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

A side-by-side comparison of Agnost AI and Assistly — 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 live meeting assistant for Mac and Windows that reads call audio locally and shows guidance in an overlay excluded from screen shares.

Bot-Free System Audio Capture: Works from your computer's audio rather than joining the meeting, so nothing appears in the participant list and there is nothing to integrate with the call app.
Screen-Capture-Excluded Overlay: The assistant window is excluded from screen capture at the OS level, so it stays visible to you and invisible in shares and recordings.
Auto-Assist Without Prompting: Detects when a question lands or when you think out loud and streams structured talking points into your thread automatically, with no hotkey and no break in eye contact.
Multi-Speaker Language Tracking: Separates your voice from other participants and follows who said what across dozens of auto-detected languages, even when the call switches language mid-sentence.
Two-Way MCP Context: Pulls context from Google Calendar, Notion, Linear or any MCP server during the call, and exposes your meeting history back over MCP so Claude, ChatGPT or Cursor can query it later.
Personas from Your Material: Builds a persona from your CV, docs and notes and switches modes for a sales call, client review or interview so responses match your background and phrasing.
Automatic Recap and Action Items: Turns the transcript into a summary with owners and deadlines the moment the call ends, auto-saved and searchable across sessions.
Per-Client Projects: Files each session to a project based on the calendar, and scopes answers and mid-call lookups to that client's history so context never crosses between accounts.

Live Sales Calls: Surfacing objection handling and product detail the instant a prospect asks, without breaking eye contact to search a doc.
Client Account Reviews: Recalling what was committed to a specific client in a previous session, with the source call cited, while the review is still running.
Non-Native Language Meetings: Following a call that switches language mid-sentence and receiving guidance in clear English.
Customer Success Handoffs: Leaving every call with a written summary and assigned action items instead of reconstructing notes afterwards.
Meetings Where Bots Are Unwelcome: Getting live assistance on calls with clients or legal teams who object to a recording bot joining the room.
Querying Past Meetings from Your Editor: Asking Claude, ChatGPT or Cursor what was agreed in a past session over MCP without opening the app.