How can utilities deploy AI agents for utility workflows without building everything from scratch?

Bidgely gives utilities production-ready AI agents for the workflows they already run, so teams do not build an agent layer from the ground up. Every agent runs on the pre-trained utility models behind the UtilityAI Platform, using appliance-level data, behavioral profiles, and DER signals. Prebuilt agents ship ready for production rather than as templates that need months of configuration. A utility can start with one agent, then add others on the same deployment without rebuilding the intelligence layer underneath. Prebuilt agent examples include high bill complaint analysis for call center teams, transformer loading monitoring for grid operations, and Integrated Resource Planning for regulatory and rates.

What kinds of utility workflows can Agentic Automation support across customer, grid, and regulatory teams?

Bidgely's Agentic Automation covers three workflow domains from one deployment. For customer experience and call center: high bill complaint analysis, IVR personalization, program enrollment recommendations, and a customer-facing energy assistant. For grid planning and operations: transformer loading monitoring, outage root cause analysis, non-wires alternatives recommendation, and current-state DER analysis. For regulatory and rates: Integrated Resource Planning, market potential studies, cost and benefit analysis for regulatory filings, and rate analysis and design. Each agent stays specific to its own workflow.

How can utilities bring AI agents into the systems teams already use?

Bidgely provides prebuilt integrations with CRM, DERMS, ADMS, and BI platforms, so agents appear inside the systems utility teams already use. There is no new interface to learn and no separate tool to adopt. Agent output arrives through the CRM, DERMS, ADMS, and BI tools utility teams already work in, within the same approved deployment.

Can utilities start with prebuilt agents and still build custom ones?

Yes. Bidgely ships prebuilt agents for common customer, grid, and regulatory workflows, and utilities can build unlimited custom agents for workflows that do not match one. Custom agents inherit the same utility intelligence, data environment, and governance posture as the rest of the platform. A team can start with one use case and add agents on the same approved deployment as new needs appear, without a second approval cycle.

Can Agentic Automation run inside the utility’s own environment?

Yes. Bidgely deploys Agentic Automation on AWS, Azure, or GCP inside the utility's own environment. Utility data stays in utility infrastructure, and the utility's security and governance policies apply to every agent, every workflow, and every output. Approval happens once, at the deployment level. Every team gets access, and each new agent inherits the same data governance and security posture as the rest of the UtilityAI Platform.

How does Bidgely compare with generic AI agents or broad automation templates for utility use cases?

Generic AI agents and broad automation templates can run tasks, but they do not start with utility-specific meter intelligence. Every Bidgely agent runs on pre-trained utility models built from appliance-level data, behavioral profiles, and DER signals. Not CIS records. Not general-purpose AI. Utility teams get a utility-specific foundation for workflows across customer, grid, and regulatory use cases.

Agentic Automation

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