Agnost AI is a Y Combinator-backed analytics platform that reads production conversations from chat and voice AI agents, then clusters them into recurring intents, frustration signals, and failure patterns your offline evals never see. It launched on Product Hunt today, August 25, 2026, and connects to an existing agent through a coding-assistant skill, an SDK, or OpenTelemetry.
Short answer: Agnost AI is a production analytics platform for AI agents that clusters conversations into intents, silent failures, hallucinations, and churn signals. Setup runs through an npm skill, SDK, or OpenTelemetry in about three lines of code. The free plan covers 1,000 events per month; paid plans start at $49/month for 10,000 events.
Last updated August 25, 2026.
When I worked through Agnost AI's docs, its quickstart skill, and its published pricing page today, I wanted to know what you're actually connecting to your agent before you point production traffic at it. This is a same-day launch, so there's no independent usage history yet — here's what the setup path looks like, what each tier actually costs, and what I'd want to see hold up before trusting it with real conversation data.
What you'll need
You need an AI agent already running in production, or at least in a staging environment you can trigger real interactions in — Agnost AI reads existing conversations, tool calls, and traces, it doesn't build the agent for you. Connection happens through one of three paths: a native skill you install with npx, a direct SDK, or OpenTelemetry if your stack already exports traces that way. You'll need your organization ID from the Agnost AI dashboard before you run the setup prompt, and a coding agent (like Claude Code) capable of running the skill installer, since the quickstart is written as a prompt you hand to your assistant rather than a manual file edit. No credit card is required to start on the free tier.
Step-by-step: setting up Agnost AI
1. Create an account and grab your org ID
Sign up at agnost.ai and go to Settings > Organization in the dashboard at app.agnost.ai to copy your organization UUID. You'll paste this into the setup prompt in the next step, and it's what ties incoming events back to your account.
2. Install the skill
Run npx skills add AgnostAI/skills --skill agnost-ai from your project, or hand your coding agent this prompt directly: "Use the agnost-ai skill to add Agnost AI analytics. Org ID: your-org-id. Target app: [your app]." In my read of the docs, this is meant to instrument your agent's model calls, tool calls, and handoffs without you touching the integration code by hand.
3. Trigger a real interaction
Restart your app and run one genuine chat turn, agent action, or MCP tool call — not a canned test string. The docs note events typically show up within seconds; if nothing appears, the usual culprits are a stale org ID, an app that didn't actually restart, or a code path that never got exercised.
4. Inspect the staging trace
Open the Conversations, Tool calls, and Events sections in the dashboard and confirm what shows up matches what you expect from that one interaction. This is the step worth not skipping — it's much easier to catch a misconfigured integration on one trace than after a week of production data has already been mis-tagged.
5. Enable on production conversations
Once staging looks right, point the same integration at your live traffic. Agnost AI's own claim is that it already processes more than one million messages a day across customers, which at least suggests the ingestion path is built for real volume rather than a demo.
6. Review clustered intents and violations
Agnost groups conversations into recurring patterns — feature requests, frustration signals, hallucinations, broken promises, policy violations — ranked by how often they show up. This is where the platform's actual pitch lives: turning thousands of individual chats into a short list of things worth fixing.
7. Pick a plan once you know your event volume
Watch consumption on the free tier before committing to Starter or Pro. The jump from Free's 1,000 events to Starter's 10,000 is manageable, but Starter to Pro's 1,000,000 events is a much bigger leap in both scope and price.
Example prompts you can copy
A few starting points close to what the setup flow and dashboard actually expect:
- "Use the agnost-ai skill to add Agnost AI analytics. Org ID: [your-org-id]. Target app: [your app name]."
- "Show me the highest-impact failure cluster from the last 7 days, with two example conversation traces for each."
- "Which recurring user intents in the last 30 days aren't currently handled by any existing tool or flow?"
- "Flag any conversation where the agent made a claim it didn't retrieve evidence for, and group those by root cause."
Specific asks beat generic ones here — "show me problems" gives the dashboard nothing to rank against, while naming a time window and an output shape gets you something you can act on the same day.
Common mistakes to avoid
The mistake I'd flag first: instrumenting only the happy path in staging and calling it done, so the first real signal you get on hallucinations or broken promises arrives days later in production instead of during your one verification trace. Second, treating the "1,000,000 messages analyzed daily" figure on Agnost's site as evidence the clustering is accurate for your specific agent — that's a volume claim, not a quality claim, and it's the company's own number on a product that launched today. Third, underestimating how fast a multi-step agent burns through the Free plan's 1,000-event cap; a single conversation with several tool calls can use up more events than a simple chatbot turn, so the free tier goes faster than the number suggests. Fourth, skipping the org-ID and target-app fields in the setup prompt and letting the skill guess, which is a common source of events landing in the wrong workspace or never showing up at all. Fifth, reading intent clusters as a finished backlog instead of a starting point — the platform ranks patterns by frequency, not by how hard or valuable each one is to actually fix.
Agnost AI vs. Progress AI Observability at a glance
Both are launch-week entrants in AI agent monitoring, but they solve different halves of the problem — Agnost clusters conversation-level intents and failures after the fact, Progress traces individual agent runs step by step as they happen.
| Agnost AI | Progress AI Observability | |
|---|---|---|
| Focus | Conversation clustering, intent and failure discovery | Step-by-step tracing of model, tool, and retrieval calls |
| Free tier | 1,000 events/month, 7-day retention, no card | 10,000 units/month, 7-day retention, no card |
| Entry paid plan | $49/month (10,000 events, 30-day retention) | $29/month (200,000 units, 30-day retention) |
| Top tier shown | $499/month Pro (1,000,000 events, 90-day retention) | Enterprise, custom |
| Connection methods | Skill install, SDK, OpenTelemetry | SDK per language (Python, JS, .NET) |
| Best for | Finding recurring problems across many conversations | Debugging why one specific agent run failed |
I confirmed Agnost's tiers on its docs site and cross-checked the launch details on Product Hunt, both on August 25, 2026. Progress's numbers come from my own testing in Progress AI Observability: Setup, Pricing, and Verdict. Check both vendors' live pricing pages before budgeting — plans in this category are new and change often.
Tools that make this easier
If the agent you're monitoring runs through Claude Code or another coding assistant, AI coding assistant: a beginner’s guide and best AI tool for code cover the tools most likely to be producing the traces Agnost reads. For teams whose agents route through Model Context Protocol servers, ElevenLabs MCP in Claude walks through a real MCP integration you'd want visibility into. If you're building or shipping a customer-facing chat agent, How to Use ChatGPT Agent Mode is worth a look before you decide what "success" even means for the conversations you're about to start clustering. And on the trust side of this problem, AI agents lie, cheat, and steal — that’s putting off users covers why silent failure detection like Agnost's matters beyond uptime metrics.
My take
Agnost's actual mechanism — read production conversations, cluster them into intents and violations, rank by frequency — is a reasonable answer to a gap that trace-level tools like Progress AI Observability don't fully cover: knowing an agent's tool calls all returned 200 OK doesn't tell you the user left frustrated. What I can't verify on launch day is how well the clustering holds up on a noisy, high-volume agent versus a tidy demo, or whether "silent failure" detection produces false positives once real users start typing things a demo script never would. The free tier's 1,000-event cap is thin for anything beyond a quick evaluation, and Starter's jump to $49/month for 10,000 events is worth testing against your actual traffic before committing. If you're already tracing individual runs with something like Progress or LangWatch, I'd treat Agnost as a complementary layer — the two are answering different questions — rather than a replacement.
Frequently Asked Questions
Is Agnost AI free?
For light use, yes. The Free plan covers 1,000 events per month with 7-day retention and no credit card required. Anything beyond a quick evaluation on a real agent will likely outgrow it, at which point Starter ($49/month, 10,000 events, 30-day retention) is the next step.
How long does it take to set up Agnost AI?
Installing the skill and connecting one agent takes a few minutes if you already have your organization ID handy — the setup is one npx command plus a prompt to your coding agent. Getting clustered intents and failure patterns you actually trust takes longer, since the dashboard needs real conversation volume, not one test trace, before the groupings mean anything.
What is the easiest way to get started with Agnost AI?
Grab your org ID from Settings > Organization first, then run the skill install prompt with that ID and your target app named explicitly. Verify one staging trace in the dashboard before pointing it at production — skipping that check is the fastest way to end up debugging a misconfigured integration instead of your agent.
Does Agnost AI replace step-by-step tracing tools?
No. Agnost clusters conversations after the fact to surface recurring intents and failures; it doesn't give you the same call-by-call trace explorer that a tool like Progress AI Observability does. Teams that need both would run them side by side rather than pick one.
How does Agnost AI compare to Progress AI Observability?
Agnost focuses on conversation-level pattern discovery — grouping many chats into ranked failure and intent clusters. Progress focuses on tracing a single agent run's model, tool, and retrieval steps as they happen, with built-in LLM-as-a-judge scoring. They overlap in goal (catching what evals miss) but not in method.