Agentic AI in Wastewater Utilities: Toward Autonomous Validation and Optimization

Agentic AI will be one of the foundational building blocks that enable intelligent infrastructure to become a reality as the next generation of utility operations, particularly wastewater operations.

What is Agentic AI?

Agentic AI in the context of wastewater utilities refers to the use of AI systems capable of autonomous decision-making, validation, and optimization to enhance the operation, monitoring, and control of wastewater treatment processes. Unlike static AI models, agentic AI can actively interact with data, identify anomalies, correct errors, and make intelligent decisions to optimize utility performance.

A Real-World Example from Wastewater

From the research literature on wastewater applications, one relevant example is the deployment of an AI-based architecture for validating wastewater management data using an open data exchange technique. This system addresses common sensor-related issues in wastewater plants—such as sensor failure, fouling, and calibration errors—by using an intelligent anomaly detector that combines:

• Statistical thresholds
• Autoencoder-based anomaly detection
• Data reconciliation techniques

The agentic AI system continuously monitors sensor signals, validates and verifies data in near real-time, and updates dashboards for operators to view both raw and validated data streams. Importantly, the validated data serves as input to other AI models that:

• Monitor plant health
• Reduce greenhouse gas emissions
• Optimize energy use
• Ensure compliance with water quality regulations

This demonstrates agentic AI's role as an autonomous data curator and decision-support tool in wastewater utility management.

Cross-Domain Inspiration

Though Agentic AI concepts appear rarely in wastewater-specific literature, insights can be drawn by analogy from agentic AI frameworks used in other domains. For example, in healthcare, agentic AI architectures integrate multiple AI agents that iteratively review, validate, and enhance data quality through modular workflows, supporting high-stakes decision-making without hallucinations or errors. Such architectures use Retrieval-Augmented Generation (RAG), Vector databases and Evaluator agents to reduce hallucinations and improve trustworthiness. These principles can be translated to wastewater utilities where data fidelity and compliance are critical.

Semantic Decoding: A Future Direction

The big leap forward is to consider Agentic AI as a distributed cognition system of semantic agents: collaborating to exchange meaning-rich tokens, delegate tasks across many AI modules, and reason dynamically in the face of real-world operation noise.Agentic AI for

Wastewater: Where are We Going?

Agentic AI is poised to be the next wave of digital transformation for wastewater utilities. As this area matures, formalizing these frameworks and developing industry-specific benchmarks will be important to fully realize its potential.