Focused review for AI agent spend leaks

AI Agent Cost Leak Review

A USD $299 focused review for one AI agent cost leak: context bloat, model-routing drift, tool-call loops, retry storms, cache misses, oversized RAG chunks, verbose traces, or one workflow where token spend is rising faster than product value. The output is a spend map, ranked leak drivers, and a fix checklist for lowering recurring LLM cost without breaking the workflow.

Request cost leak review Cost spike emergency Open TokenMeter Quick scan packages View full sprint quote

EntryUSD $99 Quick Scan

FocusedUSD $299 AI Agent Cost Leak Review

EmergencyUSD $1,000 AI Cost Spike Emergency Sprint

FullUSD $1,000 Full Audit Sprint

Start RulePayment only after written scope acceptance

When to use it

Use this when usage grows but confidence does not

This package is for builders who already have an agent, chat workflow, RAG pipeline, coding-agent task runner, or tool-using product in motion and need a narrow outside pass on why the bill is high.

Agent loops: tool retries, planning loops, self-correction passes, browser steps, code edits, or test reruns that burn tokens silently.

Context bloat: giant prompts, repeated system text, full logs, oversized file snippets, stale memory, or untrimmed conversation history.

RAG cost: over-broad retrieval, duplicate chunks, rerank fanout, embedding waste, or low-value context sent to expensive models.

Model routing: tasks using frontier models when a smaller model, cached input, batch path, or deterministic code path is enough.

Observability gap: missing per-feature, per-customer, per-tool, or per-run cost attribution before launch or pricing changes.

Review checklist

What I check in the cost boundary

The review follows one expensive path from user action to prompts, retrieval, tool calls, retries, model choices, cached inputs, logs, and billing attribution.

Token budget by stage: system prompt, user context, retrieved context, tool output, scratchpad, reasoning traces, and final response.

Loop and retry controls: max iterations, retry backoff, duplicate tool calls, repeated test runs, and failure-mode spend caps.

Model routing policy: cheap-path candidates, fallback rules, context-window thresholds, batchable work, and escalation triggers.

Cache and reuse: stable prompt prefixes, reusable summaries, retrieved-context dedupe, cached input eligibility, and avoidable prompt churn.

Instrumentation: request IDs, tool span IDs, per-customer cost tags, redacted traces, and daily guardrails before a bill spike becomes invisible.

Pricing readiness: whether the product price, free tier, or internal budget can survive realistic agent usage.

USD $299 output

Focused review deliverables

The focused package reviews one agreed workflow or cost path. It is designed to turn a confusing LLM bill into specific product and engineering changes.

Request $299 cost review

Spend map: user action, model calls, token volume, retrieval, tools, retries, caches, and billing attribution for one workflow.

Ranked leaks: concise findings with estimated impact direction and lowest-risk fix path.

Reduction checklist: prompt trimming, retrieval limits, model-routing changes, cache candidates, loop caps, and observability gaps.

Pricing guardrails: package, free-tier, or internal budget checks for the selected workflow.

Upgrade path: if the review expands into security, auth, SSRF, or full agent launch risk, the full USD $1,000 sprint can be scoped separately.

Cost evidence

Start with a calculator snapshot or sanitized trace

TokenMeter can estimate model spend for a workload, and a sanitized trace can show where the agent is actually spending. Do not paste private prompts, customer data, API keys, or proprietary logs in public issues.

SignalRunaway token spend, repeated tool loops, or model-routing drift

ToolUse TokenMeter to estimate monthly LLM spend

EvidenceAttach sanitized request counts, model mix, token totals, retry counts, or a redacted trace summary

GuardrailNo secrets, private prompts, customer records, API keys, or raw production traces in public issues

Escalate when it is urgent

Use the $1,000 emergency sprint when the bill is already spiking

If one focused leak is too narrow because the agent is already generating runaway spend, use the AI Cost Spike Emergency Sprint instead. It is scoped for a 24h containment plan after written acceptance and payment confirmation.

View emergency sprint Open emergency intake

Best fit: daily cost spikes, failed launch pricing, repeated tool loops, retry storms, or unclear model-routing waste.

Output: spend-driver map, containment plan, kill-switch candidates, loop caps, cache candidates, and cost instrumentation checklist.

Evidence: TokenMeter snapshot, sanitized usage totals, model mix, retry counts, billing-period delta, and redacted trace summary.

Payment packet

Copy after scope acceptance

Payment is requested only after the written scope is accepted. The review can use public repo context, public docs, screenshots, summarized usage numbers, or a sanitized trace.

Submit payment proof Review terms

Copyable packet

I accept the AI Agent Cost Leak Review package.

Package: USD $299 AI Agent Cost Leak Review
Scope: [one agent workflow, RAG path, coding-agent loop, model-routing path, or token spend issue]
Delivery: [public issue comment or private Markdown report]
Payment timing: after written scope acceptance only.

Ethereum address (ETH or ERC-20 USDC/USDT/DAI):
0xa7F2235a77FBc4eCcbF60923BCDF6Df74eC710FF

Solana address (SOL or SPL USDC):
5CjUaMAsbXx2Hjczwoqi4MChTU1KjfUzbdiwPqZeceVM

Payment proof form:
https://github.com/jackjin1997/agent-audit-sprint/issues/new?template=payment-confirmation.yml