Agnost AI
Agent analytics that catches silent AI agent failures in live production conversations, then turns them into reviewed prompt fixes.
Agnost targets the right failure mode, and it targets it more completely than most agent observability pitches: the loop ends with an approved prompt change plus a regression eval generated from the real traces that broke. The framework coverage is unusually broad — LangGraph state and interrupts, CrewAI delegation loops, Pydantic AI schema-valid-but-wrong output, DSPy optimizer traffic separated from user traffic. Where it bites is scope and cost. The Free tier's 1,000 events/month over a rolling 30-day window is a taste, not a pilot, and Pro steps to $499/mo for 1,000,000 events. Run it against one high-traffic agent before you commit.
Verified 2d ago · liveness 69/100 · cite: rightaichoice.com/tools/agnost-ai
- AI engineers and ML teams running agents on real production traffic
- Product owners tracking intent, trust, friction and handoff metrics
- Sales and support agent teams debugging silent qualification and policy failures
- Voice agent operators whose silence and interruptions hide user confusion
- Teams still prototyping agents with no meaningful production traffic
- Buyers whose primary need is latency, cost and error-rate APM monitoring
- Anyone wanting a deterministic unit or regression testing framework for agents
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Skip Agnost if you would rather the agent rewrite its own prompts without a human approving each change, or if your agents have no production traffic yet to analyze.
Events, not conversations, are what you buy — a single conversation with several turns and tool calls burns several events, so a 1,000-event free allowance can vanish in a day of real traffic
Free ($0/mo, 1,000 events) is genuinely early production, not a pilot for anything busy. Starter at $49/mo (10,000 events, 30-day retention) fits a single low-volume agent with someone who needs onboarding help. Pro at $499/mo (1,000,000 events, 90-day retention, founders Slack) is the real working tier and sits above what most trace-only observability tools charge, while staying below full enterprise APM contracts. Enterprise is custom-priced once you need VPC deployment or audit logs.
In short
Agnost AI — Agent analytics that catches silent AI agent failures in live production conversations, then turns them into reviewed prompt fixes. Best for AI engineers and ML teams running agents on real production traffic, Product owners tracking intent, trust, friction and handoff metrics, Sales and support agent teams debugging silent qualification and policy failures. Free to start; paid plans from $49/mo.
What's new in Agnost AI
Checked 4 days agoAcross the latest 10 updates: 9 feature updates and 1 news mention.
Vercel AI SDK and VoltAgent observability via OpenTelemetry
Agnost released OpenTelemetry-based instrumentation for Vercel AI SDK streaming/tool calls and VoltAgent conversation and tool execution.
Pydantic AI Observability: validation, retries, outcomes
Agnost added Pydantic AI instrumentation that distinguishes schema-valid output from correct results.
OpenAI Agents SDK Observability: handoffs, tools, outcomes
Agnost now tracks OpenAI Agents SDK handoffs, guardrails, and streaming completion, separating execution from user success.
Mastra Observability: trace agents and workflow outcomes
Agnost added Mastra trace export with user and conversation identity for diagnosing tool failures and bottlenecks.
LangGraph Observability: monitor state, interrupts, retries
Agnost released LangGraph monitoring that preserves thread identity and distinguishes paused runs from failures.
LangChain Observability: from agent traces to useful answers
Agnost shipped LangChain monitoring that diagnoses retrieval and tool failures while preserving conversation identity.
DSPy Observability: monitor optimized programs in production
Agnost added DSPy module-call tracking, separating optimizer traffic from user traffic and surfacing version regressions.
CrewAI Observability: monitor tasks and delegation
Agnost launched CrewAI instrumentation that separates crew completion from task success and flags delegation loops.
Agno Observability: monitor agents, teams, and sessions
Agnost added Agno instrumentation for session and team-run tracking, aimed at routing and memory diagnosis.
Agnost publishes agent observability guides for 11 frameworks
Agnost released a monitoring guide index covering CrewAI, LangGraph, LangChain, OpenAI Agents SDK, Agno, DSPy, Pydantic AI, Mastra, Vercel AI SDK, VoltAgent, and Spectrum-TS.
What people actually say about Agnost AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
56 mentions across 4 sources (Hacker News, YouTube, Product Hunt, Lemmy) · researched Aug 26, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Catches real behavioral failures like rageprompting and repeated rephrasing that evals miss
- +Observes production conversations, not just test assertions
- +Provides detailed traces, logs, and decision-path visualization
- +Pitch is directly validated by founders' own outreach to user teams
- +Lightweight integration with existing agent frameworks
- −Pricing is 'contact us' — only vague 'pennies per million messages' claim
- −No independent reviews or critical evaluations exist yet
- −Real user feedback outside launch comments is essentially absent
- −Doesn't directly answer whether it cuts model inference costs
- −Integration and platform specifics are unlisted — you have to guess
- • No listed free tier or trial tier
- • No published overage pricing if you exceed a message volume
- • Possibility of sales-led pricing that varies per team
Viability Score
How well maintained and how widely used is Agnost AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Detect silent agent failures from live production conversations
- Catch user corrections, rephrased questions and abandonment signals
- Group similar failures into clusters ranked by users affected
- Track which failure clusters are growing over time
- Drill into flagged messages behind each discovered intent
- Inspect nested tool calls and traces behind a specific message
- Generate regression evals automatically from affected traces
- Approve suggested prompt or workflow fixes to apply automatically
- Replay results shown before a fix is approved
- Automatic intent and sentiment discovery across conversations
- Quality, policy and compliance violation detection
- Slack alerts for silent failures and rising friction with copyable fixes
- Voice agent silence and interruption analysis
- Session-style replay for agent conversations
- OpenTelemetry-based instrumentation for Vercel AI SDK and VoltAgent
About Agnost AI
Agnost AI is production agent analytics for teams whose agents already talk to real users. Instead of scoring runs pass/fail in a test harness, it instruments live conversations from LangChain/LangGraph, the OpenAI Agents SDK, CrewAI, Vercel AI SDK, Pydantic AI, Mastra, Agno, DSPy, VoltAgent and Spectrum-TS, plus raw OpenAI and Anthropic calls, then flags the failures nobody files a ticket for: users correcting the agent, asking the same question twice, giving up mid-task, or getting a confidently wrong answer about plan limits. As of October 2026 Agnost also instruments through OpenTelemetry, which is how the Vercel AI SDK streaming and tool-call path and VoltAgent conversation/tool execution are captured. The workflow runs clusters → messages → traces → fix. Agnost groups similar failures and ranks them by how many users they affect, opens the flagged messages behind an intent so you can read corrections and frustration in context, then shows the nested tool calls behind a specific message so you can see what the agent and its tools actually did. From there it suggests a prompt or workflow change, generates a regression eval from the affected traces, and shows you replay results before you approve the fix to apply it automatically. Setup is short: download the skill with `npx skills add AgnostAI/skills --skill agnost-ai`, or run the vendor's prompt against your codebase. Python, TypeScript and OpenTelemetry are supported if you wire your own harness. Included on every plan are automatic intent and sentiment discovery, quality/policy/compliance violation detection, and alerts for silent failures and rising friction — delivered to Slack with a copyable fix. An event is one captured operation (an agent turn, model generation, or tool call); a conversation typically spans several. Usage is measured over a rolling 30-day window and automated analysis pauses at the cap. Agnost sits between agent observability tools that stop at traces and generic APM that stops at latency and cost: a monitoring and improvement layer for AI engineers, ML teams and product owners running autonomous agents on real traffic.
Behind the Verdict
The honest pitch for Agnost is that most agent observability tools answer 'did the run complete?' and Agnost answers 'did the user get what they came for?' That distinction shows up in the actual instrumentation. The CrewAI connector separates crew completion from task success. The Pydantic AI connector distinguishes schema-valid output from correct results. The LangGraph connector preserves thread identity and distinguishes paused runs from failures. Those are the three places where a pass/fail harness lies to you, and Agnost named them explicitly. Strengths. The signal is behavioral rather than evaluative — user corrections, repeated questions, abandonment, frustration — so you do not need to author graders before you get value. Clustering does the prioritization for you: grouping similar failures and ranking by users affected answers the question teams actually stall on, which is which of the forty things wrong to fix first. The loop closes. Alert to Slack with a copyable fix, open the intent, read flagged messages, follow nested tool calls, review the suggested prompt change, check replay results, approve, and the regression eval is generated from the traces that broke. Most tools in this category stop one or two steps short of that. Weaknesses and where to be careful. Event volume is the metered unit, and an agent turn, model generation and tool call each count separately, so a busy agent burns 1,000 events fast — the Free tier is for early production, not for evaluation at scale. Retention is tier-bound: 7 days on Free, 30 on Starter, 90 on Pro. A slow-moving failure that surfaces once a quarter is invisible on anything below Pro. And the product is a monitoring and improvement layer, not a replacement for your latency/cost/error-rate APM and not a deterministic unit-test framework — you still need those. Where it fits: one or two agents with real traffic, an owner who can approve prompt changes, and a team comfortable shipping fixes weekly. Where it does not: prototype-stage agents with no traffic to mine, enterprises that need SOC 2 Type II cleared today, and teams that want automated changes without a human in the loop.
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Real-world workflow fit
Concrete scenarios for the personas Agnost AI actually fits — and what changes day-one when you adopt it.
Install the agnost-ai skill with npx and connect the running LangGraph app, then let a week of real conversations accumulate. Open the clusters view and sort by users affected to see which intent is hurting the most people.
Outcome: A ranked list of recurring failures instead of a backlog of anecdotal bug reports, plus Slack alerts when a cluster starts growing.
An alert lands in Slack about incorrect plan-limit answers. Open the intent, read the flagged messages where users corrected the agent, then follow one message into its nested tool calls to see whether the retrieval or the prompt produced the wrong answer.
Outcome: A specific root cause with 126 traces and 98 customers attached as evidence, and a suggested prompt change ready to review.
install
Outcome: ok
Use Cases
- Detect memory failures that show up as users repeating the same question
- Find where users rage-prompt, get stuck or give up before churn
- Diagnose AI sales agents that qualify shallowly or fake confidence
- Analyze voice agent silence, interruption and hesitation as confusion signals
- Trace a bad response back through its nested tool calls
- Prioritize which recurring agent failure to fix first
- Turn production corrections into reviewed, applied prompt changes
- Measure the first minutes of agent onboarding for trust breakdowns
Limitations
- The framework connectors, tier structure and improvement loop are documented, so fit is assessable.
- The honest constraints sit elsewhere.
- SOC 2 Type II is still in progress — only Type I is complete — so regulated buyers may need to wait.
- Retention scales with tier: 7 days on Free, 30 days on Starter, 90 days on Pro; short retention limits how far back you can reconstruct a slow-moving failure.
- Event volume is the metered unit and an event is one captured operation — an agent turn, model generation or tool call — so a conversation with several turns and tool calls consumes several events; a high-traffic agent moves past 1,000 events/month quickly.
- At your limit, automated analysis and dashboard access pause until usage falls below your allowance.
- Finally, this is a monitoring and improvement layer: it will not replace your latency, cost or error-rate APM, and it is not a regression test framework.
as of 2026-10-15
Verification history
We have re-verified Agnost AI 4 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Agnost AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
A solo builder or small team with an agent in early production that wants to see real failure clustering before paying anything.
What this tier adds
Starting tier: $0/mo for 1,000 events/month, 7-day retention, full product access including automatic intent and sentiment discovery.
Starter
$49/mo
Ideal for
A team with one low-volume production agent that needs more history and a hand getting instrumented.
What this tier adds
$49/mo adds 10x the event volume (10,000/month), retention to 30 days, onboarding help and self-improvement suggestions.
Pro
$499/mo
Ideal for
A high-volume agent iterating fast where 7- or 30-day retention is not enough to reconstruct slow-moving failures.
What this tier adds
$499/mo steps to 1,000,000 events/month, 90-day retention and founders Slack access on top of Starter.
Enterprise
Custom
Ideal for
Regulated or large buyers who need the agent running inside their own network and formal audit and SLA commitments.
What this tier adds
Custom price adds self-hosted VPC deployments, audit logs, custom SLAs/SLOs, custom improvement workflows and fine-tuned SLMs over Pro.
Where the pricing makes sense
The company stage and team size where Agnost AI's pricing actually pencils out — and where peers do it cheaper.
Free ($0/mo, 1,000 events) is genuinely early production, not a pilot for anything busy. Starter at $49/mo (10,000 events, 30-day retention) fits a single low-volume agent with someone who needs onboarding help. Pro at $499/mo (1,000,000 events, 90-day retention, founders Slack) is the real working tier and sits above what most trace-only observability tools charge, while staying below full enterprise APM contracts. Enterprise is custom-priced once you need VPC deployment or audit logs.
Setup time & first value
How long it actually takes to get something useful out of Agnost AI — broken out by persona, not the marketing-page minute.
Under an hour for instrumented frameworks: run `npx skills add AgnostAI/skills --skill agnost-ai` or the vendor's prompt against your codebase, connect your framework, and events start flowing. Custom harnesses via Python, TypeScript or OpenTelemetry take longer. Real value needs days of live traffic — there is nothing meaningful to cluster on day one.
Switching to or from Agnost AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith or other trace-only observability: run Agnost alongside your existing tracing and let it cluster the same production traffic, since Agnost reads conversations rather than replacing your trace store.
- →From manual QA spreadsheets: replace the tab of anecdotal user complaints with the clusters view, which ranks the same complaints by users affected.
- →From a homegrown OpenTelemetry pipeline: point the existing OTel instrumentation at Agnost for VoltAgent and Vercel AI SDK paths instead of maintaining custom dashboards.
- ↗To a generic APM tool: keep your latency/cost/error-rate monitoring in place, since Agnost does not cover that layer.
- ↗To a deterministic agent test framework: export the regression evals Agnost generates from production traces and maintain them as standing test cases.
- ↗To a trace-only observability tool: retain framework instrumentation and route events there if you no longer need behavioral failure clustering.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Agnost AI”, and we withheld 6: 6 could not be judged, because “Agnost AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Agnost AI.
Official links
Tools that pair well with Agnost AI
Common stack mates teams adopt alongside Agnost AI, with the specific reason each pairing earns its keep.
Raindrop
Raindrop traces, detects, and simulates fixes for silent AI agent failures in production before they reach your users.
Galileo AI Evals
Galileo AI Evals turns offline evaluations into production guardrails that gate your AI agents in real time.
Comet
Opik by Comet turns LLM traces into root-cause groupings, eval scores and code fixes so agent teams stop shipping the same bug twice
Featured Head-to-Head Comparisons
Agnost Ai vs Dash0
If you're an SRE or DevOps team standardizing on OpenTelemetry and need a full-stack observability platform with built-in AI assistance, Dash0 is the clear choice — its freemium tier and transparent pricing lower the barrier. But if your pain is specifically AI-agent failures that evals miss, Agnost AI is laser-focused on that niche, though its lack of integrations and opaque pricing make it a harder sell for broader adoption.
Agnost Ai vs Arize Phoenix
If you need to catch production edge cases that standard evals miss and want a quick, focused monitoring solution, Agnost AI is your pick. But if you want a comprehensive, open-source, self-hostable platform for tracing, evaluating, and iterating on agent performance, Arize Phoenix is the clear winner.
Agnost Ai vs Phoenix
If you need a fully managed, production-focused observability layer that catches the weird edge cases your evals miss, Agnost AI is the pick — but you'll pay undisclosed enterprise prices and get zero integration ecosystem. If you want free, open-source, self-hostable control with deep trace-level debugging, LLM-as-judge evaluation, and ghost-trajectory simulation, Phoenix wins hands-down. Go Phoenix unless you specifically require a commercial vendor's closed-box anomaly detection.
Alternatives to Agnost AI
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Raindrop
Raindrop traces, detects, and simulates fixes for silent AI agent failures in production before they reach your users.
Galileo AI Evals
Galileo AI Evals turns offline evaluations into production guardrails that gate your AI agents in real time.
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