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

A side-by-side comparison of Agnost AI and NoMac — 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.

Paid

Cloud-based iOS build, TestFlight, and App Store submission pipeline that AI agents can drive through a CLI or MCP — no Mac required.

Cloud-Mac Signed Builds: Produces signed iOS release builds on remote Macs so developers never need Xcode or local certificates.
TestFlight Preview in Minutes: Uploads and distributes builds to TestFlight, delivering the app to your iPhone in about three minutes.
Automated App Store Submission: Handles metadata, screenshots, review-readiness checks, and the actual App Store submission call.
MCP Server for Agents: Ships a Model Context Protocol server that any MCP-speaking agent (Claude Code, Codex, Cursor) can drive end-to-end.
CLI and HTTP API: Offers npx @nomac/cli plus a public API for agents or scripts that prefer not to use MCP.
Certificate and Signing Autopilot: Manages provisioning profiles, signing identities, and API-key rotation without exposing your Apple ID password.
Crash and Feedback Loopback: Crashes and tester feedback flow back to the calling agent so it can iterate without a human copy-paste step.
Setup Wizard: Walks through the one-time Apple gates (Developer account, App Store Connect API key, App Privacy form) and verifies each step live.

Solo Indie Development: Ship iOS apps from a Windows or Linux machine without buying a Mac.
Agent-Driven Releases: Let a Claude Code or Codex agent handle the whole build-test-submit loop while the developer supervises.
Continuous Delivery: Push every merge to TestFlight automatically for internal QA and beta feedback.
AI-Generated Apps: Use it as the deployment leg for AI-built mobile apps that need to land on the App Store.
Cross-Platform Teams: Give backend or web engineers a hands-free path to publish an iOS build without Xcode expertise.
Rapid Prototyping: Iterate on iOS prototypes and ship them to real devices in minutes rather than hours.