Agnost AI Review (2026): Pricing, Features & Honest Verdict

Agnost AI is product analytics for conversational AI agents. It reads your production chat and voice transcripts, clusters the recurring failures, and turns them into evals you can actually run. Good at catching the problems your offline test suite never sees. Best for teams running chat or voice agents in production. Price: from $49/mo, free plan available. Rating: 7.5/10.

What is Agnost AI?

Agnost AI is a monitoring and analytics layer for teams shipping chat and voice agents. It sits on top of your production traffic and reads the conversations themselves, not the click paths around them. The pitch on the homepage is blunt: “See what broke before users leave.” It groups thousands of transcripts into a ranked list of recurring problems, points at the exact users behind each one, and hands you a fix.

The company is Agnost Tech Inc, a Y Combinator company that launched publicly in 2026. It was built by two childhood friends, Shubham and Parth Ajmera. Shubham was an analytics engineer at Cisco and the first hire at Formbricks. Parth studied CS at IIT Madras, built Spark pipelines at Microsoft, and led GPU technology at Infurnia. The company says it is processing over a million events a day, with teams at Google, Exa and Corgi already on it.

We found it on Product Hunt at rank four, and the reason it caught our eye is the specific gap it names. Every LLM team has evals. Evals test the cases you already thought of. The failures that actually cost you customers are the ones nobody wrote a test for: the agent that promised a refund it cannot issue, the user who typed “you said it was done, but nothing happened” and then never came back. Those live in the transcript, in plain English, and almost nothing reads them.

What are Agnost AI’s key features?

Conversation clustering ranked by impact

The core surface takes every production conversation and auto-clusters it into recurring patterns. Not a wall of traces. A ranked list of problems, ordered by how many users hit each one. Under each cluster you get the actual conversations, so you can read the ten transcripts behind “users are asking for CSV export and we tell them no” rather than trusting a summary. This is the difference between an observability tool and an analytics tool, and Agnost is firmly the second thing. It answers “what is going wrong most” instead of “show me trace 4,812.”

Violation detection for hallucinations and broken promises

Agnost flags four categories of bad agent behaviour: hallucinations, broken promises, policy violations, and user frustration. The broken-promises category is the one worth paying for. An agent that says “I’ve updated your subscription” when the tool call silently failed will pass every latency check, every error-rate dashboard and most evals, because from the system’s point of view nothing went wrong. It only looks wrong if you read what the agent said and compare it to what actually happened. Frustration and sentiment detection sit alongside it, which is how the churn-signal claim works.

Three ways in, including plain OpenTelemetry

You can connect through the Agnost skill, the Agnost SDK, or by pointing an existing OpenTelemetry exporter at their endpoint. That last one matters more than the marketing gives it credit for. If you already instrument with OTel, and most serious teams do by now, you are not rewriting your tracing layer to try this. The company advertises a three-line install. The OTel route means the real number for an already-instrumented stack is closer to one config change. It is framework agnostic and model agnostic, which is where the name comes from.

Findings that turn back into evals and PRs

The loop closes at the far end. A cluster Agnost surfaces can be converted into an eval case, and the platform will generate a GitHub PR with a proposed fix tested against your historical conversations. That last bit is the clever part: the regression suite is your own past traffic, so a prompt change gets scored against thousands of real conversations rather than a handful of hand-written cases. How well this works in practice depends entirely on your codebase, and we would treat generated PRs as a starting draft rather than something to merge on trust. But the direction is right. Finding the bug is only half of it.

How much does Agnost AI cost?

Agnost AI starts at $49/mo on the Starter plan, and there is a real free tier at $0 with 1,000 events a month and 7 days of data retention. Pro is $499/mo with a million events and 90-day retention. Enterprise is custom.

Read the unit before you read the price, because it changes the maths. An event here is not a conversation. Agnost counts “agent turns, model generations, handoffs, guardrails, and other operations,” so a single multi-turn support chat that calls three tools can burn a dozen events or more. That makes the Starter tier’s 10,000 events a month much smaller than it looks. A modestly busy agent will chew through it in a week.

The bigger problem is the shape of the ladder. Starter gives you 10,000 events for $49. Pro gives you 1,000,000 for $499. That is 100 times the volume for 10 times the price, with nothing in between. If your agent does 60,000 events a month you are stuck choosing between a plan you have outgrown and a plan that costs ten times more than you need. We could not find a published overage rate on the plan table either, so what happens when you cross the Starter line is not something you can work out in advance.

Against the field, Starter looks expensive and Pro looks fair. Langfuse gives you 100,000 units for $29 on its Core plan and 50,000 free on Hobby. LangSmith’s free Developer tier covers 5,000 base traces before pay-as-you-go kicks in, and Plus is $39 per seat. Braintrust’s free Starter includes 10,000 scores and 1 GB of processed data before its $249 Pro tier. Agnost is not competing on cost per unit at the low end. It is competing on the analysis it runs over those units, which none of the tracing tools do in the same way.

Who is Agnost AI best for?

Use Agnost AI if you have a chat or voice agent in front of real users, at real volume, and you already know it fails in ways your dashboards do not show. Support agents, sales agents and onboarding agents are the sweet spot, because their failures are conversational rather than technical and they cost you a customer rather than a 500. Teams already running OpenTelemetry get the cheapest possible trial: point the exporter, wait a week, look at the clusters.

It also suits anyone whose eval suite has stopped finding things. If your CI passes on every run and your churn is still climbing, the gap between the two is exactly what this reads.

Skip Agnost AI if your agent is low volume or internal. Under a few thousand events a month the free tier will hold you fine, but you also will not have enough conversations for clustering to say anything a human read-through would not. Skip it if what you need is span-level debugging and latency waterfalls, because Langfuse and LangSmith do that better and cheaper. And skip it for now if you sit in that 50,000 to 500,000 event band, at least until there is a tier that fits you. Paying $499 for volume you will not use is a bad trade when the free plan will tell you whether the analysis is worth anything.

Best Agnost AI alternatives

Langfuse. Open source, self-hostable, and much cheaper per unit: Hobby is free with 50,000 units a month, Core is $29/mo with 100,000 units, Pro is $199/mo. It is a tracing and eval platform first, so you get the raw observability and the prompt management, but the conversation-level clustering and violation detection are not there. Pick Langfuse if you want to own the data and do your own analysis.

LangSmith. The default if you are already deep in the LangChain ecosystem. Developer is free with up to 5,000 base traces a month, Plus is $39 per seat per month with 10,000, then pay-as-you-go at $1.50 per compute unit. Strong on trace inspection, datasets and human review queues. Weaker if your stack is not LangChain, and the per-seat model gets expensive on a big team.

Braintrust. The most eval-centric of the three. Free Starter with 10,000 scores, 1 GB of processed data and $10 of model credits, then $249/mo for Pro with 50,000 scores and 5 GB. Built around scoring functions and experiment comparison, so it is the right pick if your team wants rigorous offline evaluation. It has the same blind spot Agnost is built to fill: it grades what you tell it to grade.

Final verdict: is Agnost AI worth it?

Agnost AI is solving a real problem that the observability tools have mostly ignored. Reading transcripts at scale and ranking what is broken by how many people it hurt is the correct job to be doing, and doing it on top of OpenTelemetry rather than demanding a proprietary SDK is the correct way to ship it. The founders have the background for it and a million events a day is not a toy. For a team with a live agent and a churn problem they cannot explain, this is worth an afternoon.

The pricing is what holds the score down. The Starter tier is too small for the unit it meters, the leap to Pro is a hundredfold in volume for tenfold in money, and there is no published overage rate to plan around. That is fixable and will probably be fixed, but today it means a growing team hits an awkward wall fast. Start on the free plan, connect your OTel exporter, and give it a week of real traffic. If the clusters it returns tell you something you did not already know, the $49 is easy. If your volume then puts you between the tiers, ask them for a number before you commit.