AI Agents Expert
for intelligent workflows, matched in minutes with vetted and available freelancersHire experts who design agentic workflows, connect language models to business systems, and build reliable tool-using assistants. FRATCH matches you quickly and precisely with vetted, available freelancers suited to your AI Agents project.
Meet FRATCH Experts who have recently used AI Agents
Chris W.
Last position:
Senior Strategy Advisor, Transformation Lead – Program realignment with vision, governance, and priority management at Sparkassen-Finanzgruppe | S-Communication Services
In-house consulting provider and transformation driver within the group, in a multi-stakeholder environment and working with C-level leaders.
Realigned and stabilized a cross-functional transformation and scaling program across the group. Refined the vision, priorities, and portfolio of initiatives, and established robust governance, planning, and management structures. Structured roles, responsibilities, and strategic initiatives, incorporating AI and IT automation opportunities.
Designed program realignment and project portfolio management
Developed a strategy model and vision for IT projects
Structured the portfolio, roadmap, and priorities
Established governance and regular meetings
Developed an operating model for flagship projects
Assessed AI and automation opportunities
Clarified roles and responsibilities
Implemented change initiatives
Designed, facilitated, and evaluated workshops
Brought 17 initiatives into a management framework
Improved transparency and decision-making
Strengthened accountability in program management
Sharpened the operating model
Integrated three top-5 institutions
Engaged over 80% of stakeholders
Governance
Portfolio management (PPM)
Change Management
Artificial intelligence
Workflow automation
AI use case assessment
Confluence
Jira
Stakeholder management
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Alwin G.
Last position:
IT Interim Manager & AI Strategist
- Founder of CheironX: AI-supported GRC management (ISO 27001, BSI IT-Grundschutz, TISAX, DORA)
- Strategic focus on Agentic AI and GenAI for modern IT Governance, Risk & Compliance Management
- IT interim management and strategic consulting
Roland C.
Last position:
Founder, Agents in Everyday Business at CXO AI OS
CXO AI OS is an agent system made up of six components. Instead of using AI as a chat window, it creates a system that understands a company’s context, makes decisions according to its rules, and acts on its behalf.
- For mid-sized companies: a guided sprint followed by ongoing operations for a team, department or prioritized cluster, based on an AI assessment
- For self-employed professionals: a program where participants build their own agent system
- Sequence in the company: assessment, prioritization, sprint, operations
- Implementation in Claude Cowork or ChatGPT Work, without coding
- Architecture: Chief of Staff, Goals, Advisors, Agents, Context, Catalog
Lutz E.
Last position:
Freelance Software Developer – GoPro MultiCam Control via COHN at Plutos Company UG
- Developed a pilot system to control and monitor multiple GoPro cameras
- Implemented start, stop, status, media, download and diagnostic functions via COHN endpoints
- Built camera detection, individual and group control, and stable session/KeepAlive logic
- Set up a download pipeline with a queue, retry handling and temporary files
- Ensured consistent session, folder and file naming for multicamera recordings
- Integrated provisioning, certificates and network operations
Gonzalo S.
Last position:
Parental Leave at Home
- Full-time parenting break after Klim, planned and now complete. Available for new projects immediately.
- Kept my skills current with a hands-on course in API and AI integrations.
- Built small tools with AI agents and Claude Code to practise automation and reporting.
- Sharpened my stakeholder management with a toddler: daily negotiations, unclear requirements, zero escaped defects:D
Paul W.
Last position:
Enterprise Architect at ePrimo GmbH
Led the end-to-end integration and migration architecture for decommissioning SAP IS-U market communication and transitioning to Salesforce Marketing Cloud (SFMC) with E.ON IT Services — covering regulated EDIFACT market messaging, a three-phase data migration (transfer of market message inventory after cutover), redesign of market communication processes, and full GDPR and German energy-market compliance.
- Designed the three-tier target architecture (SAP IS-U → NScale B2B gateway → SFMC) processing 50,000–100,000 EDIFACT messages daily within sub-5-minute SLAs.
- Architected the three-phase data migration: 36 months of historical data, zero-loss cutover of in-flight market messages, and near-real-time CDC synchronization.
- Delivered secure, BDEW/GDPR-compliant integration (AS2/OFTP2, RFC/IDoc, REST APIs, OAuth 2.0, Azure AD, AWS S3) achieving >99.5% uptime, <0.1% migration discrepancy, and RTO <4 hours.
- Applied BPMN to model and optimize the market communication processes.
Technologies: SAP IS-U, NScale, Salesforce Marketing Cloud, EDIFACT, OAuth 2.0, Azure AD, AWS S3, BPMN, Jira, Confluence, Draw.io
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
Christian F.
Last position:
Project Lead & Cyber Security Consultant at Deutsche Börse Gruppe
- Established a cyber resilience framework for AI-accelerated threats in the Frontier AI Threat Endurance (FATE) program: capability assessments, maturity assessments and gap analyses covering cyber security, operational resilience, incident response, recovery and governance, followed by target concepts and improvement measures
- Supported action owners from specification through implementation: implementation roadmaps, decision papers, management presentations and governance reporting, as well as audit- and supervision-ready documentation; managed stakeholders from IT, Information Security, Risk Management, Operational Resilience, Business Continuity and the business units
Karen M.
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Derek K.
Last position:
Founder at FiftyForward
Building a company to "support people over 50 as they start a new career"
Focus: "AI first" approaches to establish all relevant processes reliably in the shortest possible time
Core areas:
Business development (including competitor monitoring, channel selection, B2B and B2C strategies)
Marketing (building and optimizing funnels, organic and with paid ads)
Sales (direct B2B and B2C outreach resulting from marketing funnels)
Fulfillment (platform and process development, AI optimization)
Product management (developing the offering and MOAT)
One-to-one coaching and mentoring, platform development and operations
Bruno P.
Last position:
Product Owner
Design and implementation of a “unified-commerce” platform for SMEs to sell and deliver multi-product bundles
Achievements: Designed and delivered a fully functional, CPQ-based “buy flow” process for configuring and selling multi-product bundles on time, and secured customer approval within the specified 90-day timeframe. Successfully tested the integration of various interface services for customer search, customer data enrichment, address validation, service qualification, phone number validation, credit checks, appointment selection and quote-to-order transition.
Responsibilities:
- Strategic goal delivery: Translating strategic customer goals into action and delivering them (e.g. release content, business value).
- Backlog management: Creating and refining backlog items (Initiatives, Epics, User Stories, Defects) in close coordination with the project team and client.
- Prioritization & releases: Owning Product Backlog prioritization and delivery of defined release goals.
- Deliverable tracking: Tracking work completed by suppliers and the client.
- Roadmap & release planning: Developing and delivering roadmaps and release plans together with the client.
- Scope ownership: Owning the contractually agreed scope of work.
- Claim management: Proactive claim and change management with the client.
- Team coordination: Managing and coordinating the development team.
- Leveraging synergies: Using synergies between client projects and product development.
- Proposal preparation: Preparing proposals (with supervision) and presenting them in person to clients and partners.
Skills: Developing, communicating and delivering product visions; Product Backlog Management; Stakeholder Management; Regular reporting to management and steering committees; Requirements analysis & engineering; Planning and documenting workshops; Functional leadership and coordination of (distributed) project teams and external service providers; Epic Management; User Story specifications; Creating use cases, supporting software testing and User Acceptance Testing (UAT); Release Management and sprint planning; TO-BE process design; Process optimization; Planning, designing and specifying interfaces to existing and new systems; Identifying, assessing and managing project risks; Proactive claim and change management; Facilitating sprint planning and reviews; Data migration; Scrum; Kanban; REST API; JSON; XML; BPMN; UML; Jira; Confluence
Taner P.
Last position:
IT Consultant – IT Governance and Compliance at Premium automotive manufacturer
Result: Managed over 20 compliance campaigns in the development division, reaching 95% of the application portfolio and fulfilling over 4,000 compliance requirements.
- Campaign management: Planned, coordinated and monitored campaigns to improve compliance maturity; KPI-based management reporting
- Gap analysis: Assessed business-critical applications and prioritized compliance risks across Information Classification, Conformity Statement, Privacy Impact Assessment and other artifacts
- Stakeholder management: Served as the link between IT Security, Application Owners and business units; facilitated workshops and provided tailored implementation support
- Target vision and AI use cases: Developed a target model for maintaining compliance throughout the application lifecycle and assessed which artifacts could be populated automatically by AI agents using app and code data
Samet P.
Last position:
AI Consultant & Project Manager for Reporting Automation at Toyota Financial Services (EAR)
- Documented complex financial reporting processes in deep-dive sessions with subject matter experts
- Analyzed and mapped the 3-tier data flow SAP – Essbase – HQ (984 account entries, triple chart of accounts)
- Identified the biggest time drains (40 % of close time)
- Evaluated technologies (AWS, Microsoft Fabric, Custom/Hybrid) with cost-benefit analysis
- Created a validation rule catalog with 351 rules as the basis for pre-submission automation
- Business case: estimated savings potential of 480–960 T€/year for the EAR region
- Hands-on implementation with Microsoft Fabric, RAG, AI agents/Tool Calling and n8n; productive use of Microsoft Copilot and Copilot Studio
- AI-supported project management workflows (minutes, tasks, knowledge synchronization)
- Stakeholder management at senior management level (EAR Accounting, IEA IT, TFSC Japan), workshops and training on AI tools
Technologies: Microsoft Fabric, Copilot Studio, Claude AI, Python, Power BI, SAP, Essbase/SmartView, Excel (VBA), Notion, SharePoint
Gregor P.
Last position:
Engineering Data Management Project Manager
Technical and commercial risk assessment for an investment project in the hardware sector (scaling potential, liability risks, operational processes) Process consulting for two SMEs in the engineering environment, with a focus on workflow digitization and PLM
Discover over 15,000 top freelancers
Statistics of experts using AI Agents
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.8 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Professional Services, Banking and Finance
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
96%
Master's degree or higher
70%
Doctorate
12%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
97%
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts using AI Agents
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
AI Agents experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (93%)
- Professional Services (48%)
- Banking and Finance (48%)
- Manufacturing (37%)
- Automotive (37%)
- Retail (35%)
- Education (33%)
- Healthcare (32%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What AI Agents are
AI Agents are software systems that use language models, business rules, and connected tools to pursue a goal. They can interpret requests, plan a sequence of actions, retrieve information, make decisions within defined limits, and return a result. Unlike a fixed chatbot flow, an agent can adapt its next step to the situation.
What they build
Companies use AI Agents to automate work that crosses documents, applications, and approval steps. Common deliverables include:
- Customer support assistants that retrieve answers and create cases
- Research agents that search, compare, and summarize source material
- Sales and operations workflows connected to CRM and ERP systems
- Document intake, classification, extraction, and routing
- Internal assistants for knowledge access and recurring business tasks
Ecosystem and tooling
Strong work with AI Agents spans model providers such as OpenAI, Anthropic, Google, and open-source model ecosystems. Professionals may use LangChain, LangGraph, LlamaIndex, Semantic Kernel, vector databases, retrieval-augmented generation, function calling, and structured outputs. The surrounding stack often includes Python or TypeScript, APIs, queues, databases, observability, and cloud infrastructure.
When expertise matters
Freelance expertise is useful when a proof of concept must become a dependable product, or when an existing assistant gives inconsistent results. Companies also bring in specialists to select models, design tool permissions, connect private data, control operating costs, and define safe fallback paths. Experience with identity, data protection, testing, and deployment is especially important for business-critical workflows.
Reliable agent design
Good AI Agents are constrained, observable, and easy to correct. Professionals define clear goals, tool schemas, context boundaries, escalation rules, and approval points rather than giving an agent unrestricted access. They test realistic conversations and failure cases, measure grounded answers, protect sensitive data, and keep humans in control of consequential actions.
Choosing a specialist
Look for evidence of shipped agentic AI systems, not only prompt experiments. A strong specialist can explain why an agent is preferable to a deterministic workflow, when retrieval or fine-tuning is appropriate, and how quality will be evaluated. Ask for a clear architecture, test plan, security approach, monitoring strategy, and handover documentation. Remote collaboration works well when requirements, data access, review practices, and language expectations are agreed early.
Frequently asked questions
Curious about AI Agents? Here are the answers that come up again and again.
AI Agents are used for tasks that require interpretation, information retrieval, and action across connected systems. Examples include support triage, research, document processing, sales operations, and internal knowledge assistance. They work best where the process has a clear goal but the path varies by request.
Agentic AI can choose among approved tools and plan a sequence of actions based on the situation. A conventional chatbot usually follows a defined conversation flow, while standard automation repeats explicit rules. Many reliable solutions combine both approaches, reserving agent behavior for steps that genuinely need flexibility.
A capable AI Agents specialist usually understands APIs, data modeling, retrieval-augmented generation, prompt and context design, and cloud deployment. They should also know identity management, logging, evaluation, and security for model-connected systems. Experience with Python or TypeScript is common, but architecture matters more than a particular language.
Autonomous agents need more than prompt-writing experience when they can access business data or take actions. A small prototype may need focused specialist support, while a production workflow requires skills in testing, permissions, monitoring, failure handling, and deployment. The right level depends on the impact of errors and the number of systems involved.
AI Agents projects often work well remotely because design, implementation, and evaluation can happen through shared repositories and controlled environments. On-site collaboration may help when the workflow depends on sensitive operations, complex stakeholder discovery, or restricted infrastructure. Clear access rules, documentation, and regular reviews are essential in either setup.
A good AI Agents solution is judged with realistic tasks, known failure cases, and checks for factual grounding and correct tool use. Review whether the system explains failures, protects data, escalates uncertain requests, and avoids unauthorized actions. A specialist should provide repeatable evaluations rather than relying on impressive demonstrations.
An AI assistant is often better when a person should review every response or action. An autonomous agent is more suitable when it can complete a bounded workflow under explicit permissions and safeguards. The decision should consider risk, process variability, data access, and the cost of human review.
Professionals working with AI Agents should track changes in model APIs, tool-calling methods, evaluation practices, and agent orchestration frameworks. They also need a strong understanding of security, privacy, retrieval quality, and production observability. Framework knowledge helps, but durable skills come from designing systems that remain useful when models or providers change.
The average hourly rate of freelancers who have used AI Agents in their recent projects is 100 €, which corresponds to a daily rate of about 798 € based on an 8-hour working day.
Of the freelancers who have used AI Agents in their recent projects, 96% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers who have used AI Agents in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.8 years.
The most common languages among freelancers who have used AI Agents in their recent projects are English (98%), German (98%), and French (16%).
The most common industries among freelancers who have used AI Agents in their recent projects are Information Technology (93%), Professional Services (48%), and Banking and Finance (48%).
The most common business areas among freelancers who have used AI Agents in their recent projects are Information Technology (96%), Product Development (91%), and Project Management (63%).
Main locations of FRATCH Experts, who have recently used AI Agents
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Dusseldorf 
Dortmund 
Essen 
Nuremberg 
Vienna 
Zurich Countries:
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