AI Agent Developer.

Builds AI agents that complete tasks rather than merely answering questions.

Remote & on-site DACH-wide
AI Agent Developer — enquire

An AI agent developer designs and implements agentic systems: AI that plans multi-step tasks, calls tools, interacts with systems and validates results — from the research agent to automated case processing. This is the discipline behind the buzzword „Agentic AI“.

The role is more demanding than classic LLM integration, because agents can chain errors: without sound guardrails, human-in-the-loop checkpoints and observability, automation quickly turns into a risk. Experienced agent developers therefore build the control mechanisms first, then the autonomy.

01

Automated case processing

Recurring tasks — quote review, data maintenance, complaint preparation — are handled by an agent, while critical decisions remain with humans.

02

Multi-agent workflows

Several specialised agents work together: research, drafting, review. The developer orchestrates roles, handovers and escalations.

03

Agents on enterprise tools

Integration with CRM, ERP, ticketing via APIs or MCP — the agent works within your systems, not alongside them.

04

Agentic AI pilot project

A well-defined use case proves within 6–8 weeks whether agentic automation holds up in your process — with measurable criteria.

Core competencies
Agent architecturesTool/function callingGuardrails & evaluationWorkflow orchestrationPython/TypeScript
Tools & frameworks
LangGraphClaude Agent SDKOpenAI AgentsMCPn8n/Temporal
Plus factors
Process analysis experienceRPA backgroundSecurity awareness (prompt injection)
Where do you draw the line between automation and human approval?
Good agent developers think in terms of checkpoints first. Anyone who answers „the agent does everything itself“ has not yet experienced production damage.
How do you protect an agent from prompt injection via emails or documents?
Agents process external content — without an answer to this question, any system with write permissions is a security risk.
How do you measure whether an agent performs its task well?
Agentic systems require end-to-end evaluation across entire task chains, not just the quality of individual responses.
Which use case would you NOT solve with an agent?
Judgement: determinism, liability questions and simple rule-based cases are often better served by classic automation.
What does an AI agent developer cost as a freelancer?
Based on our market observations, hourly rates in the DACH region in 2026 range between 110 and 165 euros. Profiles with demonstrably production-ready agent systems are rare and sit at the upper end.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An AI agent pursues a goal across multiple steps: it plans, calls tools and systems, checks intermediate results and escalates to humans when it is uncertain.
Are AI agents already mature enough for enterprise use?
For well-defined, easily measurable processes, yes — with guardrails and human approval checkpoints. Fully autonomous agents without control mechanisms are not yet justifiable in most enterprise contexts in 2026.
Which systems can AI agents work with?
In principle, with anything that offers an API — CRM, ERP, ticketing, email, document storage. Standards such as MCP (Model Context Protocol) are increasingly simplifying integration.

Looking for a AI Agent Developer?

Tell us about your project — within one business day you’ll get an honest assessment of availability, day rate and alternatives.

Request experts