Waste taxonomy

Xerg classifies spend into two groups:

• confirmed waste
• savings opportunities

This distinction is important because only confirmed waste contributes to wasteSpendUsd, structuralWasteRate, and CI threshold checks.

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The current finding kinds

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Retry waste

Classification: waste
Confidence: usually high when explicit failed attempts are observed, medium when a later retry attempt is used as a proxy Xerg counts retry waste from explicit retry evidence when the source records it: the strongest case is a failed or aborted attempt followed by a later explicit retry attempt in the same run. If only the later retry attempt is visible, Xerg may count that later attempt as inferred retry overhead with lower confidence. Session transcripts (OpenClaw sessions, Claude Code) carry no explicit attempt fields, so Xerg uses transcript inference there: an errored call followed by a same-model call within ten minutes counts the failed call’s spend as inferred retry overhead. Typical fix:

• add exponential backoff
• reduce retry count
• avoid retrying known non-recoverable failures

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Context bloat

Classification: opportunity
Confidence: medium For workflows with at least three runs, Xerg looks for runs whose input token volume is far above that workflow’s own baseline. This is a directional context-trimming signal, not proven waste. Typical fix:

• trim prompt scaffolding
• send less repeated context
• split broad tasks into smaller scoped calls

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Loop waste

Classification: waste
Confidence: high When a source records iteration numbers and a run reaches at least seven iterations, Xerg treats spend from iteration six onward as loop waste. If iteration structure is missing, Xerg suppresses loop waste rather than inferring it from a flat log. Typical fix:

• add an iteration cap
• add an early exit condition
• tighten workflow guardrails

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Tool-loop waste

Classification: waste
Confidence: medium (inferred) When a source exposes tool identity, four or more consecutive calls invoking an identical tool sequence indicate an agent stuck re-running the same operation. Spend beyond the second repetition is counted as likely loop waste. Typical fix:

• detect repeated identical tool calls and stop after a few repeats
• add a progress check between iterations
• surface the loop to the user instead of retrying silently

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Context growth

Classification: opportunity
Confidence: medium Within a single session, when effective context (input plus cache reads) at least doubles from its early baseline and ends above 50k tokens, every later call is paying for the accumulated context. Earlier compaction or trimming compounds savings. Typical fix:

• compact the session before context reaches the flagged size
• trim stale tool results from history
• split long sessions into fresh ones

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Cache churn

Classification: opportunity
Confidence: medium When a workflow writes far more tokens to prompt cache than it ever reads back, the cache-write premium is not paying for itself. This usually means the prompt prefix changes between calls and invalidates the cache. Typical fix:

• keep the system prompt and early context byte-stable
• move volatile content out of the prompt prefix
• verify cache hits with provider usage reports

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Downgrade candidates

Classification: opportunity
Confidence: low Xerg flags expensive models on workflows that look operationally simple, such as heartbeat, triage, summary, tagging, or monitoring style tasks. This is intentionally an A/B test candidate rather than a claim of proven waste. Typical fix:

• test a cheaper model on the same workflow
• compare quality and cost before changing defaults

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Idle waste

Classification: opportunity
Confidence: medium Xerg looks for cadence evidence: five or more runs firing at a near-constant interval with near-identical size, whatever the workflow is named. The spend may still be justified, but the cadence and model tier are worth reviewing. Typical fix:

• reduce cadence
• move to event-driven triggering
• use a cheaper model for routine checks

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Cache carryover

Classification: waste
Confidence: medium For Cursor usage audits, Xerg flags extreme long-session cache carryover as waste when too much stale cached context is being dragged through the session and the billed pattern clears the waste threshold. Typical fix:

• summarize long chats into a short recall note
• start a fresh chat
• carry forward only the facts that still matter

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Max mode concentration

Classification: opportunity
Confidence: medium For Cursor usage audits, Xerg flags workflows or usage patterns that lean too heavily on max mode instead of escalating selectively. Typical fix:

• default to standard mode
• escalate only when standard mode fails
• review auto-escalation habits

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Confidence levels

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Provenance

Findings also carry local provenance fields in the core summary: Older summaries can lack signalSource. Xerg treats those findings as unknown in local rollups instead of assuming they were observed.

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Why this matters in the product

• retry-waste, loop-waste, tool-loop, and qualifying Cursor cache-carryover roll up into confirmed structural waste when their required structure is present
• context-outlier, context-growth, idle-spend, candidate-downgrade, cache-thrash, and Cursor max-mode-concentration roll up into opportunity impact
• the Action queue and hosted Optimizations backlog may prioritize an opportunity even when it is not confirmed waste

For the recommendation object shape returned in JSON mode, see findings and recommendations.