AI Agents for Energy: The Agentic Web Comes to Grids

Utilities aren’t short on dashboards. They’re short on time—and on experienced operators who can stitch together SCADA events, outage tickets, asset health, market signals, and weather forecasts fast enough to act.

That’s why the idea behind the “agentic Web” hits differently in energy and utilities than it does in consumer tech. If the internet is shifting from human-first pages to machine-to-machine negotiations, energy operations are already halfway there: markets are algorithmic, telemetry is streaming, and control actions are increasingly automated. The question isn’t whether AI agents will show up in the control room. It’s whether we’ll deploy them with the identity, payments, and security guardrails that keep a grid safe.

AI agents—autonomous systems that can plan, use tools, coordinate with other agents, and execute actions—could become the “primary users” of utility-facing digital services. That shift changes how we design everything from asset management software to DER orchestration and vendor portals.

What the “agentic Web” means for utilities

The core point of the agentic Web is simple: today’s web is designed for humans, while tomorrow’s web will increasingly be designed for agents talking to other agents. Interfaces, protocols, and trust models will change accordingly.

In utilities, that translates to a move away from:

• Humans clicking through portals to request data, place orders, file interconnection paperwork, or schedule work
• Analysts manually pulling reports to decide “what to do next”

…and toward:

• Agent-to-agent workflows where your utility’s agent negotiates data access, requests proofs, places service orders, and updates plans continuously
• Machine-readable service catalogs for grid services (flexibility, reactive power, congestion relief) rather than human-readable PDFs and web forms

A practical scenario: outage response becomes a multiagent workflow

Picture a winter storm event. Your systems already generate alerts; the bottleneck is coordination.

An agentic approach looks like this:

1. A Grid Ops Agent ingests feeder alarms, OMS outages, AMI “last gasp” messages, and weather nowcasts.
2. A Crew Dispatch Agent checks crew availability, safety constraints, and travel-time forecasts.
3. A Supply Chain Agent confirms transformer and fuse inventory and triggers replenishment if stock is tight.
4. A Customer Comms Agent drafts targeted restoration ETAs and pushes updates through approved channels.

None of this requires a sci-fi “fully autonomous utility.” It requires a secure way for agents to request data, prove who they are, and take limited actions without becoming a liability.

Where AI agents fit in the “AI in Energy & Utilities” stack

Most utilities already use AI for point solutions—load forecasting, image-based inspection, or predictive maintenance. Agents are the connective tissue that turns those point models into end-to-end operational outcomes.

Here’s how I map agents to the familiar energy AI stack:

Predictive maintenance: from prediction to work execution

A model can predict a failure risk. An agent can do something about it.

A maintenance agent can:

• Pull recent historian trends (temperature, vibration, dissolved gas analysis)
• Check maintenance history and warranty terms
• Decide whether to recommend monitoring, a planned outage, or immediate replacement
• Create a work order, request permits, and reserve parts

This is where utilities see ROI: not in the prediction itself, but in reducing the time from “risk identified” to “risk mitigated.”

Grid optimization and demand forecasting: faster loops, fewer handoffs

The biggest hidden cost in grid optimization is coordination latency—waiting for approvals, translating between tools, and reconciling competing objectives.

An operations agent can continuously reconcile:

• Day-ahead and intraday market prices
• Congestion forecasts
• DER availability (batteries, EV charging, flexible loads)
• Feeder and substation constraints

Then it can propose (or execute) control actions within a policy envelope—like dispatching storage or issuing demand response calls—while keeping an auditable trail.

Renewable integration and DER orchestration: agents negotiate flexibility

As DER penetration grows, utilities increasingly depend on external parties: aggregators, C&I customers, microgrids, and EV infrastructure operators.

Agent-to-agent communication makes this more scalable:

• Your Utility Flex Agent broadcasts a flexibility need (kW, location, time window, ramp rate)
• Aggregator agents respond with bids and constraints
• A settlement agent validates performance and triggers payment

That “payment + identity + privileges” triad shows up as a non-negotiable requirement in an agentic energy ecosystem.

Protocols, identity, and payments: the boring parts that matter most

The IEEE Spectrum interview highlights emerging open protocols for agent tool use and agent-to-agent communication (for example, tool-use standards and agent communication standards). Utilities should pay attention for one reason: protocols shape market structure.

Energy already learned this lesson with standards like CIM, IEC 61850, OpenADR, and OCPP. Agent protocols will sit alongside these—not necessarily replacing them, but orchestrating across them.

Agent identity: “who is this agent and what can it do?”

In energy, identity isn’t just authentication. It’s authorization with real-world consequences.

A well-designed agent identity model should encode:

• Operator of record (utility, vendor, aggregator, customer)
• Permitted actions (read-only telemetry vs. dispatch authority)
• Scope (which feeders, which sites, which assets)
• Time bounds (temporary elevated privileges during an event)

My stance: if an agent can affect grid state, it should be treated like a privileged operator account—with stronger controls than a typical enterprise user.

Agent payments: settlement isn’t optional in flexibility markets

As grids become more dynamic, the volume of small transactions rises: a thousand batteries each delivering a few kilowatt-hours of support.

Agents need to:

• Price services in near real time
• Validate delivery against measured data
• Trigger settlement workflows automatically

This is where many “agent demos” fall apart in utilities: they can propose actions, but they can’t close the loop through validated measurement and settlement.

Security risks: why agent autonomy raises the stakes in critical infrastructure

The Spectrum interview doesn’t sugarcoat it: autonomous agents expand the attack surface. In energy, that attack surface includes OT networks, market participation systems, and customer data.

A realistic risk list for utilities deploying agentic AI includes:

• Prompt injection and tool manipulation: an attacker feeds content that causes an agent to execute the wrong tool action (like opening access, changing a setpoint, or exposing data).
• Sensitive data leakage: agents accumulate context—asset locations, vulnerabilities, customer details, credentials. If that context leaks, the blast radius is big.
• Privilege escalation by workflow creep: agents start “read-only,” then someone adds “write” permissions to make the pilot more impressive.
• Multiagent chain compromises: one compromised vendor agent becomes a pivot point into broader ecosystems.

The uncomfortable truth: an AI agent with the ability to act is closer to a junior operator than to a chatbot. Treat it that way.

“Secure-by-design” for energy agents (what I’d require in a pilot)

If you’re building an AI agent program in 2026 planning cycles, I’d start with these hard requirements:

1. Action gating: high-impact actions require explicit human approval (at least initially).
2. Least-privilege tools: separate tools for read vs. write; don’t give a single agent broad credentials.
3. Deterministic logging: immutable audit logs of inputs, tool calls, and outputs.
4. Sandboxed execution: isolate agent runtime from core OT systems; use controlled interfaces.
5. Red teaming as a routine, not an event: automated adversarial testing against your agent workflows.

Utilities already do versions of this in NERC CIP programs and vendor access management. The difference is that agents can act at machine speed, so the controls must be tighter.

How to start: 4 energy use cases that are “agent-ready” now

You don’t need to redesign the internet to get value. You need well-bounded workflows with clear success criteria.

1) Substation and feeder event triage

• Inputs: SCADA alarms, relay events, outage tickets, weather
• Outputs: probable cause hypotheses, recommended switching plans, next-best actions
• Why it works: high value, mostly advisory, measurable outcomes (MTTR reduction)

2) Predictive maintenance to work order automation

• Inputs: condition monitoring, inspection notes, maintenance history
• Outputs: prioritized work orders with parts/permits pre-filled
• Why it works: reduces admin time, improves backlog hygiene

3) DER constraint-aware dispatch recommendations

• Inputs: feeder constraints, DER availability, market prices
• Outputs: dispatch plans within strict policy limits
• Why it works: agent coordinates multiple tools without needing full autonomy

4) Vendor coordination and compliance evidence collection

• Inputs: asset registries, patching evidence, access logs, vulnerability scans
• Outputs: compliance packages, risk exceptions, remediation tracking
• Why it works: heavy on document collection and reasoning, light on OT control

People also ask: do AI agents replace operators?

No—and any vendor implying that is asking for a failed deployment.

What changes is the shape of work. Operators spend less time pulling data and more time validating actions, managing exceptions, and supervising automation. The best deployments I’ve seen treat agents as operational coworkers: useful, fast, and occasionally wrong.

What to ask vendors (and your own team) before you buy an “agentic” solution

A lot of products are being relabeled as agents. Use questions that force clarity:

• What tools can the agent call, and with what permissions?
• What’s the approval model for high-impact actions?
• How do you prevent prompt injection and tool misuse?
• Where is context stored, and how is it protected?
• Can you show an end-to-end audit trail for one workflow run?
• What’s the rollback plan if the agent behaves unexpectedly?

If the answers are fuzzy, the product isn’t ready for critical infrastructure.

The next internet shift will hit utilities first—if we plan for it

The agentic Web isn’t just a new browsing experience. It’s a redesign of how digital work gets done through autonomous, tool-using systems that negotiate and act. For AI in Energy & Utilities, that’s not a side story—it’s the operating model for a grid that’s more distributed, more dynamic, and harder to run with yesterday’s staffing and software patterns.

The teams that win won’t be the ones with the flashiest agent demo. They’ll be the ones that treat agent identity, permissions, auditing, and red teaming as first-class engineering work.

If you’re planning your 2026 roadmap, start small but design for scale: pick one workflow, instrument it end-to-end, and build the security envelope you’ll need when agents move from “recommend” to “execute.” What’s the first operational decision you’d trust an AI agent to prepare—even if you’re not ready to trust it to act yet?