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Hybrid AI Architecture

FinOps Guard — How it actually works

4

Scenarios

Operate · Optimise · Govern · Inform

16

Hybrid AI Agents

4 per scenario · constrained LLM

0

Auto-Executions

HITL gate on every scenario

Human Goal — any of 4 scenarios

Remediate cost spike
Broker RI / Savings Plans
Enforce cost-attribution tags
Govern quarterly budget
LLM Orchestration Layerconstrained prompts · structured output
Breaks goal into 4 sub-tasks
Enforces output format per agent
No free-form hallucination
Dispatches agents sequentially

16 Hybrid AI Agents — 4 per scenario

A1 → A2 → A3 → A4
Anomalous Spend Remediation
A1

Detect

Anomaly Scanner

A2

Investigate

Root-Cause

A3

Simulate

Impact Model

A4

Remediate

API Proposal

RI & Savings Plan Broker
A1

Analyse

Usage Scanner

A2

Compare

Cost Simulator

A3

Optimise

Right-Size ID

A4

Draft

RI Recommender

Cost-Attribution & Tagging Enforcer
A1

Audit

IaC Scanner

A2

Infer

Owner Attribution

A3

PR

Terraform Injector

A4

Notify

Slack DM Composer

Budget Forecast Governor
A1

Track

Burn-Rate Monitor

A2

Forecast

Scenario Engine

A3

Triage

Workload Finder

A4

Draft

Finance Memo

Deterministic Manifest Buildercode formulas · no LLM · SHA-256 signed
Savings maths — deterministic formulas
Proposed API calls — templated ARNs
Execution ID — SHA-256 hash
Immutable audit trail per run
Strict Human-in-the-Loop Gateall 4 scenarios · no bypass
Execution blocked — zero auto-actions
Physical countersignature required
Human Approved
LLM Communication Layer
Slack alert → FinOps / Engineering lead
GitHub PR → Terraform tag fixes
RI purchase recommendation memo
Budget justification → VP Finance

Bounded Agentic Autonomy

These 16 agents are not free-roaming chatbots — they operate within mathematical and logical guardrails. No open-ended reasoning loops. No hallucinated numbers. No runaway API spend.

The LLM Doesn't Do the Math — It Calls the Calculator

When an agent reaches the Simulate phase, it hands off to a deterministic math engine (OR-Tools / SciPy). The LLM feeds the inputs, waits for the mathematically exact answer, then reads the result. Savings figures are never guessed — they are calculated.

OR-Tools · SciPy · deterministic output

The LLM Doesn't Do the Routing — It Follows a State Machine

Agents don't loop endlessly and burn API tokens. The pipeline is a Deterministic State Graph (LangGraph / custom state machine): Step 1 must complete before Step 2 fires, Step 3 validates before Step 4 reaches the Human Gate. Every transition is code — not LLM intent.

LangGraph · state machine · no open loops

Complete Security & Privacy Boundary

Database lookups, git history parsing, and IAM log queries run on deterministic local code — sensitive infrastructure credentials and proprietary repo contents never leave your environment or reach a third-party LLM provider. The LLM only sees what you explicitly pass it.

local execution · zero credential leak · air-gappable

Deploy this architecture in your cloud
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FinOps Guard · Hybrid AI · Agentic Cloud Cost Lifecycle

Your Hybrid AI FinOps team — that can't act without you

Four autonomous Hybrid AI agents — combining deterministic rules with contextual reasoning, never guessing — continuously monitor your cloud spend, trace root causes, model impact, and prepare remediation. Then they stop dead until a human signs the manifest. No cloud API call, no GitHub PR, no Slack message fires without your signature.

4

Scenarios

16

Hybrid AI Agents

0

Auto-Executions

Scenario Inputs — edit to match your environment

Anomalous Spend Remediation
$/day
$/day

Agentic Workflow — Anomalous Spend Remediation

01

Detect

Scan billing telemetry, flag exact resource & anomaly score

02

Investigate

Cross-reference GitHub commits that triggered the cost spike

03

Simulate

Calculate daily burn rate, monthly impact & RI savings gap

04

Remediate

Propose cloud API calls to terminate or downgrade rogue resources

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