Analytics for AI agents. Automated evaluation of every conversation against PM-defined rubrics. No engineering ticket. No manual log review. No guesswork.
Built by a team that has lived this problem. Read our story
Your infrastructure monitoring shows uptime and latency. Your tracing tool shows spans and call chains. Everything looks green. But none of these systems answer the question your PM needs answered: did the user accomplish their goal?
“Teams can observe that their agent ran. They cannot observe whether it worked.”
Behavioral signals are the critical “fourth pillar” of AI observability, beyond traces, metrics, and logs. In industry surveys, observability is consistently the single lowest-rated component of the AI stack.
A 52-point gap between knowing what ran and knowing what worked.
Infrastructure telemetry says the request returned 200 in 1.2s. It cannot tell you the response hallucinated your pricing or silently abandoned the user’s intent by turn four.
Single-turn benchmarks are where models get evaluated. Multi-turn production conversations are where they fail. These degradation modes only emerge across turns, invisible to any tool that operates at the trace or request level.
System prompt adherence degrades as conversation length increases. Behavioral constraints from turn zero are progressively overwritten by user context.
The agent resolves ambiguity incorrectly and proceeds confidently down the wrong path. The user doesn’t realize until several turns later.
Earlier turns are displaced by recent context. The agent contradicts its own prior statements or silently drops requirements.
By turn six, the agent is solving a different problem. No individual turn looks wrong. The trajectory is the failure.
These are not edge cases. Every failure mode produces a “successful” trace: 200 status, tokens consumed, latency within SLA. The failure is only visible through conversation-level evaluation.
Polaryst ingests every conversation your AI agent has and performs session-level evaluation: automated assessment of whether each conversation achieved its intended outcome.
Connect your agent. Every session, user, and turn flows in automatically. Zero instrumentation burden.
Every conversation is assessed across quality dimensions you define: goal completion, intent recognition, friction, hallucination. Analytic rubrics, not thumbs-up/thumbs-down.
Which intents fail consistently. Where conversations break. Which users activated vs. which are about to churn. Structured for a PM to act on.
Edit your agent’s system prompt or routing logic. Replay real conversations through the modified harness. Measure the delta before deploying.
The output is product intelligence. Grader scores per dimension. Churn risk flags. Intent failure rates by cohort. Every insight structured for a PM, not an engineer.
Polaryst uses analytic rubric decomposition: criteria broken into specific, independently-scored dimensions, shown to outperform holistic scoring in evaluator-human agreement.
Three layers, each grounded in research:
Each grader evaluates conversations against a specific dimension: goal completion, intent recognition, tone, hallucination rate. Define them through chat or manually. Polaryst generates calibrated rubrics.
Your team annotates conversations and graders. Every annotation refines the evaluation layer. Grader prompts are autonomously refactored. The system learns from your judgment.
An always-on layer that detects when grader scoring has drifted from its baseline. It flags degradation, identifies which dimensions shifted, and triggers recalibration automatically.
The moat is your accumulated evaluation intelligence. After three months, your Polaryst instance reflects your product’s quality standards, your users’ patterns, and your team’s judgment. A competitor starting from zero cannot replicate that.
| Trace tools | Product analytics | Polaryst | |
|---|---|---|---|
| Primary user | Engineers | Product / Growth | Product / CX / Leadership |
| Unit of analysis | Traces and spans | Events and funnels | Conversations and outcomes |
| Evaluation | Manual, holistic | ✗ None | Analytic rubric, PM-defined |
| Multi-turn | Per-turn only | ✗ None | Session-level trajectory |
| Churn signals | ✗ None | Usage-based | Conversation-level behavioral |
| Sandbox testing | ✗ None | ✗ None | Replay real conversations |
| Eng. dependency | High | Moderate | ✓ None |
Polaryst performs longitudinal evaluation across your entire conversation history. Ask it anything:
“Why are enterprise pricing users dropping off after the second turn?”
“Show conversations where intent in turn one diverged from the final resolution.”
“Compare goal completion rates before and after last Tuesday’s prompt update.”
Answers come with evidence: specific conversations, grader scores, trajectory patterns, and before/after comparisons across agent versions.
See which intents your agent handles well and which fail systematically. Track goal completion by segment, intent, and conversation depth.
Stop reading random transcripts. Polaryst surfaces the conversations that matter: already graded, categorized by failure mode, ranked by impact.
The board asks: "Is the AI working?" Show goal completion trends, churn-to-quality correlations, and evaluation improvement curves over time.
We spent years building AI products and watching the same pattern repeat: teams ship agents, celebrate the launch metrics, then discover they have no visibility into whether users are being helped.
“Every tool in the market was built for engineers monitoring infrastructure. Nobody had built the Mixpanel for AI agents.”
The problem is well-documented. The solution did not exist. So we built Polaryst.
Early access for a small number of teams. If you’re shipping an AI agent and can’t answer “did it work?” with data, we should talk.
Talk to us (15 min)Or reach out directly: yash@polaryst.com