Agentic Workflow for Automated Utility Relocation Planning and Costing

Manual utility coordination is a multi-week, error-prone process where engineers manually query disparate as-built records, GIS layers, and material databases to identify conflicts and draft relocation plans. This creates a critical path delay, inflates contingency budgets by 15-25% for unforeseen clashes, and ties up senior technical staff in repetitive data reconciliation. The operational upside comes from compressing this phase from weeks to hours, preventing costly field redesign, and enabling precise costing that improves bid accuracy and project margin.

Implementation requires an orchestrator, like LangGraph, to manage specialized agents for geospatial querying, cost database access, and conflict logic. The workflow integrates with systems like Esri ArcGIS, SAP S/4HANA for material data, and Autodesk BIM 360. Critical controls include human-in-the-loop approval for final plans, exception routing for data gaps, and full audit logging of all agent decisions and data sources to meet engineering quality assurance standards.

This workflow automates a critical, high-latency bottleneck in civil engineering: manually coordinating with utility owners and planning relocations. It eliminates weeks of back-and-forth by deploying specialized agents to autonomously query fragmented utility GIS, as-built PDFs, and material cost databases. The operational upside is direct: it prevents costly field surprises, reduces project delays by 30-50%, and creates auditable, optimized plans that integrate directly with Primavera P6 or Microsoft Project for scheduling.

Implementation requires an orchestration layer, likely built with LangGraph or a custom framework, to manage state and handoffs between the detection, planning, and costing agents. Each agent interfaces with legacy systems—SAP for materials, Esri for GIS, Document AI for PDFs—via APIs or browser automation where APIs are lacking. Critical controls include a mandatory human-in-the-loop approval gate for all plans, confidence scoring on data quality, and full observability into agent decisions for auditability and continuous refinement of the logic.

Phase 1 establishes the core data ingestion and conflict detection engine, delivering immediate value by automating the manual cross-referencing of civil design files against utility GIS layers and as-built records in systems like Esri ArcGIS or Bentley OpenUtilities. This initial agent identifies clashes, flags high-risk conflicts, and generates a preliminary conflict report, reducing the planning cycle's discovery phase from weeks to hours. The focus is on building a reliable data pipeline and a high-confidence detection model, with all outputs routed through a human-in-the-loop review gate in the project management platform (e.g., Autodesk BIM 360, Procore) before any downstream actions are taken.

Phase 2 layers on the planning and costing intelligence, where approved conflicts trigger specialized agents. A planning agent queries material databases and historical project data to generate optimal relocation paths, while a costing agent pulls live rates to produce detailed estimates. Phase 3 integrates the approved, costed plans directly into the project scheduling tool (e.g., Oracle Primavera P6) and updates the master schedule. Each phase includes validation checkpoints and a feedback loop to retrain detection and costing models, ensuring the system improves with use while maintaining the necessary governance and auditability for high-stakes infrastructure projects.

The automation's value is locked in cost avoidance and schedule certainty, preventing multi-million-dollar field conflicts and utility coordination delays. To capture this, the workflow must integrate with as-built records in GIS, material databases like RSMeans, and scheduling tools like Primavera P6. The core risk is inaccurate data triggering flawed plans, so the initial rollout focuses on a single project phase with high-quality utility records, using outputs for planner review rather than direct field execution.

Implementation proceeds through controlled stages: a shadow mode comparing AI plans to manual ones, a co-pilot phase where plans require engineer sign-off, and finally autonomous execution for low-risk, repetitive conflicts. Observability is built on granular audit logs in the orchestrator, tracing each agent's rationale, data sources, and cost assumptions. This phased governance ensures the workflow delivers ROI through labor leverage and risk reduction while maintaining the necessary oversight for high-stakes infrastructure decisions.

The business value of automated utility relocation planning hinges on preventing costly field conflicts and schedule delays. However, this requires ingesting and trusting fragmented data from as-built drawings, GIS, and legacy asset registers. Poor data quality—missing depths, outdated service lines, or incorrect material specs—directly translates to flawed relocation plans and financial exposure. The workflow's first layer must be robust data validation and enrichment agents that flag inconsistencies for human review before costly simulation begins, ensuring the automation's output is actionable and defensible.

Implementation requires orchestrating these validation and exception paths within a framework like LangGraph, where each agent's output is gated by confidence scores and business rules. The human review queue must integrate directly with the engineering team's issue-tracking system (e.g., Jira, ServiceNow). Monitoring is critical: dashboards should track exception rates by data source (e.g., 15% of water main records require manual verification), providing a clear metric for data remediation efforts and proving the workflow's operational maturity to stakeholders.