May 2026 Summaries

In the context of retail, e-commerce, and marketplace operations, DeltaStream provides a crucial solution for AI agents by transforming raw data streams into fresh, stateful, prebuilt context, enabling these agents to make accurate and reliable decisions in real-time. Unlike the traditional runtime raw-data assembly, which often results in incomplete context and wrong decisions, DeltaStream offers a comprehensive, continuously updated context that significantly improves the correctness of AI decision-making while reducing tool calls and token usage. This prebuilt context allows even smaller, cost-effective models to achieve high accuracy in complex decision-making tasks, such as order promises, fulfillment, and recovery, ultimately enhancing customer experience and maintaining trust. The approach mitigates the risk of broken promises due to outdated or incomplete data, showcasing the importance of having a prebuilt context for AI agents to navigate the dynamic and intricate web of retail operations effectively.

DeltaStream is a logistics platform designed to provide real-time, prebuilt context for AI agents dealing with complex logistical challenges, such as critical healthcare shipments. It continuously processes raw data from various sources, such as shipment status, vehicle telematics, and weather conditions, to create a comprehensive operational context. This context allows AI agents to make more accurate and timely decisions by reducing the need for multiple tool calls and minimizing token usage. A benchmark comparing DeltaStream's prebuilt context approach to traditional runtime raw-data assembly showed significant improvements in decision-making accuracy and cost efficiency, even enabling smaller models to outperform larger ones when equipped with the right context. DeltaStream's role in logistics is to transform raw data into actionable insights, thus enhancing the efficiency of logistics exception management and making smaller, cost-effective AI models viable for production.

Airline operations face complexities that necessitate AI agents having fresh, prebuilt context to make informed decisions, as demonstrated in the case of a Flight Disruption Manager Agent. The DeltaStream platform excels by continuously transforming raw airline operation data into ready-to-use, real-time context, which is crucial for making accurate operational decisions. This prebuilt context approach significantly outperforms traditional runtime data assembly, as evidenced by a benchmark showing that both large and small models achieved 100% accuracy with DeltaStream context, compared to poor performance without it. DeltaStream not only enhances accuracy but also reduces token usage, tool calls, and costs, making smaller, less expensive models viable for production. The platform achieves this by performing complex real-time data processing, including stream ingestion and stateful joins, ensuring the agent receives the most current and comprehensive operational truth to make timely, correct decisions.

AI agents are particularly suited for cybersecurity, where Security Operations Centers (SOC) face overwhelming alerts and require precise context to triage incidents effectively. Instead of relying on runtime raw-data assembly, which often fails due to fragmented and incomplete data from various systems, DeltaStream offers a solution by continuously building a comprehensive, stateful security context. This context is essential for accurate decision-making, as demonstrated by benchmarks where AI models using DeltaStream's prebuilt context outperformed those relying on runtime data assembly. DeltaStream reduces the need for large models, decreases tool calls, and lowers token usage by providing agents with a fresh, pre-assembled security context, enabling them to act with precision and efficiency. This approach not only improves accuracy but also enhances cost-effectiveness and operational safety, making it an invaluable tool for cybersecurity tasks such as incident response and threat hunting.

AI agents transitioning from demos to production require high-quality, fresh context to make accurate decisions in complex operational scenarios, such as banking and customer support. DeltaStream offers a solution by providing a real-time context platform that processes raw operational events, aggregates data, applies policy, and detects patterns to deliver pre-computed, stateful context to AI agents. This approach improves the correctness of agents' responses, reduces tool calls and token usage, and addresses the limitations of runtime context assembly, which can fail due to the need for stateful joins, rolling-window aggregations, and policy evaluations. DeltaStream's prebuilt context layer ensures AI agents have the necessary information to answer questions accurately and efficiently, making it essential for production-ready AI systems in dynamic environments.