Agent fan-out control
In plain wordsOne question to an AI agent can trigger dozens of hidden calls. We map that chain and show where to cut it.
Croutons evaluates how planners, agents, retries, retrieval and tool calls amplify inference consumption across a completed task.
The problem
A single user task fans out into many model and tool calls through planners, sub-agents, retries and retrieval loops.
Signs you have it
- Token cost per task far above single-call baselines
- Deep tool-call chains with repeated retrieval
- Retry storms on flaky tools
- Latency dominated by sequential agent steps
Who it is for
Teams operating multi-agent or tool-using AI systems in production.
What we look at
- Unnecessary model calls
- Retry loops
- Agent execution depth
- Tool-call amplification
- Retrieval fan-out per task
What to send us
- End-to-end traces for completed tasks
- Agent or planner configuration
- Tool and retrieval inventories
- Retry and timeout policies
How the diagnosis works
Reconstruct fan-out for representative tasks. Attribute amplification to planner depth, retries, tools and retrieval. Recommend depth limits, reuse and gating controls.
What you get
- Fan-out map for sample tasks
- Amplification drivers ranked
- Recommended depth and retry controls
- Pilot measurement plan
Expected outcome
Lower waste per completed task through controlled execution depth, not guaranteed absolute cost floors.
Limits, stated up front
- Requires traces that show the full agent path
- Does not rewrite agent frameworks
- Some fan-out is necessary for quality; the analysis separates waste from required work
Related use cases
Agent reliability analysis
Your AI agent gives different answers, retries too much or fails without telling anyone. We find out why and what to fix first.
Inference cost diagnosis
Your AI bill keeps growing. We show you which models, agents and retries are driving it, and what to change first.
Evaluation-system design
If your tests do not match how your AI fails in real life, they will not catch the next failure.
Agent fan-out control: common questions
What is agent fan-out?
When one task given to an AI agent spreads into many smaller model calls, tool calls, searches and retries behind the scenes. Each one costs money and time, and the user only sees the final answer.
Will limiting fan-out make our agent less capable?
Not if it is done carefully. Some fan-out does useful work. The diagnosis separates required steps from waste, so limits only target loops and repeats that add nothing.
Which tracing tools do you support?
Any trace export that shows the full path of a task: model calls, tool calls and retries with timestamps. JSON or CSV exports from common observability tools are fine.
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