AI agent development for your business workflows
An AI agent becomes useful when the agent can work with the information and tools your team already uses. Gateflow develops agents for an agreed business task, from gathering the required records to preparing a result or requesting approval for a change. The project starts with the work your team needs completed.
AI agent development with Gateflow
Gateflow is an IT company in Frankfurt that builds applications and connects business systems. Agent development brings those services together around a task with a recognizable beginning and end. The company works with teams across Germany, remotely or on site by agreement. Read about Gateflow to understand the services available alongside the agent project.
Business workflows that an agent can complete
Describe what starts the task, which information a person currently looks up and what counts as a finished result. Gateflow separates steps that follow fixed rules from steps requiring interpretation. This helps your team decide where a model adds value and where ordinary application logic gives a more predictable answer.
AI agent permissions before system access
Access is limited to the records and actions needed for the task. Your team decides whether the agent may read information, prepare a draft or change a record. Actions requiring approval stop for review. A test environment gives the team a place to check those boundaries before production credentials are introduced.
Tool integration for existing applications
The agent needs dependable ways to request information and report an unsuccessful operation. Gateflow builds tool integration around the available APIs and agreed permissions. A missing record, unavailable system or rejected request needs a visible response so the agent does not continue as though the required action had succeeded.
Business application interfaces for reviewing results
An agent also needs a practical place for people to review work. Gateflow can add business application interfaces that display the proposed result, supporting records and any missing information. Reviewers can then accept a draft or return the task for correction without searching through a separate conversation to understand the request.
Agent acceptance tests using real work
The test cases include examples supplied by your team and situations where information is incomplete or contradictory. Gateflow checks the result and the actions taken to produce that result. The acceptance record distinguishes completed checks from known limits, helping your team decide which tasks are ready for routine use.
AI agent handover and continued development
The handover explains configuration, permitted tools and the steps for investigating a failed run. Your team receives the agreed code and operating instructions. Further maintenance or additional tasks can form a separate scope. Changes to prompts, models or connected systems should be checked against the accepted cases before release.
Agent project scope and next steps
Bring one task description, representative records and the names of the systems involved. Gateflow uses these to discuss access, implementation effort and how your team will accept the work. The project process sets out responsibilities and handover arrangements before development begins, so the assignment has a defined result and decision owner. The estimate depends on the system connections and approval steps included in that first task; additional tasks are scoped separately.
Discuss your AI agent development
Gateflow develops an AI agent for your agreed business task, including system connections and approval steps. You will receive the implementation, acceptance-test results and operating instructions for the tasks included in the project.