World Congress 2026 Europe

Building a Multi-Agent Orchestration Engine That Actually Follows the Rules

July 9, 2026 16:50 – 17:20 · 30 min Stage 9

What this session covers

At E.ON Digital Technology, we’re building an AI-powered platform that spans the entire software development lifecycle—from ideation, requirements in Jira, through code generation and GitLab integration, to automated testing, infrastructure deployment, and production monitoring.

Enterprise environments don’t tolerate agents going off script. We need them to follow our SDLC guardrails, and not to improvise.

While the workflow itself enforces standards, the graph controls transitions, embeds approval gates, and ensures compliance. Users just chat; the system handles the rest.

We’ll walk through the architecture and share real examples: legacy application modernization, cloud-native migrations, and gains in developer productivity.

The Problem

AI coding assistants are powerful, but they’re designed for autonomy. In an enterprise like E.ON, that’s a problem. We have governance requirements, approval workflows, and integration points across the entire SDLC—Jira for planning, GitLab for code, our automation platform for deployments, our monitoring stack for observability. Letting an agent “figure it out” isn’t an option.

What We Built

We created a multi-agent platform based on LangGraph where:

-Specialized agents handle distinct phases: requirements analysis, code generation, test creation, infrastructure provisioning, log analysis

-The workflow graph enforces our SDLC—agents don’t decide when to ask for approval or which phase comes next; the graph does

-Graph disaggregation: the workflow breaks down complex tasks into manageable steps, improving task completion rates and reducing hallucinations

-Enterprise integrations connect each phase to our actual toolchain (Jira, GitLab, deployment automation, monitoring)

-Workflows are pluggable—different use cases get different flows with appropriate checkpoints

The result: developers interact through a conversational interface, but the underlying system enforces similar standards we’d expect from manual processes.

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