Technology
Why AI Agents Break at Scale
Why AI Agents Break at Scale
The adoption of AI agents in production systems is accelerating rapidly. From customer support to financial trading and healthcare monitoring, AI agents are now responsible for real business-critical actions.
Yet, most teams encounter serious issues once they scale beyond early experimentation.
AI agents do not fail because of model quality.
They fail because orchestration architectures were never designed for long-running, stateful, multi-step intelligence.
This post breaks down where traditional approaches collapse, highlights a real-world production failure mode observed across the industry, and explains why streaming-first architectures are emerging as the foundation for reliable AI agents.
A Real Production Failure: Zombie Inference and Burned GPUs
A recent LinkedIn post captured a failure mode many teams quietly experience in production:
“Our request completion rate dropped, but GPU utilization stayed at 100%. The root cause was zombie requests — inference kept running even after the downstream client disconnected.” — LinkedIn post on production LLM cost spikes
This wasn't caused by a bad model or traffic surge. It was architectural.
What Happened
Clients timed out or disconnected
The gateway didn't immediately notice
Upstream LLM inference continued running
GPUs stayed fully utilized
No user ever received the output
The result: Compute spend skyrocketed while real work collapsed.
This class of issue is especially dangerous because dashboards look "healthy" until finance asks questions.
The Costly Misconception Around AI Agents
Most engineering teams believe LLM inference cost is the primary bottleneck.
In reality, the hidden cost lies elsewhere.
What Teams Assume
LLM API calls are the main expense
Optimizing prompts will fix scaling issues
Disconnects automatically stop work
What Actually Happens
Inference is tied to HTTP request lifecycles
Disconnect does not imply cancellation
Python async cancellation is cooperative
Background tasks continue running off-request
GPUs generate tokens that no one consumes
At scale, these inefficiencies silently dominate cost.
Why Traditional Agent Architectures Fail
Stateless, request-driven architectures were built for short-lived APIs, not autonomous agents or long-running inference.
Key Failure Points
Idle compute waste: CPUs wait while GPUs run, yet billing continues
Zombie inference: Client disconnects do not cancel upstream generation
External state explosion: Redis, DynamoDB, and caches become mandatory
Manual reliability engineering: Retries, checkpoints, and recovery logic are hand-built
Poor observability: No clear lineage from request to inference to outcome
These issues compound under load.
Streaming-First AI Agent Architecture
Streaming architectures solve problems that AI agents naturally create.
Instead of binding inference to ephemeral HTTP connections, streaming-first systems treat inference as dataflow.
Event → Stream Processing → Inference → Sink
In a streaming architecture, inference is triggered by events, not sockets.
There is:
No HTTP disconnect to detect
No late cancellation problem
No assumption that a client is waiting
Inference runs because data exists, not because a connection is open.
2. Explicit workflows, not fire-and-forget
Work is modeled declaratively as a graph:
Inputs
Transformations
Inference
Outputs
Nothing runs accidentally in the background. Every GPU cycle is part of an explicit pipeline.
3. Event lifecycle replaces request lifecycle
In traditional systems, cancellation is reactive:
“Did the client leave?”
In streaming systems, control is proactive:
“Is this event still valuable?”
This allows:
Control events
Abort semantics
TTLs
Fan-out limits
Cost-aware inference decisions
Zombie work becomes an engineering choice, not an accident.
The Economics of Streaming at Scale
At 100,000 requests per hour (≈730M per month):
GCP Vertex AI: ~190,000 USD per month in compute
AWS Bedrock: ~24,000 USD per month in compute
Custom Kubernetes: ~1,800 USD per month in compute
LLM cost (constant): ~117,000 USD per month
Same workload. Same model. Over 100x difference in orchestration cost.
Streaming-First Architecture
Monthly compute: ~2,900 USD
Percent of total cost: ~2–3 percent
The premium is small. The reliability gain is massive.
What This Looks Like in Practice at nstream.ai
At nstream.ai, this philosophy translates into:
Connector-based ingestion instead of request handlers
StreamGraphs that explicitly model inference workflows
Backpressure-aware execution
Observable, replayable, and controllable pipelines
Compute aligned with data value, not network timing
Inference becomes infrastructure, not an HTTP side effect.
Conclusion
Zombie inference is not a bug. It is a symptom of mismatched architecture.
As AI agents scale, systems must move away from request-driven execution toward streaming-native design.
Streaming-first architectures:
Eliminate disconnect-driven compute leaks
Make expensive work explicit
Provide built-in control, observability, and safety
Align GPU spend with real business value
This is the architectural approach taken by platforms like Nstream AI — because at scale, reliability is not optional, and wasted inference is simply too expensive to ignore.
Check out the documentation for more details.