Service 05 — Agentic Engineering
We leverage specialized AI agent systems to accelerate mission-critical data architectures, DWH migrations, and pipeline development by up to 70%. Led by experienced Senior Data Architects – for uncompromising enterprise stability.
Methodology & Philosophy
Unstructured "Vibe Coding" works for solo developers, throwaway scripts, and quick demos. However, when dealing with regulated financial systems (BaFin, MaRisk), critical industrial pipelines, decades-old Data Warehouses, or GDPR and EU AI Act audits, the blind application of AI generates one primary outcome: unmanageable security and liability risks.
At Finelbo, Agentic Engineering does not mean unleashing uncontrolled black-box models onto production data. It is the methodological evolution of our over 13 years of experience in designing complex enterprise data systems.
Our core principle is: Human in the Lead, not just Human in the Loop.
Our Senior Data Architects define the system architecture, establish deterministic validation guardrails, and bear ultimate responsibility. Specialized multi-agent systems take over iterative analysis, code generation, dbt modeling, and test automation at breathtaking speed.
| Feature | Unstructured Vibe Coding | Finelbo Agentic Engineering |
|---|---|---|
| Human Role | Prompter (reactive, unguided) | Human in the Lead (architecture-leading & fully accountable) |
| Methodological Approach | Unstructured prompting & ad-hoc generation | Structured multi-agent architecture with deterministic guardrails |
| Architecture & Documentation | Emergent, opaque, rarely documented | Explicitly designed, full ADRs & automatic Data Lineage |
| Quality Assurance & QA | Based on blind trust in AI outputs | Deterministic validation pipelines, dbt tests & regression suites |
| Enterprise Governance & Audit | No traceability, massive audit risks | Seamless audit trail, GDPR, BaFin & EU AI Act compliant from Day 1 |
| Area of Application | Isolated sandboxes & throwaway prototypes | Mission-critical enterprise data platforms & legacy migrations |
The Agentic SDLC
In every single phase of the Software Development Life Cycle (SDLC), AI agents take over repetitive analysis and synthesis tasks – while our Lead Architects make the critical quality and architectural decisions.
Role of AI Agents
Structure heterogeneous business requirements, analyze undocumented legacy schemas (SAS, COBOL, SSIS), and identify logical contradictions.
Role of our Lead Engineers
Validate the business domain logic, clarify regulatory constraints, and make binding architectural decisions.
Role of AI Agents
Develop modeling alternatives (Data Vault 2.0 Hubs/Links/Sats, Star Schemas), generate schema mappings, and draft Architecture Decision Records (ADRs).
Role of our Lead Engineers
Select the target topology, validate against Non-Functional Requirements (latency, resilience, costs), and own the system boundaries.
Role of AI Agents
Generate modular dbt transformation models, PySpark scripts, and tailored data libraries based on strict coding guidelines.
Role of our Lead Engineers
Perform code reviews, optimize partitioning strategies, and master highly complex edge cases.
Role of AI Agents
Create automated Data Quality tests, generate synthetic test datasets, and conduct continuous regression checks.
Role of our Lead Engineers
Define the test and acceptance strategy, verify boundary values mathematically, and guarantee data consistency.
Role of AI Agents
Create standardized Terraform code (IaC), CI/CD pipelines, and monitoring configurations via secure Model Context Protocol (MCP) interfaces.
Role of our Lead Engineers
Define rollout and security strategies, secure Private Cloud Endpoints, and own the production handover.
Entry Model · Timeboxed Format
No months-long preliminary studies. No non-binding Powerpoint slides. A clearly defined breakthrough with a fixed price, fixed scope of work, and three tangible deliverables on your real data infrastructure.
Your concrete use case (e.g., migration of a complex ETL pipeline, construction of a Lakehouse Data Mart, or a domain-specific MCP agent) – implemented directly on your cloud infrastructure, not an isolated slide demo.
Seamless documentation of all architectural decisions made (ADRs), data flow diagrams, security concepts, and governance proofs for your IT board and compliance auditors.
Sound economic viability analysis, TCO estimation, identification of technical dependencies, and a clear, risk-free implementation plan for enterprise-wide rollout.
We mutually define the exact technical scope upfront – no scope creep, no surprises.
Collaboration Models
Finelbo takes on the holistic design, architecture, and turnkey implementation of your project using Agentic Engineering. Your internal team is continuously involved and learns the methodology directly on the live system.
Ideal for:
Our consultants and engineers work directly embedded within your internal development teams. We establish multi-agent toolchains, MCP servers, and deterministic CI/CD QA practices until your team can lead independently.
Ideal for:
Proven Practice
We do not test methods on our clients. Our approaches for automated migrations, Model Context Protocol interfaces, and deterministic data pipelines have proven themselves in real corporate environments.
Secure AI agents for Jira orchestration and PR screening via MCP servers. MVP in 8 weeks.
Read Case Study [Carl Zeiss Vision]Decomposition of historical pipelines into modular PySpark workflows with 80% reduced latency.
Read Case Study [Denner AG]Automation of data engineering tasks with reusable libraries and robust dbt tests.
Read Case StudySchedule Consultation
We analyze your use case and show you a functioning, production-ready result on your data infrastructure in 14 days – binding and at a fixed price.
