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Digital Worker 8 AI Agents Example

DGNB Sustainability Certification Digital Worker

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.

8 AI Agents
7 Tech Stack
AI Orchestrated
Scoped Availability
Worker ID: dgnb-certification-workflow

Problem Statement

The challenge addressed

Achieving DGNB (German Sustainable Building Council) certification requires extensive data extraction from BIM models, complex carbon calculations across building lifecycle stages, gap analysis against 40+ criteria, and coordination with multiple sta...

Solution Architecture

AI orchestration approach

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Orchestrates an 8-agent AI workflow that automates BIM data extraction, calculates embodied carbon using EN15978 methodolo...
Interface Preview 4 screenshots

Illustrative preview (sample data, not verified outcomes): DGNB Certification Assistant - Project setup with AI agent team, BIM model upload, and target certification level selection

Illustrative preview (sample data, not verified outcomes): Agent Execution & Tool Calls - Live compliance validation with inter-agent communication and workflow progress tracking

Illustrative preview (sample data, not verified outcomes): Human-in-the-Loop Decision - Review optimization recommendations with cost, carbon reduction, and ROI analysis

Illustrative preview (sample data, not verified outcomes): Analysis Complete - Executive summary with certification gap analysis, carbon footprint, and EU Taxonomy alignment scores

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

8 Agents
Parallel Execution
AI Agent

Workflow Orchestrator

DGNB certification involves 5+ phases with complex task dependencies, requiring coordination between 7 specialist agents while maintaining quality control and human oversight at critical decision points.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Decomposes certification workflow into task graphs (DAG) with dependency management. Operates in supervised autonomy mode (auto-approve threshold 90%). Uses chain-of-thought reasoning for task decomposition, coordinates parallel execution where allowed, manages human decision points for optimization approval, and handles workflow state across 5 phases with progress tracking.

EXAMPLE #1
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AI Agent

BIM Data Extractor

DGNB requires precise material quantities from BIM models—concrete volumes, steel tonnage, insulation areas—which must be extracted accurately and linked to EPD data for carbon calculations.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Operates in full-auto mode using ReAct pattern. Parses IFC4x3 models to extract 300+ material entries with quantities (e.g., CEM I Concrete C30/37: 380 m³). Links materials to EPD identifiers (EPD-IBU-2024-001), generates digital twin synchronization points, performs 3D geometry analysis for quantity validation, with performance to be validated during evaluation.

EXAMPLE #2
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AI Agent

Carbon & Emissions Calculator

Calculating embodied carbon requires applying GWP coefficients across lifecycle stages (A1-A3, A4-A5, B1-B7, C1-C4, D), integrating current carbon pricing, and benchmarking against industry standards—calculations too complex for manual processing.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Implements EN15978 methodology for lifecycle carbon assessment. Calculates total embodied carbon (e.g., 1,247 tCO2e at 479.6 kgCO2e/m²), breaks down by category (Structure 55%, Envelope 25%, Finishes 20%), integrates real-time EU ETS carbon pricing (€68.50/tonne), projects 2030 costs at €125/tonne, and identifies biogenic carbon sequestration (-38.5 tCO2e from CLT).

EXAMPLE #3
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AI Agent

Gap & Risk Analyst

Identifying which DGNB criteria are met, partially met, or failing—and what actions close the gaps—requires deep knowledge of certification requirements, EU Taxonomy alignment, and climate risk assessment frameworks.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Uses tree-of-thought reasoning to explore multiple certification pathways. Analyzes gaps across ENV, SOC, ECO, TEC, PRO criteria (e.g., ENV1.1 Life Cycle Assessment: 8.5/10 points, gap 1.5 points). Performs EU Taxonomy alignment scoring (82%), TCFD physical/transition risk assessment, and prioritizes gap closure by effort-to-impact ratio.

EXAMPLE #4
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AI Agent

Sustainability Optimizer

Achieving target certification level requires evaluating hundreds of potential interventions across materials, processes, and design changes—each with different costs, carbon reductions, and feasibility constraints.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Operates in human-required mode using internal debate reasoning. Generates Pareto-optimal optimization scenarios: (1) Low-Carbon Concrete Substitution: +3.5 points, €12,000, -241 tCO2e, (2) Enhanced Bicycle Infrastructure: +3.0 points, €8,500, (3) Circular Waste Management: +2.0 points, -€2,400 savings. Calculates ROI, payback periods, and circular economy scores for each option.

EXAMPLE #5
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AI Agent

Compliance & Regulatory Validator

EU sustainability regulations evolve rapidly—CSRD, EPBD, EU Taxonomy—requiring continuous monitoring to ensure certification documentation meets current requirements and avoids compliance gaps.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Monitors regulatory updates across EU jurisdictions in real-time. Validates against DGNB 2023 criteria, EU Taxonomy technical screening (NZEB-10% threshold), DNSH assessments for 6 environmental objectives, and minimum safeguards. Tracks upcoming deadlines (CSRD: 2025-01-01, EPBD Renovation Wave: 2027) and identifies compliance gaps with remediation timelines.

EXAMPLE #6
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AI Agent

Insights & Report Generator

Certification reports must serve multiple audiences—executives need KPIs, technical teams need detailed calculations, auditors need compliance documentation—requiring different formats and detail levels from the same data.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Operates in full-auto mode with reflection reasoning. Generates stakeholder-specific reports: Executive Summary (key metrics, ROI), Carbon Analysis (EN15978 breakdown, benchmarks), Compliance Matrix (criteria scoring, evidence), Recommendations (prioritized actions). Produces 12-page PDF reports with AI-generated insights, data visualizations, and digital twin dashboard links.

EXAMPLE #7
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AI Agent

Stakeholder Coordinator

DGNB certification requires documents and approvals from multiple stakeholders (architects, contractors, suppliers)—tracking requests, follow-ups, and deadlines across parties is administratively burdensome.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Manages stakeholder communication workflows in supervised mode. Drafts professional document requests tailored to stakeholder type, tracks submission deadlines, automates follow-up sequences, escalates overdue items, and schedules coordination meetings. Maintains audit trail of all communications for certification documentation.

EXAMPLE #8
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

A production-grade agentic AI system for automated DGNB certification workflow. Features advanced agent autonomy levels (full-auto to human-required), multiple reasoning modes (chain-of-thought, ReAct, tree-of-thought, debate), real-time carbon pricing integration, EU Taxonomy and TCFD compliance, circular economy optimization, and social value assessment. Includes digital twin BIM synchronization and IoT sensor integration.

Tech Stack

7 technologies

LLM Integration: Claude 3.5 Sonnet with variable temperature (0.1-0.7) per agent role

BIM Integration: IFC4x3 parsing, digital twin synchronization

EPD Database: IBU Environmental Product Declarations with real-time updates

Carbon Market API: EU ETS spot pricing and 2030/2050 forecasts

EU Taxonomy Database: Activity codes, DNSH criteria, technical screening thresholds

DGNB Criteria Database: 2023 scheme with scoring matrices

Frontend with signal-based reactivity and OnPush change detection

Architecture Diagram

System flow visualization

DGNB Sustainability Certification Digital Worker Architecture
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