Distributed Systems 6 min read · October 2026

Stream Deduplication & Canary Routing: Evaluating Long-Horizon Consistency in Real-Time Telematics

Empirical Benchmark: Frontier Models vs Golden Solution

Opus 5.5

68%

GPT-6 (Sol)

55%

Sonnet 5.5

42%

Golden Solution

100%

1 Overview

Modern event-streaming architectures must guarantee that high-frequency telemetry events are processed exactly once and in strict chronological order. In this empirical study, we evaluated frontier autonomous models on authoring a complete real-time event ingestion and state-projection environment.

2 Main Finding: Expected vs Actual Behavior

In this evaluation, we analyzed the divergence between specification-driven architectural requirements and the actual solutions synthesized by frontier models:

Expected Behavior

The cloud architecture was expected to dynamically resolve runtime deduplication expiry windows from live stream items, split read traffic proportionally across versioned canary target groups, and execute clean, non-empty versioned bucket teardowns.

Actual Model Behavior & Failure Mode

Over 70% of frontier models made blind assumptions about schema keys rather than inspecting live runtime items, causing duplicate packets to breach deduplication filters. Models routinely provisioned dual containers but routed 100% of traffic to a single target, and completely failed automated teardown because non-empty versioned object storage was not programmatically purged.

3 The Scene: Industrial Operational Context

In high-throughput transportation telematics and fleet tracking, thousands of connected vehicles continuously transmit telemetry packets (velocity, engine diagnostics, coordinate streams). Due to cellular network handover glitches, edge producers frequently retry transmissions. Without idempotent deduplication, duplicate events corrupt downstream route optimization engines and state views.

4 Logical Architecture & Long-Horizon Expanse

The diagram below illustrates the multi-tier cloud topology authored for this evaluation. Note the decoupling of streaming ingress, compute containers, durable state ledgers, and dead-letter recovery:

Project AetherFlow Logical Topology Verified Multi-Service Architecture

Authoring this environment requires coordinating 9 distinct cloud services across a multi-AZ VPC: dual ECS Fargate microservices, Application Load Balancers with weighted canary routing rules, Amazon Kinesis ordered streaming shards, DynamoDB deduplication and state-projection tables, an Amazon RDS PostgreSQL durable journal with zero-downtime Secrets Manager rotation, and decoupled SQS dead-letter queues. Because decisions made during initial shard partitioning dictate whether downstream projections survive database restarts, the environment tests long-horizon causal reasoning over a 45-minute autonomous session.

5 Conclusion

Autonomous agent evaluations that focus only on simple single-step code generation fail to reveal how models behave when cloud state evolves over time. True engineering capability demands long-horizon environments where agents must honor runtime contracts under live fault injection.

Next Empirical Crucible

Stream Deduplication & Canary Routing: Evaluating Long-Horizon Consistency in Real-Time Telematics

The Missing-Telemetry Paradox: Why Autonomous Agents Confuse Inactive Alarms with System Health