AI Workflow for Intelligent Issue Tracking and Resolution Routing

Manual issue triage in platforms like BIM 360 or Navisworks creates a multi-day bottleneck where clashes and RFIs sit unassigned. An AI agent, using NLP on issue descriptions and geometric context, instantly classifies the discipline (e.g., Structural vs. MEP), estimates urgency, and routes it to the responsible party's queue via API. This eliminates the lag of weekly coordination meetings, compressing the 'identification-to-assignment' phase from days to minutes.

Vague issue ownership leads to finger-pointing and delayed fixes, often resulting in expensive field rework. The workflow integrates with project directory APIs (e.g., Procore, Aconex) to map issues to the correct trade contractor, design firm, or internal team based on contract scopes and model authorship. By enforcing accountability at the point of logging, you eliminate the 15-20% of issues typically bounced between parties, directly lowering the cost of corrections.

Not all issues are equal. A critical structural clash must precede a minor clearance concern. The AI agent scores issues based on severity, schedule impact, and location, then pushes high-priority alerts to field tablets and superintendents via SMS or app notifications. This ensures crews address show-stoppers first, preventing downstream task delays and keeping the critical path on track.

Manually logging, tagging, and generating weekly issue reports consumes 10-15 hours per week for project engineers. This workflow automates the entire lifecycle: ingestion from clash detection tools, auto-population of metadata, routing, status updates, and compilation of dashboards. It frees technical staff for higher-value problem-solving while creating an auditable, real-time record for claims mitigation.

Beyond routing, the system aggregates issue data to identify recurring clash types, chronic problem zones, or specific trade partners with high error rates. This intelligence, surfaced through a dashboard, allows project leadership to implement targeted process improvements, pre-emptively adjust coordination strategies, and negotiate better terms, transforming reactive firefighting into proactive risk management.

On large-scale projects with thousands of weekly clashes, manual coordination becomes impossible. This agentic workflow, built on orchestration frameworks like LangGraph or CrewAI, scales linearly with issue volume. It enables a small VDC team to manage coordination for a $1B+ program effectively, providing a force multiplier that controls labor costs while maintaining rigorous quality oversight.

This agent monitors the Common Data Environment (CDE)—platforms like BIM 360 or Navisworks—for new issues. It uses natural language processing (NLP) to parse free-text descriptions from clash reports or RFIs, extracting key entities: discipline (MEP, Structural, Architectural), component type, severity, and location. It classifies the issue against a project-specific taxonomy, transforming unstructured logs into structured, actionable tickets. This eliminates hours of manual reading and tagging by coordinators.

The core orchestration logic that maps the classified issue to the responsible party. It queries integrated project directory APIs (e.g., Procore, Aconex) to resolve responsibility based on: contract scopes, discipline leads, model authorship, and geographic zones. The engine applies configurable business rules (e.g., 'high-severity structural clashes go to the lead structural engineer and the superintendent') and places the ticket into the correct assignee's queue within the CDE or connected task management system. This ensures zero 'who does this?' delays.

This component manages all outbound communications and service-level agreements (SLAs). It sends context-rich notifications via the recipient's preferred channel (email, MS Teams, Slack, in-app alert) with deep links to the 3D model view of the issue. It monitors response times and will automatically escalate stagnant tickets based on severity—first to a backup, then to the project manager. This system is integrated with the project's communication stack to prevent issues from getting lost in noisy inboxes.

A closing agent that tracks the issue lifecycle. When a resolution is logged (e.g., 'clash resolved by adjusting duct route'), this agent can trigger downstream actions. For validated fixes, it can initiate an update to the federated BIM model or flag the clash as cleared. It also collects resolution notes to continuously train the classification and routing models, improving accuracy over the project's lifespan. This creates a closed-loop system where the workflow learns from field decisions.

The central nervous system built on a framework like LangGraph that orchestrates the handoffs between all agents and external systems (CDE, Directory, Comms). It manages state, handles exceptions (e.g., ambiguous responsibility), and routes them to a human-in-the-loop review dashboard. This layer provides full observability: audit trails for compliance, dashboards showing issue aging and agent performance, and controls for project managers to adjust routing rules or pause automation during critical phases.

The practical implementation stack. Uses browser automation agents (e.g., Playwright) to interact with legacy or API-limited design tools. Core logic is deployed as containerized microservices for scalability. Integrates via REST APIs or middleware with primary AEC platforms: Autodesk Construction Cloud, Oracle Primavera, Procore, and ServiceNow. Deployment is typically phased, starting with a pilot trade package, and includes robust fallback to manual processes to ensure no issue is ever lost during rollout.

The workflow's primary trigger, integrating via API with platforms like Autodesk BIM 360, Navisworks, or Procore to ingest new issues, clashes, and RFIs in real-time. This agent monitors the Common Data Environment (CDE), extracts issue metadata (title, description, screenshot, location), and normalizes data for downstream processing. Implementation requires handling webhooks, managing authentication, and parsing proprietary data schemas to create a unified issue object.

A specialized agent using fine-tuned or prompt-engineered LLMs to analyze free-text issue descriptions. It classifies the issue by:

• Discipline (Architectural, Structural, MEP, Civil)
• Urgency (Critical, High, Standard) based on keywords and project phase
• Responsible Party by matching descriptions against a project directory (e.g., 'ductwork' -> Mechanical Contractor). This layer reduces manual triage labor by 60-80% and requires a validation loop for low-confidence classifications.

The system's 'routing brain' that connects to CMiC, Oracle Primavera, or custom project databases via REST APIs. It resolves the classified issue to a specific vendor, subcontractor, or internal team based on:

• Contractual scope of work matrices
• Real-time team assignments and contact details
• Current workload and open issue counts. This ensures routing is contractually accurate and avoids overloading any single party, requiring constant sync with the project's master data.

Orchestrates communication based on urgency and recipient preference, integrating with:

• Email (Microsoft Graph, SMTP)
• Chat (Slack, Microsoft Teams webhooks)
• SMS (Twilio, Vonage)
• Project Management Inboxes. It sends initial notifications, follows up on stale issues, and escalates to superintendents or project managers based on configurable SLA timers (e.g., 24hrs for 'High' urgency). This layer must log all communications for auditability.

Closes the loop by monitoring for resolution updates. This agent:

1. Watches for replies in connected channels (email, chat threads).
2. Parses resolution notes and attached mark-up drawings.
3. Updates the source BIM platform via API, closing the issue and appending the resolution record.
4. Triggers downstream actions, like updating a dashboard or notifying the originator. This requires robust exception handling for partial or conflicting resolution data.

Provides operational visibility by integrating data from all previous components into a centralized dashboard (e.g., Power BI, Grafana, custom React app). It tracks KPIs like:

• Mean Time to Assignment (MTTA)
• Mean Time to Resolution (MTTR)
• Top issue categories and responsible parties
• Agent confidence scores and error rates. This is critical for continuous improvement, proving ROI, and governing the automated system's performance.