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Mindbreeze · Agentic AIAgentic AI starts with the right context
Agentic AI is only as effective as the quality of the data and context it receives. Providing the right information at the right time minimizes token consumption and reduces the risk of hallucinations.
Illustrative example
In short
Agentic AI is only as effective as the quality of the data and context it receives. Mindbreeze InSpire performs a semantic pipeline, relevance model, RAG and fact extraction before any data reaches the LLM. This is Context Engineering: AI agents work with a small set of highly relevant, pre-verified facts, not with all the noise around. Every agent action is traceable, permission-aware and based on verified enterprise data. SoftAdvice, the Mindbreeze partner in the Benelux, designs the data foundation, connects the sources and builds the first use cases with your team.
Efficiency and cost control through relevance-first AI
Mindbreeze InSpire performs a semantic pipeline, relevance model, RAG, and fact extraction before any data reaches the LLM (Context Engineering). AI agents work with a small set of highly relevant, pre-verified facts, not with all the noise around.
AI agents work behind Mindbreeze Insight Touchpoints: employees focus on the goal, and Mindbreeze coordinates the right agents, tools, and enterprise data sources automatically.
About Mindbreeze Insight Touchpoints- Lower token consumption and cost
- Faster response times
- Higher accuracy
- Scalability
- Model flexibility
How AI agents get things done
Tools
AI agents are connected to enterprise data sources, APIs, and business systems. They gather information, trigger workflows, and select the right tools for each task.
Memory
Agents track what has already happened and capture the essence of each interaction, and draw on these memories when faced with a new challenge.
Planning
An AI agent breaks large goals into smaller, manageable steps and determines what needs to happen next, without waiting for human guidance.
Autonomous task execution
AI agents carry out multi-step tasks such as research, comparison, drafting, and summarization, without manual hand-offs between tools.
Cross-system orchestration
Instead of switching between CRM, ERP, intranet, and file shares, AI agents work across all connected systems from a single point of entry.
Governed and auditable AI
Every agent action is traceable, permission-aware, and based on verified enterprise data, even in industries with strict compliance requirements.
Knowledge-grounded reasoning
AI agents reason over your organization’s actual knowledge graph and documents, not generic web data, which reduces the risk of hallucination and increases trust.
The technology behind trusted agents
RAG and Agentic RAG
Retrieval Augmented Generation helps provide accurate, grounded answers based on trusted enterprise data. With Agentic RAG, Mindbreeze InSpire goes beyond simple responses by reasoning across information, retrieving the right content, and supporting users in completing complex tasks.
Multi-agent systems
A multi-agent system brings together specialized AI agents that collaborate on different parts of a task. One agent may retrieve relevant information, another may analyze or compare content, another may check context and permissions, and another may trigger a workflow or prepare the next action.
Knowledge graph
By creating a connected view of enterprise knowledge, Mindbreeze InSpire identifies relationships between people, documents, systems, and topics across the organization. By linking relevant data points, teams uncover insights faster and receive answers that reflect the full picture of their enterprise knowledge.
Questions about agentic AI
What is Context Engineering?
Agentic AI is only as effective as the quality of the data and context it receives. Mindbreeze InSpire performs a semantic pipeline, relevance model, RAG, and fact extraction before any data reaches the LLM, so AI agents work with a small set of highly relevant, pre-verified facts. This minimizes token consumption and reduces the risk of hallucinations.
What is Retrieval Augmented Generation (RAG)?
RAG, or retrieval-augmented generation, is a method that enhances the quality of generated texts of Large Language Models. With this method, relevant information is accessed from a database or a set of documents, and then text-based information is generated based on this retrieved information.
How can a company minimize data hallucination?
By combining company knowledge extracted by Mindbreeze with the broader linguistic comprehension of a Large Language Model. Generated outputs are then grounded in accurate and relevant company-specific information, and Mindbreeze provides the factual basis for the answer, so that any answer can be validated easily.
Is Mindbreeze limited to one AI provider?
No. Mindbreeze is designed to work across multiple AI ecosystems, so you are not locked into a single vendor.
What is a Large Language Model (LLM)?
Large Language Models (LLMs) are models in the field of AI. LLMs are trained with a large amount of content to understand natural language and generate human-like output and dialog.
More about Mindbreeze
Mindbreeze InSpire
The intelligence behind trusted Enterprise AI: one governed knowledge foundation across all your systems.
Read moreMindbreeze Insight Workplace
Mindbreeze Insight Touchpoints and Mindbreeze Insight Journeys: trusted AI agents and governed workflows, grounded in your enterprise data.
Read moreDeployment options and security
Cloud, hybrid or on premises, with enterprise-grade security and permissions enforced for every query.
Read moreWhich question should your first AI agent answer?
Let's look at your sources and your first use case together.