agent-roi 0.1.2

Production control plane for agentic AI: guardrails, confidence decisioning, immutable audit logging

Agent-ROI

Agent-ROI is a lightweight Python library for adding guardrails, decision governance, auditability, and ROI reporting to agentic AI workflows.

It is designed for real production environments — not just demos.

---

Why Agent-ROI exists

AI agents are easy to demonstrate.
They are much harder to operate responsibly at scale.

Once an agent works, the real questions begin:

• When should it act automatically vs pause for human review?
• How do we enforce cost and execution limits?
• How do we audit what happened — and why?
• How do we explain the value in plain dollars?

Agent-ROI focuses on these problems.

It does not replace an agent framework.
It wraps existing agent logic with controls, decisioning, and reporting.

---

What Agent-ROI provides

Execution guardrails

• Step limits
• Tool call limits
• Cost ceilings
• Optional deterministic execution

Confidence-based decision routing

• Automatic approval for high-confidence outcomes
• Human-in-the-loop escalation for uncertain or higher-risk actions
• Explicit, explainable decision outcomes

Immutable audit logging

• Append-only audit events
• Correlation IDs per run
• Hash-chained records for traceability

ROI-focused reporting

• Executive-readable ROI summaries
• Clear linkage between actions, savings, and risk
• Designed for leadership and governance review

---

Included example: FinOps cost optimization

Agent-ROI ships with a simple FinOps example that:

• Scans a set of cloud resources
• Identifies cost-saving opportunities
• Categorizes recommendations by risk level
• Enforces cost limits and human approval for higher-risk actions
• Produces an executive-ready ROI report

The example is intentionally practical and deterministic.

---

Getting started

Install

pip install agent-roi