From precision farming to personalized nutrition, agentic AI is poised to alter every aspect of the food industry. But first, we have to understand the technology. Let’s take a look under the hood. 

What is agentic AI?

AI agents are software entities designed to perceive their environments, make decisions, and take actions to achieve specific goals. Agentic AI takes this further, focusing on autonomous decision-making and action, with minimal human intervention. These systems act as autonomous agents on behalf of human users and other AI systems. They can understand objectives independently and make decisions, initiate actions, and set priorities accordingly. They can even communicate and collaborate with other AI systems and digital infrastructure while learning from interactions to improve future responses.

As industries look to streamline processes, boost efficiency, reduce costs, and find creative solutions to problems, these advanced forms of AI have emerged to fast-track their progress. Investors know it too. Deloitte reports that companies developing agentic AI have received more than USD 2 billion in funding over the past two years (excluding OpenAI), particularly those targeting the enterprise market.

Agentic AI in the food industry

The food industry is rapidly transforming with the introduction of agentic AI into the mix, with the tech being used in several areas, including food ordering and inspections, personalized nutrition, supply chain automation, and crop monitoring. The introduction of autonomous decision-making capabilities to these areas is helping industry players enhance efficiency, innovation, and sustainability, adapting their products and services to modern consumer demands.

Broad use cases of agentic AI in the food industry

1. Real-time production monitoring and adjustments

• Agentic AI can interact with its environment, collecting real-time data from sensors and making decisions to adjust production processes. This ensures optimal conditions are maintained without human intervention.
• It can detect anomalies in production and take immediate corrective actions, reducing downtime and improving efficiency.

2. Predictive supply chain management

• Agentic AI can analyze real-time data on weather patterns, market changes, and supplier performance to predict supply chain disruptions. It suggests alternative suppliers or adjusts sourcing strategies to avoid delays and shortages.
• It can help companies adapt quickly to changes in supply chain conditions, ensuring consistent supply and reducing the risk of stockouts.

3. Quality control

• It can monitor production processes in real time, detecting small quality issues and recommending immediate adjustments. This also ensures products remain consistent across all batches.

4. Sustainability and waste reduction

• Agentic AI can analyze past and present data to optimize production schedules and ingredient sourcing, reducing waste by predicting demand and spoilage risks.
• It can suggest sustainable sourcing options.

5. Advanced product development and innovation

• AI agents can simplify regulatory compliance by automatically monitoring safety standards, ingredient regulations, and labeling requirements across different regions.
• It can accelerate product development by automating benchmarking, suggesting base formulas, and optimizing nutritional content and consumer acceptance.

6. Restaurant management and customer experience

• The tech can be used to create virtual managers that can handle routine tasks.

What’s been cooking?

In our latest research report on agentic AI in food, Speeda Edge identifies 11 key areas in the food industry in which agentic AI could have the biggest impact. These included crop inspection and maintenance, distribution management, climate control, and quality control. By classifying these against several examples of agentic AI use in the industry, it was revealed that the tech is most commonly being used for agriculture, food production, and supply chain functions. Here’s a taste of what’s been cooking in some of these areas:

Crop inspection, maintenance, and climate control

AI-enhanced tools can integrate high-resolution field imagery with environmental data to provide recommendations to optimize farming practices and even optimize greenhouse climates and irrigation based on specific grower targets. Taranis’ AI agent, “Ag Assistant," provides farmers and agronomists with tailored recommendations specific to each field and evaluates various factors, including seed type, soil conditions, and weather patterns, also offering actionable advice. Meanwhile, Blue Raddix’s autonomous climate and irrigation control system uses AI to optimize the climate and irrigation within greenhouses according to the grower's targets.

Food ordering, recipe creation, and preparation

AI assistants help improve order accuracy and reduce service times through real-time replies, while interactive AI tools can personalize recipe recommendations, digitize recipes, and offer real-time problem-solving. Fast food chain Wendy’s launched Wendy’s Fresh AI assistant in its drive-thrus, which uses GenAI to respond and adapt in real-time. This tech has cut service times by 22 seconds, achieved a 99% success rate in handling orders, and recorded an 86% accuracy rate for orders completed without staff intervention. Meanwhile, NotCo’s Giuseppe AI platform studies the composition, taste, texture, and appearance of animal products to develop plant-based alternatives. 

Distribution management

AI tools that use specialized algorithms and comprehensive data analytics to forecast product demand and recommend solutions for disruptions. Kraft Heinz’s AI-powered software, Lighthouse, is probably the most significant example of this. It manages its entire supply chain and predicts factors that could impact product delivery using a specialized algorithm and data from suppliers, factories, and distribution centers.

Other notable developments in the industry include Precitaste’s launch of an AI-powered kitchen management platform, John Deere’s AI-driven crop monitoring system, and Omdena’s AI-powered solution for food quality control.

It’s time to pay attention

Agentic AI has emerged as the next big wave in AI tech, moving beyond generative AI’s limitations. 2025 has been hailed as “the year of AI agents”—the year in which they finally break into the mainstream—and we are already seeing the signs. Companies are beginning to integrate agentic AI into their workflows, with one report by Deloitte predicting that 25% of companies that use GenAI will launch agentic AI pilots or proofs of concept in 2025, growing to 50% by 2027.

Another survey by Gartner predicts that by 2028, one-third of enterprise software applications will include agentic AI (up from less than 1% in 2024), enabling 15% of the everyday work decisions to be made autonomously.

Consumers have also shown a keen interest in personalized customer service, preferring the tailored interactions that can be delivered by agentic AI. A Nielsen report indicated that these sorts of personalized interactions could lead to a 10% increase in customer satisfaction rates.

Furthermore, industry challenges like sustainability, waste reduction, and environmental impact have also intensified the need for increased adoption of agentic AI in the industry.

Conclusion

The modern-day demands of the food industry are numerous and complex. Consumers demand personalized experiences, sustainability, and health-conscious as well as ethically sourced products, while rapid environmental and weather changes have made constant crop monitoring and predictive analytics even more crucial. By leveraging agentic AI, disruptors and major players in the industry are beginning to find a competitive edge in bringing their products and services into the future.

With major players already embracing AI-driven strategies and the market poised for substantial growth, the adoption of agentic AI is no longer a choice but a necessity for businesses seeking to remain competitive. For a more in-depth look at the subject, download the Speeda Edge report on agentic AI in the food industry.