AI agents · Automation · Integration · Delivery
AI agents that get tasks done reliably.
An AI agent does more than answer questions. It pursues a defined goal, uses approved data and tools, and carries out multi-step tasks across different systems.
I develop focused agents that fit real processes – with clear boundaries, verifiable results and human approval wherever it is needed.
What is an AI agent?
AI agents carry out tasks independently – within clearly defined boundaries.
An AI agent works towards a defined goal with the context it needs. A language model assesses the situation and determines the next step. Through defined tools, the agent can read data, call APIs or perform concrete actions.
- 01
Goal and context
The task, relevant information, expected result and stopping conditions are described unambiguously.
- 02
Planning and decisions
The model assesses the current state and decides which next step makes sense within the given constraints.
- 03
Tools and systems
Purpose-built tools connect the agent to databases, ERP, commerce, documents, APIs or development platforms in a controlled way.
- 04
Control and accountability
Permissions, validation, logging and human approvals limit what the agent is allowed to see and execute.
The agent is developed with an Agent Development Kit (ADK). I use Google ADK among other options. It structures the model, tools, state, workflows and tests – but the technology always follows the specific use case.
Concrete use cases
Where AI agents can take on work.
Good candidates are clearly bounded tasks that combine information from multiple sources, coordinate several steps or make decisions within defined limits.
- 01
Handle customer enquiries
Enquiries are classified, checked against customer and order data, and answered. Complex cases are passed to the responsible person with the complete context.
- 02
Capture orders
Emails and order documents are read, details are validated, and orders are entered into the ERP. Missing or contradictory information is requested specifically.
- 03
Check invoices
Invoices are matched against orders, goods receipts and contract data. Any discrepancies are documented and reported.
- 04
Monitor delivery dates
Open orders, stock levels and delivery commitments are checked continuously. When something deviates, the agent informs the responsible teams or proposes the next steps.
- 05
Keep projects in view
Information from tickets, meeting notes and project plans is combined. Status, open decisions and risks remain current and visible.
- 06
Prepare decisions
Internal documents and approved external sources are searched and compared. The result is a traceable basis for decisions, complete with source references.
Approach
From a clear task to a production agent.
Not every automation needs an agent. The process therefore starts with business value and only ends when behaviour, integration and operation are dependable.
- 01
Bound the use case
The goal, trigger, expected result, variants, risks and required approvals are clarified. A small, measurable process is the best starting point.
- 02
Develop the agent and tools
An ADK structures the model, instructions, state and workflow. Suitable tools connect the agent to the required systems through clearly defined interfaces.
- 03
Test behaviour systematically
Real examples, edge cases and unwanted actions become evaluations. Permissions, validation and approvals protect critical steps.
- 04
Integrate and improve
The agent is embedded in the real workflow. Execution, tool calls, quality, cost and exceptions remain observable and are improved deliberately.
Impact
What a well-bounded agent changes.
Value comes from dependable work in the process – not from making the most general AI demonstration possible.
Fewer manual handovers
Information no longer needs to be copied between email, spreadsheets and operational systems.
Faster response
Recurring checks and preparations start as soon as the relevant event occurs.
Traceable workflows
Inputs, decisions, tool calls and results can be logged and reviewed.
Expandable in stages
A focused agent can grow through additional tools, data sources and tasks in a controlled way.
Technology in use
Develop AI agents like software.
I use the Google Agent Development Kit (ADK) to develop AI agents. This code-based open-source framework supports the entire path from development and evaluation to production operation.
- Clear roles and workflows
Goals, instructions, tools and operating boundaries are defined in code. Several specialised agents can also be connected into a shared workflow.
- Connect systems and data
Custom functions, APIs and MCP servers (Model Context Protocol) give agents targeted access to existing applications and data.
- Test behaviour and operate
Workflows can be tested, evaluated and traced. This makes errors visible early and allows agents to be moved into production in a controlled way.
Ways of working
Where we can start sensibly.
The first step stays manageable. A clear use case quickly shows whether an agent is viable in business and technical terms.
- 01
Use-case workshop
Processes are assessed and prioritised by value, feasibility, data access and risk.
- 02
Focused prototype
An agent handles one realistic workflow using real interfaces or controlled test systems.
- 03
System integration
Tools, APIs, identities and permissions are implemented for ERP, commerce, databases or development platforms.
- 04
Production readiness
Evaluation, monitoring, cost control, security and human approvals are integrated into operations.
First step
Let us select one concrete task.
In an initial conversation, we assess where an agent can create real value, which systems it needs and which boundaries must apply from the beginning.
Discuss a use case
“I connect AI with the existing processes and systems – turning an interesting prototype into a controllable, useful solution.”