Building agentic AI for enterprise automation requires clear responsibilities, solid integration points, and a practical engineering approach. This article details the specific agent responsibilities, the multi-step prompt-based workflows they participate in, and the technical and personal requirements expected from engineers developing production-grade SaaS agent systems.
Core Agent Responsibilities and Workflows
The system consists of specialized agents and coordinated workflows that perform distinct enterprise tasks. Each agent is designed to operate autonomously or as part of multi-step, prompt-based, retrieval-augmented workflows that execute without manual intervention.
- Document Processing Agent: extract structured data from documents and push that data into ERP systems, enabling downstream processing.
- Supplier Negotiation Agent: autonomously respond to supplier emails and negotiate prices as part of procurement workflows.
- Regulatory Agent: compare current regulations with standard operating procedures (SOPs) and suggest changes where discrepancies appear.
- Auto-reply Agent: handle support and customer emails by generating autonomous replies that address common requests.
- CRM Update Agent: log meeting summaries and update deal statuses in CRM systems to maintain accurate sales records.
- Web Scraper Agent: monitor public tenders and trigger follow-up actions when relevant opportunities appear.
- License Optimization Agent: analyze SaaS usage and recommend cost-saving measures for software licenses.
These agents are developed using agentic AI frameworks such as Langchain and Langflow. They are integrated with enterprise platforms like SAP, Salesforce, and ServiceNow to read/write authoritative data and trigger business processes. The implementation emphasizes design and execution of multi-step workflows that are:
- Prompt-based — using structured prompts to steer agent actions;
- Retrieval-augmented — pulling contextual data from enterprise stores or prior records; and
- Autonomously executed — chaining agent outputs into subsequent steps without manual handoff.
On the engineering side, backend APIs and agent logic are built using FastAPI, Python, and containerized with Docker. The work culminates in testing and deploying features into a production-grade SaaS platform, ensuring agents operate reliably at scale.
Requirements and Implementation Expectations
Successful contributors combine hands-on engineering skills with a focus on practical AI automation. The role expects:
- Technical experience: practical experience in Python, FastAPI, or Langchain; familiarity with building backend APIs and agent logic.
- Integration experience: prior work on chatbots, API integrations, or automation tools that connect to enterprise systems.
- Platform and tooling knowledge: understanding of PostgreSQL, GitHub Actions, and Docker as part of CI/CD and deployment workflows.
- Domain interest: interest in SaaS, enterprise software, or agent-based architecture where agents interact with ERP, CRM, and ticketing systems.
- Attitude and ownership: passionate about AI and automation, willing to work hands-on, own product parts, and deliver quality-first results with structured execution and German professionalism.
The engineering deliverables align tightly with the responsibilities above: develop agentic AI systems (Langchain, Langflow), integrate agents with SAP/Salesforce/ServiceNow, design prompt-based retrieval-augmented workflows, implement backend APIs with FastAPI/Python/Docker, and test and deploy into a production-grade SaaS platform.
In summary, the role centers on building and operating a suite of specialized agents — from document processing and supplier negotiation to regulatory comparison and license optimization — integrated with enterprise tools and delivered via robust backend APIs and containerized services. Candidates should bring hands-on Python/FastAPI/Langchain experience, prior integration or chatbot work, CI/CD and Docker familiarity, and a quality-first, ownership-driven mindset aligned with enterprise standards.
