Agnost AI vs Taste Lab: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agnost AI and Taste Lab — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agnost AI
Agnost Tech Inc
Product analytics for conversational agents that surfaces silent failures, user frustration and policy violations across every conversation.
Key features
- 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.
Best for
- 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.
Taste Lab
Sen Lin
Taste Lab is a Claude Code skill that turns any URL into a complete design context: design tokens plus the reasoning and trade-offs behind every choice.
Key features
- Design Map Extraction: Captures every color, font weight, spacing value, radius, and shadow with exact px/hex/ratio citations across 20 measurement categories.
- Taste DNA Inference: Derives four design principles, each with a Trigger, Decision, Reason, Evidence, and Trade-off explaining why each choice was made.
- Four-Agent Pipeline: Runs Extract, Detect Patterns, Infer Taste, and Observer stages, each reading the page through a sharper lens.
- Anti-Slop Quality Gate: A final critic stage runs anti-slop checks and validates JSON before writing output.
- Dual File Output: Writes a {domain}.md and {domain}.json that any AI agent can build from.
Best for
- Cloning Design Systems: Give an AI agent a complete, reasoned design context to rebuild a site's look and feel.
- Design Reviews: Understand the deliberate trade-offs behind a website's visual decisions.
- Agent-Assisted Frontend Work: Feed structured taste files into coding agents so they make the right call on unseen pages.
- Design Token Auditing: Extract and document a site's full token set with cited measurements.