Use case · Supported evaluation

Agent fan-out control

Croutons can evaluate how planners, agents, retries, retrieval, and tool calls amplify inference consumption across a completed task.

Problem

A single user task fans out into many model and tool calls through planners, sub-agents, retries, and retrieval loops.

Operational symptoms

• 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 experiences it

Teams operating multi-agent or tool-using AI systems in production

What Croutons evaluates

• Unnecessary model calls
• Retry loops
• Agent execution depth
• Tool-call amplification
• Retrieval fan-out per task

Inputs required

• End-to-end traces for completed tasks
• Agent/planner configuration
• Tool and retrieval inventories
• Retry and timeout policies

Method

Reconstruct fan-out for representative tasks. Attribute amplification to planner depth, retries, tools, and retrieval. Recommend depth limits, reuse, and gating controls.

Deliverables

• 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.

Limitations

• Requires traces that show the full agent path
• Does not rewrite agent frameworks
• Some fan-out is necessary for quality; analysis separates waste from required work

Evidence or claim basis

Approved Croutons inference-diagnosis methodology

No external evidence sources attached. Claims rest on the approved internal methodology above. Repository file paths are content sources, not factual provenance.

Reviewed 2026-07-18 · Croutons product · status: supported