Agent-Aware
Diagnostics.
Multi-agent workloads suffer from coordination and communication bottlenecks that conventional tools miss.
Explain why agent workflows slow down, retry, or stall — beyond what APMs can see.
Built for AI platform and infrastructure teams running multi-agent systems.
$ iocane connect
[INFO] Detecting environment... Docker Compose found.
[INFO] Instrumenting services with OpenTelemetry...
Scanning worker-node... OK
[SUCCESS] Environment connected to iocane cloud.
$ _
The Microservices Trap
Traditional observability tools model services and requests — not agents, coordination, or shared decision-making. As agent count grows, coordination overhead — not compute — becomes the dominant source of latency. Thus, In practice, teams blame the model or the GPU, when the real cause is agent contention.
Traditional APMs see "Service A calling Service B".
iocane sees "Planner waiting for Worker due to token starvation".
Why Agent-Aware?
iocane provides the missing semantic layer between your agents and the infrastructure they run on.
Explain Bottlenecks
Identify when communication between agents becomes the primary bottleneck, not just compute.
Reveal Contention
Discover when multiple agents compete for CPU, memory, or bandwidth, causing cascade failures.
Highlight Timing
Detect timing dependencies and stale information patterns that lead to hallucinations.
GPU Waste Elimination
Expose idle GPU time caused by agent traffic contention.
Framework-agnostic
LangGraph, CrewAI, custom agents.
Policy-First Layer
Define policies that automatically mitigate contention and prioritize critical agent traffic in real-time.
Built-in Failure Detectors
Each detector explains why latency happened and what to change.
Fan-out Collapse
Recognizes when a planner spawns too many parallel calls, saturating shared resources.
Blocking Chain
Identifies long critical-path dependencies—like too many people trying to exit through one door.
Retry Storm
Detects correlated retries across agents that amplify load on backend models.
Token Starvation
Observes when long-lived token streams degrade as bulk traffic grows. Long-lived token streams degraded by background fan-out and retries.
Who uses iocane?
AI Platform Teams
Install iocane into your agent framework to detect fan-out collapses in orchestrations built on LangGraph or CrewAI.
Infrastructure / SRE
Conventional tools only see service-level metrics. Use iocane to diagnose p99 latency spikes and resource saturation caused by agent loops.
Applied AI Engineers
Bought by platform teams. Used by SREs and agent engineers.