Agentic AI in EV Recharging: The Future of Smart Energy

What if your EV charging network could set its own goals and carry them out without waiting for human input?

Agentic AI’s ability to self-direct means it doesn’t require step-by-step instructions to carry out complex tasks. By learning from experience and understanding context, it can make informed choices. The autonomous ability to define its own workflow and interact with external environments to solve complex problems has the EV industry excited about its possibilities.

That all sounds wonderful, but how far have we come and what is possible today in practical business terms?

It’s only been in the last two years that Generative AI tools have entered the mainstream in a big way. These tools, driven by large language models (LLMs), are great for content creation. However, their limits soon became obvious around the requirement for human prompts and an inability to execute multi-step workflows autonomously. Agentic AI fills this gap by making decisions and acting independently.

You can find multiple surveys of IT leaders saying anywhere from 30 to over 50 percent of their organizations are adopting or deploying Agentic AI over the next two years. But like every other advancement, it's only as useful as its ability to transform real business outcomes. So, we should start with understanding what it really is, how it’s structured, and its current real-world capabilities for the EV charging sector.

What is Agentic AI?

The first thing to understand is Agentic AI refers to a broader system that is driven by AI agents (AI-driven software programs). Each agent has specific capabilities relevant to EV network management, such as:

• Knowledge Retrieval Agents: These agents can retrieve and analyze data on charging patterns, energy grid demand, and customer usage to inform operational decisions.
• Prescriptive Knowledge Agents: In the highly regulated energy sector, these agents ensure all actions comply with environmental and electrical grid standards.
• Workflow Agents: These agents execute multi-step processes, like dynamically adjusting charging prices based on real-time demand or managing load balancing across a network of stations.
• User Assistant Agents: These agents help drivers manage their charging needs, from finding available stations to personalizing their charging plans, without needing to start from scratch every time.

Agentic AI’s structure is a system of systems. Each agent is purpose-built to handle specific functions like perception (gathering data from chargers and grids), reasoning (analyzing and deciding on the best course of action), and action (executing tasks like rerouting power or alerting maintenance). They work individually, but also together in orchestrated flows to complete complex objectives.

These systems organize agents into subagents and primary agents. Subagents focus on specialized tasks, while a primary agent oversees them, manages coordination, and ensures outputs from each agent feed into the next step. For an EV network, this could mean one agent monitors station health while another analyzes pricing, all coordinated by a primary agent focused on overall network efficiency.

Use Cases in EV Charging Environments

Agentic AI is no silver bullet for every situation. The key is finding the right use cases where its proven strengths can apply to real-world business outcomes. For EV charging, these outcomes focus on uptime, efficiency, customer satisfaction, and sustainability.

IT and Network Operations

Agentic AI is a powerful tool for managing the complexity of modern EV charging networks. It brings a level of autonomic, proactive, and adaptive management to workflows. This can include AI-powered self-healing systems for autonomous vulnerability remediation and incident resolution, reducing downtime, increasing efficiency, and optimizing performance.

Customer Support

Agentic AI systems can integrate with third-party apps, such as CRM, billing systems, and communication tools to collect relevant data. They can each do their part in a complex customer support process for:

• Automated resolution of common customer issues (e.g., billing questions, charger errors).
• Analysis of consumer data to personalize the charging experience.
• Handling complex interactions that may or may not require a human agent.

This can play out in countless ways across different customer support workflows. It depends on the level of Agentic AI system integration with existing applications and platforms.

Cybersecurity

Agentic AI can transform security and threat management for charging networks by bringing a higher level of autonomy to complex areas like:

• Ensuring data quality and pipeline optimization for secure data management.
• Proactive threat hunting across the network.
• Monitoring internal and external sources for vulnerabilities in both software and hardware.

Agentic AI Industry Use Examples

The reality of moving from the possible to the practical becomes clearer with examples of how the EV sector can use Agentic AI today.

An EV charging network can automate tier-1 Network Operations Center (NOC) tasks by leveraging Agentic AI to streamline operations and improve efficiency, including:

• Continuous monitoring of charger performance metrics, anomaly detection, and alert generation.
• Situational and root cause analysis coupled with automatic corrective action, such as rebooting a faulty charger.
• Predicting potential equipment failures and proactively scheduling maintenance to prevent downtime.
• Monitoring resource utilization, identifying grid bottlenecks, and automatically adjusting resource allocation to optimize performance and reduce costs.

By automating incident resolution and resource management, Agentic AI can contribute to building self-healing charging networks, which automatically identify and resolve issues with minimal human intervention. These approaches reduce manual intervention, lower operating costs, improve predictive maintenance, and enhance customer satisfaction.

Benefits to the EV Charging Business

Any EV charging provider with a sound planning and implementation strategy can achieve big Agentic AI benefits like:

• End-to-end automation of complex processes, from grid load balancing to customer billing.
• Multiple agent collaboration coordinated by a management agent to handle complex, interconnected tasks efficiently.
• Autonomous adaptation to changing scenarios, such as sudden peaks in demand or fluctuations in energy prices.
• Proactive issue resolution, including predictive maintenance alerts and real-time supply chain adjustments for spare parts.

The foundation of any Agentic AI strategy is accounting for implementation challenges.

Implementation Considerations

Organizations must have a sound strategy for dealing with challenges integrating Agentic AI systems into their operations, starting with:

• Documenting business processes, workflows, and knowledge to determine the best place to apply Agentic AI within the charging network.
• Organizing IT and data infrastructure to ensure robust data management, cloud architecture, and API integration.
• Defining human and machine integration to balance autonomy with oversight for agent output validation and regulatory compliance.
• Adopting change management practices to smooth the workflow transition while providing training opportunities.
• Defining clear guardrails and governance practices to drive agentic AI implementation, including audit trails and predefined decision boundaries.

Moving Beyond Hype to Practical Help

At Solugenix, we partner with you to move beyond the hype of Agentic AI to the reality of what is possible, practical, and drives real business outcomes in the EV industry. Most solutions today are semi-autonomous rather than fully autonomous, but they can still deliver important business benefits. The goal is to make better products, services, and workflows. We start by asking: does it serve the users better?

We focus on partnering with organizations to help guide Agentic AI adoption in our specific areas of expertise, which include:

• Contact and support center services transformation, where Agentic AI is a natural next step to current AI and automation integration.
• IT Ops/NOC, where ITSM transformations are taking hold, meaning even semi-autonomous Agentic AI can yield massive benefits for network uptime and reliability.

What the Future Holds

The near future of Agentic AI in EV charging involves multi-agent ecosystems where agents collaborate to achieve complex objectives through orchestration. Multimodal AI will begin merging with robotic process automation (RPA) to enable seamless, end-to-end workflows, from a customer’s voice command to the physical act of charging.

Agentic AI marketplace platforms will connect buyers and sellers of AI-powered agents, making it easier to discover what works for specific business needs. Platforms and applications with embedded Agentic AI and agent orchestration are already being introduced, including pre-built platforms, hardware, and frameworks from major tech vendors.

Understanding where we are with Agentic AI and where we’re going can make for a complex roadmap. Plotting the right course based on specified workflows and business outcomes is about moving from decision support to decision execution.

Are your systems ready to think and act on your behalf? Contact the experts at Solugenix for a consultation to explore pilot opportunities.