AI produces more output than a human-in-the-loop model can keep up with. So how do you provide checks and balances in your AI environment? That’s where Vindex comes in.
Animation in three stages. Before AI, people wrote work at a pace one human reviewer could check, one item at a time. Now, AI agents produce far more output than a human reviewer can keep up with, so a backlog forms, most output goes unchecked, and some drifts off course. With Vindex, each output your agent submits is checked against your rules: work that passes moves on, and work that fails goes back to the agent to fix.
Separation of powers for automated work
Treat your agentic systems like a democracy. No one model or agent holds absolute power. Your company writes the rules, your agents do the work, and Vindex rules on each output independently, so no agent ever grades its own work.
Legislative
Your company drafts, versions, and owns every rule. An agent can draft a rule. Only a person makes it law.
Rules catalog
Executive
Emails, replies, proposals, documents, and stories, written against the rules in force.
Any agent, via MCP
Judicial
An independent model checks each output against the exact rule version. It didn’t write the work and has no stake in it.
VindexAI
The record
Every ruling is tied to the rule version in force. Track shows pass rates and drift over time, and drift informs new rules.
Each branch checks the others. The agent never reviews its own output.
Poor requirements and agentic drift. Vindex automates the check for both.
Issue 1
Work your team writesBuilding the wrong thing leads to rework, which leads to wasted money, delayed timelines, and a compounding backlog. The SDLC bottleneck is no longer gathering requirements or development. It's requirements quality validation.
Your automated solutionIntegrated Vindex scores every user story as it's written, inside Jira, Azure DevOps, Asana, or your AI client, before anyone builds on it.
Issue 2
Work your agents writeBefore AI, it was hard enough to keep a small team aligned with company policies, internal and external formats, and business strategy, let alone an entire company. AI has scaled that challenge exponentially.
Your automated solutionAgent Reflection checks each output your agent submits against your rules catalog with VindexAI and names any rule that failed, so the agent can fix it.
The question is who writes the work. People write stories. Agents write everything else.
| Question | Integrated Vindex | Agent Reflection |
|---|---|---|
| Who writes the work | People: product owners, analysts, coaches | AI agents |
| What gets checked | A user story | Any agent output: an email, a reply, a document, a story |
| Where it runs | Jira, Azure DevOps, Asana, your AI client, CSV, and the API | Your AI client or agent, over the Vindex MCP server |
| Who sets the standard | Vindex, with its story-quality method | You, with your own rules and public rules |
| What comes back | Story health as a whole percentage, with plain-language findings | Pass or fail for each rule, naming any rule that did not pass |
| How you pay | Per story, once | Per critique |
| Free to start | 5 stories with no signup, 100 with an account | 1000 free critique credits with an account |
A story gets a health percentage and a plain finding. An agent output gets a pass or fail for each rule, and any rule that did not pass is named.
You
Score NW-108 with Vindex: “Warehouse scanning should be faster and better. The scanning process in the warehouse is slow and the team complains about it. Make scanning faster and improve the experience for the warehouse staff.”
Assistant
NW-108: 38% (At risk)
Add measurable acceptance criteria with baseline and target metrics (e.g., p95 scan-to-confirm time, scans/hour, error rate) and specify devices/network conditions for measurement.
Sample critique. Illustrative data written by Vindex. Not a customer result.
A company signs in once and finds Agent Rules beside its Integrated Vindex setup. The story-quality rule behind Integrated Vindex is also a public rule in the Agent Reflection catalog, and both products run on the same engine. What your team uses in Jira, your agents can use through the Vindex MCP server.
One sign-in covers both products. Both grants are one-time.
100
free user stories
Integrated Vindex
1000
free critique credits
Agent Reflection
CSV files uploaded to the public evaluate page are deleted after 24 hours.
The Vindex MCP server signs in with OAuth 2.1, as a person in your company.
VindexAI reviews each Agent Reflection critique. No third-party model is involved.
BAA available. SOC 2 and compliance roadmap on request.
