AI-powered chat applications answer questions, draft texts, and summarize information. In a business context, however, a convincing response is often not enough. What matters is whether the answer fits the actual task, takes relevant internal information into account, and makes its sources traceable.
This is where Agentic Chat comes in. Instead of processing every input according to a fixed sequence, the language model first analyzes the intent behind a request. It then decides which next step makes sense. It can answer directly, search organizational knowledge, or ask a follow-up question if the context is unclear. Agentic Chat does not describe a fully autonomous AI agent that controls arbitrary systems and independently executes business processes. Rather, it refers to a more flexible form of AI-supported dialogue that combines research, analysis, interpretation, and preparation according to the situation.
Why a conventional enterprise chat quickly reaches its limits
Many AI-powered business applications follow a fixed process: a user asks a question, the system performs a search, passes several results to a language model, and generates an answer from them. This approach is often referred to as Retrieval-Augmented Generation, or RAG.
This process can work well for clearly phrased knowledge questions. However, it becomes inflexible as soon as the task differs from the standard pattern. A greeting does not require a document search. An unclear question should not immediately trigger extensive research. And a complex analysis may not be solvable with a single search query. Follow-up questions also create challenges for conventional workflows. A user may first ask about a project and then request “the most important changes since the last status report.” In this case, the system must take the conversation context into account. An isolated search for the words “most important changes” would be unlikely to produce a reliable result.
The real operational problem therefore goes beyond simply finding documents. Employees often have to rephrase their information needs several times, review results, compare different sources, and then place the findings into a meaningful context themselves.
How Agentic Chat works
With Agentic Chat, the language model takes on greater responsibility for managing the conversation. It analyzes not only the wording of a question, but also the previous dialogue and the apparent intent of the user. For example, if a user asks, “What risks are there in Project Phoenix?”, important details may be missing. Does the question refer to technical risks, deadlines, costs, or contractual obligations? Is it about the current situation or the entire duration of the project?
In this case, Agentic Chat can ask a clarifying question before searching documents. If the refined request is then, “Which technical risks have been newly documented since the last steering committee meeting?”, the system can conduct a much more targeted search. Depending on the language model in use, the chat may also perform several searches in sequence. It could first identify the most recent steering committee meeting, then retrieve relevant risk documents, and finally compare the information found. Only after these steps would it generate a consolidated answer.
This step-by-step approach is often associated with reasoning models. These are language models that can process more complex tasks in several stages. They do not think like humans. However, they can plan a more extensive solution path, consider intermediate results, and derive additional search steps.
A realistic example from everyday work
A project manager is preparing for a meeting with senior management. She needs an overview of open risks, measures already agreed upon, and changes since the last monthly report. Without suitable support, she may have to search project folders, meeting minutes, tickets, and status reports individually. She opens several documents, compares different formulations, and transfers relevant statements into her own summary. This process takes time and creates a risk that important information may be overlooked.
With Agentic Chat, she can formulate the task as a specific question. The system can first clarify which period and which risk categories are relevant. It then searches the connected organizational information, evaluates suitable documents, and prepares the results in a structured form. The project manager does not receive an automatically binding decision. Instead, she receives a researched working basis that she can verify against the cited sources and use for her meeting.
The value does not come from the AI taking over the business process. It comes from connecting research, comparison, and initial preparation more closely.
Agentic Chat in INTERGATOR 6.8
INTERGATOR 6.8 introduces an agentic approach to chat. The user input is analyzed directly by the language model. Depending on the context, the model decides whether a search is necessary, whether it can answer directly, or whether it needs more information. This is particularly relevant in longer conversations and follow-up questions. The chat does not have to start the same search process for every message. It can take the existing context into account and adapt its research to the specific situation.
Organizational knowledge remains a central component. INTERGATOR is designed to retrieve relevant information from connected data sources that the user is authorized to access. General model knowledge can complement this information when no search is required or when it usefully supports the response. Users can also continue to restrict the scope of the research deliberately. Chat scopes can, for example, limit the conversation to selected documents, favorites, or defined document collections. Unlike in earlier workflows, however, such a scope no longer has to be selected before every conversation.
A clear functional distinction is important: in INTERGATOR 6.8, the chat primarily has access to INTERGATOR Search as a tool. It can use this tool to retrieve information and, depending on the model, perform multiple searches. However, it does not automatically control arbitrary business applications or execute freely selected actions in third-party systems.
Why source references are essential
In business knowledge work, an answer must not only be understandable, but also verifiable. This is especially important for contracts, technical requirements, policies, quotations, or project risks. A plausible-sounding statement is not sufficient. INTERGATOR 6.8 can therefore display the sources used as inline references directly within the chat response. Users can see which statements are based on which documents and move from the answer to the relevant source. An additional document list provides access to the key materials used.
This transparency supports professional review. It also helps users distinguish between statements based on organizational knowledge and supplementary general model knowledge. Source references do not solve every quality issue, however. A document may be outdated, contradictory, or incomplete. Review by responsible employees therefore remains necessary.
What business value does Agentic Chat provide?
The main benefit lies in shortening the path from an information need to a usable working basis. Employees no longer have to translate complex questions into perfect search syntax. They can describe their request in natural language and refine it through dialogue. This reduces repeated search attempts and makes it easier to work with large information repositories. At the same time, the chat supports employees in summarizing, interpreting, and preparing the information found for the next step in their work.
For organizations, this can create several benefits. Departments spend less time on manual research, managers receive structured overviews more quickly, and knowledge from different sources can be considered together more easily. The actual value still depends on the specific use case. A clearly defined process with relevant, current, and well-structured data provides a much stronger foundation than an unclear information environment without defined responsibilities.
Risks and limitations
Agentic Chat makes the use of language models more flexible, but it does not eliminate their fundamental limitations. Answers may still be incomplete or misleading. The model may formulate an unsuitable search query, fail to consider important documents, or assign the wrong weight to certain information. The model itself also influences the behavior. Some models are more willing to perform several search steps, while others respond more quickly and directly. More extensive reasoning processes can improve quality, but they require more time and computing resources. When external models are used, usage-based costs may also increase.
Organizational prerequisites also matter. INTERGATOR can only consider information that is connected, indexed, and accessible to the respective user. Existing authorization concepts remain in force. Missing access rights, outdated data, or systems that have not been connected therefore also limit the possible answer. Organizations should therefore introduce Agentic Chat as support for research and knowledge work. Professional decisions, approvals, and binding assessments remain the responsibility of the relevant employees.
Conclusion: More flexible assistance instead of autonomous process control
Agentic Chat develops the conventional AI chat into a context-sensitive form of research and work support. The language model can identify whether a direct answer is sufficient, whether a search is required, or whether additional context is needed first. Depending on the model, it can combine several research steps and prepare the results in a structured way.
INTERGATOR 6.8 uses this approach to integrate organizational knowledge more deliberately into the dialogue. Inline references and document sources improve traceability. Optional chat scopes also preserve the ability to restrict research to a defined set of information. The value does not lie in a fully autonomous AI agent. It lies in assistance that supports employees with research, analysis, interpretation, and preparation. Used appropriately, Agentic Chat shortens the path from a complex question to a verifiable and practically usable working basis.