Data Engineering Architectures and Token-Efficient Tooling for Large-Scale Event Handling in Modern Agentic Artificial Intelligence Systems

Abstract

Modern agentic artificial intelligence systems increasingly reason over high volume enterprise event streams that arrive from clickstreams, sensors, transactions, and application telemetry. Two forces meet in these systems. Data engineering must ingest and reduce millions of events per interval, and the language model that drives the agent must observe those events within a strict token budget. This paper connects the two concerns with a single mathematical and algorithmic treatment of large scale event handling for token efficient agents. We model the reduction pipeline as a sequence of mergeable window operators and streaming sketches, and we formulate the agent facing cost as a token budget problem in which data engineering choices set the size of every observation. We implement the full pipeline from first principles and report reproducible measurements on a stream of one million synthetic events across four domains. A single reduction pass processes about 2.6 million events per second, tumbling window aggregation reduces row counts by factors between 1.80 and 2.11, HyperLogLog cardinality estimates track the theoretical one point zero four over root m error from 11.7 percent at 64 registers down to 0.81 percent at 4096 registers, a Count Min sketch lowers heavy hitter error from 34.8 percent to 0.32 percent as its width grows, and reservoir sampling holds the sampled domain mix within 0.079 percent of the population. Feeding an agent windowed summaries rather than raw events cuts the observation token count by 39.1 percent, and a transparent episode cost model shows that this reduction lowers per episode token consumption by 38.7 percent for an eight round event driven agent. The results give practitioners a rigorous basis for pairing data engineering with token budgets in agentic deployments.