Read, extract, compare, and prepare
Useful where offers, contracts, onboarding files, or compliance inputs need structured extraction and review preparation.
Controlled AI Agent workflows
AI Agents for business processes.
Gavenir builds AI Agents that read information, structure data, prepare outputs, and help users move work through defined process steps.
Useful AI Agents are not generic chatbots dropped into a company. They need process scope, inputs, outputs, review steps, permissions, governance, and a clear connection to daily operations.
At Gavenir, an AI Agent is a process-specific workflow assistant that supports defined steps with clear inputs, outputs, review rules, permissions, and boundaries.
Definition
AI Agents as process-specific workflow assistants
At Gavenir, AI Agents support concrete business processes by reading information, structuring data, preparing outputs, and helping users move work through defined process steps.
The agent is not the whole strategy. The process comes first: what the agent should read, what it should prepare, who reviews the output, which systems are involved, and where the boundaries are.
For companies in Liechtenstein, Switzerland, and selected regional or international markets, the useful question is not whether an agent sounds advanced. The useful question is which process step it can support under control. A good first agent has defined inputs, a useful output, human review, permissions, and a clear handover into the next process step.
What agents can do
Agent capabilities are verbs inside a workflow
Capability language stays concrete: read, extract, structure, compare, flag, prepare, generate, export, route, support, and coordinate.
Read and extract
Read supplier offers or business documents and extract relevant information.
Compare and flag
Compare inputs, flag inconsistencies, and prepare issues for human review.
Prepare outputs
Prepare structured outputs, draft process documents, or export-ready records.
Support approvals
Prepare approval context and help users understand what requires attention.
Route information
Support handovers, missing-input tracking, and follow-up preparation.
Coordinate bounded steps
Coordinate selected workflow steps inside clear boundaries, permissions, and review rules.
Agent workflow types
AI Agents should be named by the work they support
Instead of presenting agents as a generic technology category, Gavenir frames them as controlled workflow components with a purpose, input, output, user, escalation path, and system boundary.
Guide users through a defined workflow
Useful where teams need help collecting inputs, checking completeness, preparing outputs, and moving work to the next step.
Classify, route, and track follow-up
Useful where internal requests, missing information, cases, or handovers need structured handling before human decision-making.
Find answers inside controlled sources
Useful where teams need retrieval from internal documents, procedures, archives, or process knowledge without exposing uncontrolled data.
Control levels
From assistance to selected execution
Read and explain
The agent reads documents or process data and provides structured information.
Prepare and recommend
The agent extracts, compares, flags issues, and prepares outputs for review.
Generate process outputs
The agent generates structured process outputs for review, export, or the next process step.
Coordinate workflow steps
The agent helps route information, prepare handovers, track missing inputs, or support follow-up steps.
Execute defined actions
The agent executes selected actions inside clearly defined boundaries, permissions, and review rules.
Operating model
A useful agent has data, tools, boundaries, and escalation rules
The visible design should make the operating model clear: what the agent can access, which tools it can call, what it may prepare, when a person reviews, and what gets logged.
Example workflow
Supplier quote to structured process output
A bounded first agent workflow can start with supplier offers and end with reviewed structured output for the next business process step.
- A user uploads one or more supplier offers.
- The AI Agent extracts supplier, line items, prices, delivery dates, and anomalies.
- The user reviews the structured output and asks follow-up questions.
- The workflow prepares a purchase requisition or ERP-ready output for the next process step.
How Gavenir builds agents
Design the agent around one workflow before expanding capability
The delivery path stays grounded in Gavenir's controlled process-first approach: start with one useful workflow, prove it, then expand carefully.
Design
Map the workflow, users, inputs, outputs, review points, escalation rules, and agent capabilities.
Build
Create the focused agent workflow with controlled data access, tool use, guardrails, and interface logic.
Launch with oversight
Use human review, limited permissions, user feedback, and quality checks before any deeper operational integration.
Improve and expand
Add capabilities, integrations, and channels only when the first workflow proves useful and controllable.
Governance and model setup
AI Agents need boundaries
Gavenir designs AI Agent workflows with human review, controlled access, and clear process boundaries. Agents can start without deep integration into core systems. Productive integrations, permissions, hosting, and model setup are aligned with the customer's IT and governance requirements.
- Human review for critical steps.
- Controlled access to documents, data, and systems.
- Limited integration first where appropriate.
- Clear process boundaries and permissions.
- IT involvement before productive use.
Related service paths
Keep agents connected to implementation and automation
Broader path
AI Implementation
Use implementation to connect agent work to process design, integration, governance, and daily operations.
Automation pathAI Automation
Use automation when agents are one mechanism inside a broader workflow improvement.
Discovery pathAI Consulting
Use consulting when the first agent opportunity still needs to be found, assessed, and scoped.
FAQ
Common questions about AI Agents
What is an AI Agent?
At Gavenir, an AI Agent is a process-specific workflow assistant that can read information, structure data, prepare outputs, and support defined process steps within clear boundaries.
Do AI Agents act independently by default?
No. Gavenir uses a controlled progression from assistance to selected execution. Human review, permissions, and process scope remain important, especially for critical business steps.
How are AI Agents different from chatbots?
A generic chatbot answers questions. A process-specific AI Agent supports a defined workflow with inputs, outputs, review steps, and process boundaries.
Can an AI Agent connect to ERP or other systems?
It can, when useful and appropriate. Many projects should start with limited integration, structured outputs, and review. Deeper integration comes after scope, value, and governance are clear.
What is a good first AI Agent use case?
A good first use case is a bounded workflow with repeated manual effort, clear input documents or data, a useful structured output, and a human review step.
What types of AI Agents can support business processes?
Common starting points include document agents, process support agents, back-office routing agents, and controlled knowledge agents. The right type depends on the workflow, input sources, review needs, and system boundaries.
How does Gavenir ensure agent quality and safety?
Gavenir defines scope, permissions, review rules, escalation paths, access limits, logs, and improvement loops before productive use. Critical outputs should remain reviewable by people.
How should a company start with AI Agents?
Start with one workflow where the agent can read defined inputs, prepare a useful output, and hand the result to a person for review. Expand only after that workflow is trusted.
Ready to explore where AI can help?
If your team spends time reading documents, preparing data, checking information, coordinating reviews, or moving work between systems, Gavenir can help you assess where AI could create value and what a realistic implementation could look like.