Polaryst gives product managers behavioral visibility into AI agent conversations — no engineering tickets, no code, no dashboards built from scratch. Just clear answers to why your agent fails and what to change.
Trusted by product teams at early-stage AI companies.
Your AI agent is live.
But you have no idea what's actually happening inside it.
Every week, your AI agent handles hundreds or thousands of conversations. Some of those conversations fail — the user abandons mid-session, the agent misunderstands intent, reasoning breaks down, trust collapses. You know it's happening. You can see it in your retention numbers, your support volume, your NPS. But you cannot see inside the agent to understand why.
Today, the only path to that visibility runs through engineering. File a ticket. Wait for a trace. Interpret raw logs. By the time you have an answer, the damage is already done.
Silent failures go undetected
The agent appears operational. Underneath, users are abandoning, looping, and losing trust. There is no alert. There is no flag.
Every insight needs a ticket
Product teams have zero direct access to behavioral data. Understanding a single conversation requires developer involvement and days of turnaround.
Generic metrics tell you nothing
CSAT and session duration do not explain behavior. You need to know whether the agent understood intent, completed the goal, and maintained trust.
Built for engineers.
Useless for product teams.
Langfuse, Helicone, Braintrust, and Grafana are powerful systems. They track token counts, latency, API traces, and cost per call. They answer the engineering question: what happened in the infrastructure? They do not answer the product question: why did the user experience fail?
These tools were designed for developers who can write evaluation pipelines in code, interpret log streams, and configure custom dashboards. They require engineering involvement at every step. For a product manager, they are inaccessible by design.
| Capability | Langfuse / Helicone / Braintrust | Polaryst |
|---|---|---|
| Designed for | ✕Engineering teams | ✓Product managers |
| Primary output | ✕API traces, token logs, latency | ✓Behavioral grading, failure reasons, improvement directions |
| Requires engineering to use | ✕Yes | ✓No |
| Understands user intent | ✕No | ✓Yes |
| Detects silent failures | ✕No | ✓Yes |
| Generates improvement actions | ✕No | ✓Yes |
| Business context awareness | ✕No | ✓Yes |
Behavioral intelligence for your AI agent.
No engineering required.
Polaryst connects to your AI agent and converts every conversation into a behavioral signal. It automatically grades user intent, detects failure patterns, and surfaces the specific changes that will improve your agent — all in plain language, all without filing a single engineering ticket.
Observe every conversation
Every user message, agent response, and reasoning step is captured and stored in a searchable conversation library.
Grade behavior automatically
Graders calibrated to your product evaluate each conversation for intent accuracy, goal completion, and failure signals.
Act on what you find
Clear improvement directions — prompt changes, tool instructions, retrieval tweaks — in language any PM can act on.
From observation to improvement.
Continuously.
Polaryst runs a 7-step behavioral improvement loop on every conversation your agent handles. Each step builds on the last.
Everything a product team needs
to understand and improve their agent.
Conversation Library
Every agent conversation, searchable and filterable by date, grader result, user intent, and failure type. No log parsing required.
Behavioral Graders
Automatically generated graders calibrated to your product, your users, and your definition of success. No manual evaluation setup.
Failure Detection
Conversations showing high-intent abandonment or thin UX friction are automatically surfaced. You see the problem before users stop coming back.
Reasoning Trajectory
Step through the agent's reasoning turn by turn. See exactly where intent was misread, logic broke, or the user lost trust.
Experiment Sandbox
Test new prompts, tool instructions, or system messages against real past conversations before shipping changes.
Improvement Intelligence
Polaryst surfaces the specific change — prompt structure, retrieval data, tool instruction, workflow step — most likely to fix the pattern.
Not for engineers.
For the people responsible for the product.
Owns the agent roadmap. Needs to know what to fix next and how to measure whether a change actually worked.
Responsible for the conversation experience. Needs to see where users get stuck, confused, or frustrated — in their own language.
Handles escalations when the agent fails. Needs root cause of complaints without relying on engineering to pull logs.
Accountable for agent quality at the business level. Needs a clear metric for performance and improvement over time.
Simple pricing.
Aligned with your team size.
Polaryst is currently in early access. Pricing will be based on the number of agent conversations evaluated per month and the number of active graders. No per-seat pricing. No hidden engineering setup costs.
Starter
For early-stage teams evaluating up to 5,000 conversations per month.
- Up to 5,000 conversations / mo
- 10 graders
- 1 agent
- Email support
Growth
For scaling teams with multiple agents and higher evaluation volume.
- Up to 50,000 conversations / mo
- Unlimited graders
- Up to 5 agents
- Experiment sandbox
- Priority support
Enterprise
For teams with complex agent infrastructure and custom evaluation needs.
- Unlimited conversations
- Custom grader logic
- SSO and access controls
- Dedicated onboarding
- SLA support
All plans include full access to behavioral grading, failure detection, and the improvement loop. No feature is gated behind tier upgrades.
Stop guessing why your agent fails.
Start seeing it.
Request early access to Polaryst and get behavioral visibility into your AI agent within your first session.
No engineering setup required. Onboarding takes under 30 minutes.