Automation Workflow for Mandatory Regulatory Reporting on Service Reliability

Manual processes require analysts to pull outage records from the OMS, customer counts from the CIS, and weather data from external sources, then calculate SAIDI/SAIFI metrics in spreadsheets. This workflow automates the entire ingestion and calculation pipeline, pulling from source systems via APIs or agents, applying regulatory formulas, and flagging data anomalies for review. It eliminates 40-60 hours of manual effort per reporting cycle and removes the risk of formula errors.

Missed regulatory deadlines result in fines and reputational damage. This autonomous workflow enforces a strict timeline: agents compile data on a scheduled trigger, generate formatted reports (PDF, XML) per PUC templates, and route them through a pre-configured approval chain (e.g., to engineering, legal, and the VP of Operations) with built-in SLA reminders. Every data point, calculation, and approval is logged in an immutable audit trail, creating a defensible record for regulators.

By automating the repetitive assembly of compliance reports, your regulatory and operations analysts are freed to focus on higher-value work: investigating the root causes behind metric fluctuations, simulating the impact of grid investments on future reliability scores, and preparing strategic briefs for management. This turns a cost center focused on administrative compliance into a strategic function that drives operational improvement.

Manual reporting leads to version control issues and inconsistent interpretations of reporting rules year-over-year. This workflow codifies the business logic—definitions of 'major event days,' inclusion/exclusion rules for certain outages—into version-controlled, executable code. This ensures every report is generated from a single source of truth, dramatically reducing the risk of subjective errors or non-standard interpretations that could trigger regulatory scrutiny.

Instead of a quarterly or annual compliance scramble, the underlying data pipeline can be configured to produce real-time or daily reliability dashboards. Operations leadership can monitor SAIDI/SAIFI trends against targets as they happen, enabling proactive interventions before reporting periods end. This transforms compliance from a retrospective exercise into a forward-looking operational management tool.

Regulatory frameworks evolve (e.g., new metrics like CAIDI, MAIFI, or resilience indices). A custom, agent-based workflow is built to be extended, not replaced. New data sources, calculation modules, and report templates can be added as plugins. The orchestration layer (e.g., LangGraph) manages the flow, allowing your team to adapt to new mandates in weeks, not months, protecting your long-term compliance investment.

The workflow begins with agents continuously polling the Outage Management System (OMS), SCADA, and Meter Data Management System (MDMS) for raw outage events, restoration timestamps, and customer counts. This layer normalizes fragmented data (e.g., reconciling OMS trouble tickets with SCADA breaker operations) into a unified reliability event log, which is the single source of truth for all calculations. Data quality checks and exception routing for missing or conflicting records are built in.

A dedicated computational agent applies PUC-mandated formulas to the cleansed event log. It calculates System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and other required metrics (e.g., CAIDI, MAIFI) for each reporting period and circuit. The agent handles edge cases like major event exclusions, partial restorations, and critical customer designations, producing an auditable calculation trail. Results are stored in a versioned database for historical trending and audit.

This agent retrieves the calculated metrics and populated data tables, then merges them into the official regulatory filing template (e.g., PUC Form 411). It uses an LLM orchestration layer (e.g., LangChain) to draft the required narrative sections—explaining major events, improvement initiatives, and cause codes—ensuring language aligns with past submissions and regulatory expectations. The output is a complete, formatted draft report (PDF, XML) ready for human review.

Generated reports are automatically routed through a configured approval workflow. The draft is first sent to a Reliability Engineer for technical validation, then to Regulatory Affairs for compliance check, and finally to the Designated Filing Officer for sign-off. Each step is managed via a system like ServiceNow or a custom task queue, with escalation rules for overdue reviews. All changes, approvals, and rejections are logged in an immutable audit trail required for regulatory defense.

Upon final approval, an agent executes the submission. It securely transmits the filing package to the regulator's portal via API or browser automation, confirms receipt, and downloads the submission confirmation. The final package and all supporting evidence are then archived in the corporate Document Management System (e.g., SharePoint, OpenText) and linked to the general ledger for cost recovery tracking. The agent also triggers internal notifications to leadership that the filing is complete.

The entire workflow is monitored by a dedicated observability agent. It tracks pipeline health, data freshness, calculation drift, and approval cycle times. Using historical baselines, it can flag anomalies—like a sudden spike in SAIDI for a circuit—and alert engineers before the reporting period closes, allowing for investigation and data correction. This transforms reporting from a backward-looking task into a proactive reliability management tool.

Regulators require a verifiable chain of custody for every data point in a SAIDI/SAIFI report. The workflow must tag each outage record with its source system (OMS, SCADA, MDMS), ingestion timestamp, and any transformation logic applied. This is typically implemented via a metadata layer and immutable logging to a system like Datadog or Splunk, creating a defensible audit trail for regulatory review.

Not all outage events map cleanly to reporting rules (e.g., major storm exclusions, planned maintenance). The workflow must detect edge cases and route them to a human analyst for classification via a queue in ServiceNow or Jira. The agent must preserve context—raw data, its proposed classification, and the relevant rule—to accelerate review and document the rationale for any override.

A utility operating across state lines must apply different PUC formulas for customer minutes interrupted (CMI), exclusion criteria, and reporting formats. The core architecture uses a rules engine (e.g., Drools, custom YAML configs) that agents query with geotagged outage data. This separates volatile regulatory logic from the stable data pipeline, allowing compliance teams to update rules without redeploying the entire workflow.

The final stage involves orchestrating agents to compile validated data into the precise PDF, XML, or portal forms required by each regulator. This uses a document agent (e.g., via LangChain's document assembler) to populate templates, followed by a routing agent that submits via SFTP, API, or web portal simulation. The workflow must capture submission receipts and log them to the central audit trail.

To prevent reporting errors, the workflow must include validation agents that run parallel calculations using historical data to ensure new reports align with past submissions and expected statistical ranges. Discrepancies trigger alerts before submission. This backtesting loop is critical for catching systemic data drift or logic errors introduced during system updates.

A practical rollout starts with a single jurisdiction or report type. The build requires robust connectors to legacy Outage Management Systems (OMS) and Customer Information Systems (CIS), often using a combination of REST APIs, message queues (Kafka), and—for mainframes—browser automation agents (Playwright) to extract data where modern interfaces don't exist. This phased approach de-risks the integration and demonstrates ROI early.