Agnost AI vs Rivault: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Agnost AI and Rivault — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Agnost AI

Agnost Tech Inc

Freemium

Product analytics for conversational agents that surfaces silent failures, user frustration and policy violations across every conversation.

Silent Failure Detection: Reads each trace next to the conversation to catch cases where the run reported success but the user got nothing useful, including broken promises and confidently wrong answers.
Automatic Conversation Clustering: Turns thousands of chats into ranked recurring problems, ordered by user impact and ready to investigate rather than left as raw logs.
Frustration and Churn Signals: Pinpoints where users rage-prompt, get stuck or abandon the conversation, so churn drivers are visible before the user leaves.
Policy and Quality Violation Alerts: Flags hallucinations and quality, policy and compliance breaches with the exact conversation and trace behind each one.
Evidence-Backed Fix Recommendations: Hands over the highest-impact fixes with supporting evidence, a recommended change and the evals needed to ship it safely.
Two-Step Skill Install: Connects to an existing agent by installing an agent skill and running one prompt, with no rebuild of the agent and no separate implementation project.
Feature Request Mining: Surfaces what users repeatedly ask for across conversations, turning support volume into a prioritised roadmap signal.
Live Demo Without Signup: Ships a public interactive demo where you can click any insight and inspect the underlying conversations before creating an account.

Diagnosing Agent Churn: Finding the recurring conversation pattern that makes users abandon a support agent, with the specific chats as evidence.
Auditing Production Agents for Compliance: Reviewing conversations for policy violations and unsupported claims across real traffic rather than a hand-picked sample.
Prioritising Agent Improvements: Deciding which prompt or flow to fix next based on how many users hit each failure cluster instead of on anecdote.
Catching Regressions After a Prompt Change: Watching whether a newly shipped change increases silent failures or user frustration in live conversations.
Building Evals from Real Failures: Turning observed production failures into regression evals so the same bug does not ship twice.
Mining Conversations for Roadmap Input: Extracting repeated feature requests from support and sales chats to feed product planning.

Freemium

A zero-knowledge vault that lets AI agents request sensitive data on demand, unlocked by Face ID or passkey and redacted after each task.

Zero-knowledge Vault: Sensitive data is encrypted client-side so Rivault never has access to the underlying values by default.
Per-request Auth: Every agent access triggers a fresh authorization request that you approve with Face ID or a passkey.
Deterministic Redaction: Data is scoped to a single task and redacted from the agent's context after completion.
Agent-agnostic Integration: Works with chat-based AI agents and Computer Use Agents that automate browser or desktop flows.
Ownership of PII: Store SSN, payment info, phone numbers, and emails once and reuse without leaving copies in session logs or memory.
Biometric Unlock: Face ID or passkey approval keeps the human in the loop for every sensitive field release.

Agent-driven Bookings: Approve passport and card details on-demand when an agent books flights or hotels.
Automated Form Fill: Let CUAs complete government or insurance forms without leaving PII in browser logs.
Customer Support Delegation: Grant scoped access to account numbers when an agent handles a service call for you.
Recurring Task Automation: Reuse stored credentials across many agent workflows without pasting them each time.
Enterprise Agent Deployments: Add a consent + audit layer for teams deploying agents that touch sensitive employee or customer data.