Your gateway already gives you
- Provider, model, tokens, latency, cost
- User, API key, app, timestamp
- Quota, routing, fallback, basic logs
AI Gateway Audit Layer
Audit AI usage after your gateway.
AgentixAudit summarizes sessions by team and project, detects model overuse, and turns gateway logs into monthly review packs.
Passive event copy
No prompt storage by default
Team/project findings
Audit flow
01
Gateway events
model · tokens · owner · session
02
Session summary
task · complexity · model fit
03
Review action
downgrade · batch · template · attribute
Example finding
Frontier model used for low-complexity repeated work
Recommended action: cheaper model tier or template workflow
The problem
AI gateways are necessary, but their logs are not enough for management review.
A gateway can route calls, enforce keys, record tokens, and calculate cost. It usually cannot answer the question CFOs and AI platform leaders actually ask: was this AI usage reasonable for the work?
What leaders still cannot see
- Was a frontier model needed for this task?
- Could repeated work become a template?
- Should similar calls run as a batch?
- Which project owns this high-cost usage?
Gateway integration
AgentixAudit sits after your gateway as a passive audit layer.
Keep LiteLLM, Azure OpenAI, Bedrock, OpenAI gateway, One API, internal proxies, or your existing routing layer. AgentixAudit receives a copy of usage events and produces audit findings outside the production call path.
1
Existing AI gateway
Routes model calls and records request logs.
Event copy
2
AgentixAudit ingest
HTTP API, webhook, Kafka, JSONL/CSV, S3, database, or OpenTelemetry export.
Audit summary
3
Audit engine
Classifies task, complexity, model fit, value signal, recommendation, and confidence.
Findings
4
Reports and actions
Team/project findings, optimizable spend, review questions, and action tracker.
Passive mirror, not inline blocking
Customer-side summarizer optional
Prompt storage off by default
Works with request-level or session-level data
Audit engine
It turns usage records into structured audit judgments.
The engine does not claim automatic ROI. It produces evidence-backed findings about whether the work was complex, whether the model tier was appropriate, and what optimization action is reasonable.
- Classifies task category, complexity, model fit, value signal, risk flags, and confidence.
- Detects frontier-model overuse, repeated sessions, template candidates, missed batching, and missing owners.
- Rolls findings up by team, project, app, cost center, task type, and model tier.
Audit summary fields
task_category
complexity
model_fit
value_signal
optimization_recommendation
risk_flags
confidence
summary
What it finds
Specific optimization findings that teams can review.
Example finding
$4,800 / month
Frontier model used for low-complexity proposal variants
- Team
- Growth
- Project
- Q3 outbound campaign
- Pattern
- Repeated generation across similar sessions
- Recommendation
- Move to a template workflow or cheaper model tier
Batchable workload
Similar document summaries run one-by-one instead of a scheduled batch process.
Unknown project owner
High-cost sessions are attributed to a team but not to a project, ticket, customer, or business process.
Month-end budget burn
Usage spikes near budget close with weak project attribution and repeated low-complexity tasks.
Outputs
The deliverable is a review pack, not another raw usage table.
Operators get a dashboard for investigation. Executives get a monthly review pack with findings, questions, estimated optimizable spend, and recommended actions.
Team-level report
Spend, model mix, task categories, findings, and action owners by team.
Project-level findings
Which projects justify frontier models, and which need attribution repair or review.
Optimizable spend estimate
Identified spend that may move to cheaper models, batch, templates, or tighter ownership.
Executive questions
Questions for business owners: what outcome justified the spend, and what policy should change?
Privacy boundary
Default workflow stores metadata and audit summaries, not full conversations.
For privacy-sensitive teams, summaries can be generated inside the customer environment and only metadata plus structured audit fields are written to AgentixAudit.
No prompt storage by default
Customer-controlled retention
Auditable access
Private deployment option
Stored by default
team
project
model tier
tokens + cost
task category
model fit
recommendation
confidence
FAQ
Common buyer questions
How does AgentixAudit integrate with our AI gateway?
It receives a copy of gateway usage events through HTTP ingestion, webhook, Kafka, batch files, S3, database export, or telemetry. Your gateway remains the production routing layer.
What data do we need?
At minimum: user or app, team, model, provider, timestamp, tokens, and cost. For deeper audit: session ID, task category, complexity, model fit, recommendation, risk flags, and confidence.
What does the audit engine look for?
Low-complexity tasks on frontier models, repeated similar sessions, missing batch usage, template candidates, unknown project ownership, high-cost unattributed usage, anomalous spikes, and month-end budget burn.
Do you store prompts and responses?
Not by default. The system is designed around metadata, cost, attribution, and structured audit summaries. Full content can stay in the customer environment.
AgentixAudit