even if you change your underlying tech-stack.
Build smarter agents with well-integrated data and context
- Access data from any data source with KNIME’s 300+ built-in connectors to provide AI agents a complete picture and sufficient context from your data sources.
- Preserve meta information by connecting to original data sources rather than creating isolated copies that can also become outdated.
- Build specialized data integration tools to clean and transform data for ingestion aligned with how your agent needs it, saving time, reducing AI token usage and increasing reliability of agents.
Access and customize the all relevant LLM models in the industry to power your agentic applications
- Connect to and prompt large language models (LLMs), chat models, and embedding models provided by OpenAI, Azure OpenAI Service, Google, Anthropic, IBM Watson AI, Hugging Face, GPT4ALL, and DeepSeek.
- Combine data with LLMs as part of agentic workflows, build in custom business logic and prompts securely, and get more relevant results.
Create a custom repository of agentic applications and services
- Discover pre-built tools, AI workflows, and AI agents created by the KNIME community.
- Then use KNIME Hub to create your own repository of different tools to build AI agents.
- Build AI systems with workflows following the Model Context Protocol (MCP), deployed as a service that works from the Hub.
- Deploy your agentic workflows directly from the Hub as apps or services.
Build guardrails to ensure data and model governance in agentic workflows
- Prevent sensitive information being accessed by LLM providers with KNIME. Block requests including sensitive data or anonymize personally identifiable information before requests are made to GenAI tooling.
- Benefit from the full flexibility of KNIME workflows to customize the guard-railing in line with your requirements.
- Build in LLM-as-a-judge controls with KNIME Giskard nodes or manually orchestrate additional LLMs to check agent decisions and reduce risk.
Good Data Makes Good Agents
In this talk Michael demystifies agentic AI, breaking it down and explaining the key building blocks of tools, AI workflows, and agents. Watch the talk to also see how good data makes good tools and better agents and get some examples of agents built with KNIME workflows.
Michael Berthold (KNIME)
Access slidesMichael Berthold (KNIME)
Building AI Agents with KNIME – A Practical Walkthrough with KNIME Experts
Emilio demonstrates how to build an AI agent step by step in this practical session. Learn how to incorporate AI-driven decision-making into your KNIME workflows and build your first agentic workflow.
Christian Birkhold, Emilio Silvestri (KNIME)
Access slidesChristian Birkhold, Emilio Silvestri (KNIME)
Recommended resources
Good Data Makes Good Agents
March 2025 | Michael BertholdAgentic systems are only as good as the data they have access to.
Browse agent examples on KNIME Hub
Explore example agents and other learning materials to help you get started.