Inference Pipeline Control Coherence

Structural Problem

Inference serving pipelines operate multiple control loops simultaneously: dynamic batching adjusts batch sizes based on queue depth, caching systems manage key-value stores and prompt caches, routing layers distribute requests across model replicas, and auto-scaling policies adjust the number of serving instances. Each control loop is individually rational, but their interaction creates coherence problems under production load.

The structural problem mirrors runtime control coherence (ai.04) but manifests differently in inference contexts: latency SLAs create hard timing constraints, request-level variability is high, and the economic model (cost-per-token, cost-per-request) creates different optimization pressures than training throughput.

System Context

This application operates across the inference serving stack, from request ingestion through model execution to response delivery. The relevant system boundary includes load balancers, request queues, dynamic batching engines, KV-cache management, model execution, and the auto-scaling policies that manage serving capacity.

Diagnostic Capability

• Batching-caching interaction analysis identifying how dynamic batching decisions affect cache efficiency and vice versa
• SLA-aware control coherence assessment evaluating whether control loop interactions maintain latency guarantees under load
• Auto-scaling stability diagnostics detecting oscillation patterns in inference scaling decisions
• Cost-per-token structural attribution tracing cost inefficiency to specific control loop incoherence patterns

Typical Failure Modes

• Batching-latency conflict where increasing batch size improves throughput but violates latency SLAs for queued requests
• Cache thrashing where dynamic batching patterns create access patterns that defeat cache locality
• Scaling oscillation where auto-scaling and load balancing interact to create rapid up-down cycling
• Queue depth instability where request queue management and batching create feedback loops that amplify load variations

Example Use Cases

• Inference architecture assessment: Structural coherence analysis of inference serving pipelines before production deployment
• SLA compliance engineering: Identifying control loop interactions that risk SLA violations under realistic load patterns
• Cost optimization: Structural analysis of which control incoherence patterns contribute most to cost-per-token overhead

Strategic Relevance

Inference serving is the revenue-generating layer of AI operations. Control coherence in inference pipelines directly determines whether SLA commitments are met and whether cost-per-token economics are sustainable. As inference workloads grow and diversify, structural coherence analysis becomes essential for maintaining service quality and economic viability.

SORT Structural Lens

The SORT framework addresses this application through four structural dimensions, each providing a distinct analytical layer.