inside Agents

AI as a team member with a role, context and responsibilities

inside Agents are designed to bring AI from the chat window into your team. They carry out tasks, support processes, recognise patterns, communicate within the team and facilitate knowledge transfer. They collate content, check for connections, remind you of outstanding issues and summarise results – but they do not operate outside the rules. Their contributions are visible, documented in a trackable manner and are subject to the same review, approval and authorisation processes as the contributions of other team members. This ensures that AI-supported work remains transparent and compatible with human oversight and responsible use.

Agents als teamplayers

In inside, agents are not regarded as external tools, but as digital team members. They are assigned to a personal profile, a role, a team context and clear responsibilities. This makes it transparent what agents are used for, the context in which they work, and who is responsible as their ‘buddy’ for their configuration and deployment.

When an agent is created, it is specified which team they are assigned to, which tasks they are to undertake, and which communication and work areas are relevant to them. An agent therefore does not exist outside the organisation, but is integrated into existing structures: into teams, tasks, topics, review processes and approvals.

This is a striking difference compared to many AI applications. A chatbot often remains a lifeless input field. Workflow automation is a technical process running in the background. An Inside Agent, by contrast, is visible as an active participant in the flow of knowledge: it does not work in isolation. It works as part of a team.

Interfaces

The API key opens up access to external systems.

In order for agents to carry out tasks, they require controlled interfaces to external systems or technical services. The inside API key is the key that enables such connections to be established.

Through these interfaces, agents can, retrieve information, initiate processes, check data or bring results from external applications back into inside. They can be connected to workflow engines, analytics tools, data sources or other systems.

It is important to note that whilst the interface opens up technical possibilities, it does not automatically grant organisational independence.

What an agent is permitted to do is governed by roles, permissions, job descriptions and governance. In this way, technical automation does not become a blind process, but rather a controlled contribution within the team context.

The API key connects the agent to the outside world. Governance determines what may result from this connection.

Communication

Agents operate where teams are working.

Inside Agents do not just communicate via isolated input fields. They can be embedded in the communication channels where collaboration already takes place.

During a sprint, agents can take on tasks, provide status updates or contribute results to SprintTasks. This makes them part of the day-to-day work rather than a separate support tool.

In 1:1 chats, team members can speak directly to an agent, ask questions, clarify tasks or review interim results. This is particularly suitable for individual support, research, preparation or reflection.

In team chats, agents’ contributions are visible to multiple participants. An agent can provide guidance, summarise results, draw attention to outstanding issues or structure discussions. This ensures there is no hidden AI dialogue, but rather a transparent contribution within the shared workspace.

These changes transform the role of AI communication. It does not remain private within a chat window; instead, it becomes visible where coordination, evaluation and collaborative learning take place.

From an AI-generated response to a responsible contribution.

Inside Agents make results visible, verified and connected.

Results

The results generated by Inside Agents do not stay in the chat window. They can be filed under relevant topics, processed further and categorised by subject area.

There, answers, summaries or analysis results turn into contributions to the knowledge stream. They can be commented on, supplemented, checked and categorised under existing topics. This makes it clear what a result relates to, the context in which it arose and its significance for the organisation.

An agent can therefore carry out preparatory work: researching, collating, structuring, summarising or highlighting outstanding issues. However, their contributions do not replace professional approval. They follow the same review, approval and release processes as contributions from other team members.

It is only through review, evaluation and approval that an AI result becomes a reliable knowledge contribution. In this way, inside combines AI support with traceability, human oversight and responsible use.

Agent-Types

inside Agents can take on different roles. Not every agent is a chatbot. Not every agent is a process automation tool. And not every agent works directly with people.

Different types of inside agents help to distinguish between areas of application:

Tool agents carry out specific, recurring tasks. They initiate actions and return results to the sources in the form of status and log reports. They are similar to traditional automation, but are integrated into inside in a visible and traceable manner.
Service agents support research. They structure information and prepare results based on the contents of a research list.
Team agents operate within the context of a team. They understand roles, tasks and responsibilities, and deliver results to where collaboration takes place. They support communication, orientation and coordination.
System agents monitor technical or organisational systems. They identify patterns, report risks, monitor stability and can cooperate with other agents.
Guide, Coach and Mentor Agents support learning and development. They highlight connections, ask questions, provide guidance and help people to better understand, contextualise and apply knowledge.

inside Agents are not a single function. They form a role model for AI-supported teamwork within the organisation.

Scope: More than just a chatbot

A chatbot answers questions. This can be helpful, but it usually remains an isolated process. The response is generated during a conversation, disappears from the chat history and is not automatically visible or verifiable to others.

inside Agents go beyond this.

They operate within a team context. They can take into account roles, tasks, topics and responsibilities. Their results can be transferred to the relevant topic area, commented on, reviewed and approved.

The difference lies not only in the intelligence of the response. The difference lies in the integration.

A chatbot provides a response.
An Inside Agent contributes to the flow of knowledge.

Scope: More than just workflow automation

Workflow engines are powerful when it comes to executing processes. They connect systems, respond to triggers, transfer data and initiate actions. This is important, but very technical in nature.

The problem: Results often remain where they are technically generated. In logs, tickets, status fields or dashboards. Teams can see that something has happened, but not always what the business implications are.

Inside Agents can utilise workflow automation, but they don’t stop there. They bring results back into the team context. They can summarise, categorise, explain, flag outstanding issues and provide input for further work.

This makes inside the front end for AI-supported communication of results within the team.

Not: Process successfully executed.
But: Result understood, evaluated and made actionable.

Scope: More than just a dashboard

Dashboards display status. They show figures, traffic lights, progress, workload or deviations. This is useful, but it is no substitute for a coordinated interpretation.

A dashboard says: Something is red.
A team must clarify: Why is it red? What does that mean? Who needs to take action? What can we learn from this?

Inside Agents can provide support at this stage. They not only make status visible, but also help to translate results into communication. An Agent can highlight patterns, identify risks, explain correlations or prepare a decision-making template.

Inside is therefore not simply another dashboard. Inside becomes the place where results from AI, workflows and systems feed into the team’s knowledge flow.

Governance makes all the difference

AI does not become valuable to businesses simply by automating as much as possible. AI becomes valuable when its contributions are traceable, verifiable and accountable.

That is why inside Agents do not operate outside the framework of governance.

Their contributions are visible. They can be documented, commented on, reviewed and approved. Responsibilities remain clear. Decisions remain the responsibility of humans. Approvals follow defined processes.

This is particularly important when AI results are used in knowledge management, quality assurance, decision-making processes or organisational knowledge transfer. In these areas, a quick answer is not enough. It must remain clear how a result was achieved, what it is based on and who assessed it.

So inside Agents do not just help with automation. They help to make AI work suitable for organisational use.

Conclusion

AI does not belong alongside the organisation, but within your company’s knowledge flow.

Inside Agents bring together three levels: AI assistance, process automation and team collaboration.

They can carry out tasks, utilise interfaces, support processes and generate results. But their true strength only emerges through integration into inside: through roles, team context, communication, topics, review, acceptance and approval.

This ensures that an AI response does not become a fleeting chat history.
Automation does not become an isolated system process.
A status display does not become an unresolved interpretation problem.

Inside Agents make AI work visible, verifiable and connected. They bring AI to where knowledge is created, evaluated and further developed: within the team.