Behavioral evaluation Platform for AI agents
Polaryst applies structured behavioral graders to production agent conversations. Surface failure patterns, diagnose root causes, and measure improvement, without engineering dependency.
Built for product teams at companies shipping AI agents to production
Product teams have zero behavioral visibility into production agents
Traditional observability tracks system metrics - latency, tokens, errors - but misses user intent and trust. This creates a structural gap: Engineering owns the Platform, but Product owns the experience. Polaryst bridges that gap.
standardized behavioral metrics for AI agents across the industry
of product teams rely on manual conversation review to assess agent quality
average time for a PM to get an answer about agent behavior from engineering
Sources: LangChain State of AI Agents 2025, internal research
Where User Intent
becomes a black box.
The gap between raw tokens and user satisfaction is where agents fail. Polaryst makes the invisible, visible.
Behavioral Intelligence
Translating raw logs into actionable product strategy.
The behavioral
intelligence layer
Polaryst provides the infrastructure to observe, evaluate, and optimize AI agent behavior at scale.
Behavioral Detection
Surface failure patterns that Platform metrics miss.
Zero Engineering Dependency
Onboard in minutes with just a URL.
Contextual Grading
Evaluation logic calibrated to your product.
Closed-Loop Improvement
Direct path from failure to refinement.
The control center for agent behavior
Polaryst provides a unified interface for product teams to monitor, diagnose, and improve agent performance without touching code.
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Trace tr_4k1s
SuccessSession: sess_990123 • User: user_8812 • Mar 12, 2:14:05 PM
Behavioral Analysis
The agent requested information that was previously provided in the context window. This indicates a context-retrieval failure or prompt-instruction conflict.
User frustration detected at turn 3. Trust score dropped from 0.82 to 0.41.
Model Metadata
Engineering observability is not enough
Infrastructure tools tell you if the agent is alive. Polaryst tells you if the agent is actually working for your users.
The Missing Layer
Infrastructure Layer
Logs, Traces, Metrics. Owned by SRE/DevOps. Focuses on system uptime and model latency.
Behavioral Layer
Intent Resolution, Trust Scores, Behavioral Flags. Owned by Product. Focuses on user outcomes.
Integrate in minutes,
not sprints
Polaryst is designed to be non-intrusive. We don't sit in the critical path of your agent. Simply forward conversation objects to our ingestion endpoint via our lightweight SDK or a standard webhook.
No Latency Impact: Asynchronous ingestion ensures your agent performance remains unaffected.
Privacy First: PII redaction happens at the edge before data ever reaches our servers.
Universal Compatibility: Works with any LLM, orchestration framework, or custom stack.
Built for the people who own agent quality
but cannot measure it
Owns the agent roadmap, has no behavioral data to prioritize with
Your AI support agent handles 4,000 conversations per week. You learn about failures from escalation tickets and anecdotal Slack messages. Prioritization decisions are based on loudest-customer bias, not systematic behavioral data.
Automated flag detection surfaces the highest-frequency failure patterns daily. PM feedback becomes data-driven.
Reports on product quality, has zero agent-specific KPIs
Your dashboards track page views, NPS, and conversion. None of these decompose to the agent conversation level. When leadership asks whether the AI agent is improving, the answer is qualitative.
Behavioral trend dashboards show goal completion rate, flag frequency, and trust score over time. No engineering data pipeline required.
Hears about agent failures first, has no diagnostic capability
Customers report that the agent gave incorrect information. You file an internal ticket. Engineering investigates. Three to five days later, you get a partial answer.
Search any conversation by customer or flag type. Drill into the exact turn where the failure occurred. Diagnosis takes minutes, not days.
How it works
under the hood
A high-throughput, low-latency evaluation pipeline designed to process millions of agent interactions with zero impact on production performance.
Data Ingestion
Conversations are ingested via lightweight SDK integration or webhook. Each conversation object includes: user messages, agent responses, tool calls, intermediate reasoning steps, timestamps, and session metadata. Polaryst does not require access to your model Platform, prompt templates, or API keys.
Grader Execution
Graders are LLM-powered evaluation functions that take a conversation as input and produce a binary flag as output. Graders execute asynchronously post-ingestion. Per-turn graders run on each agent response for time-sensitive violations (e.g., boundary breaches). Average latency: < 2s.
Feedback Loop
PM feedback (thumbs up/down on individual flags) is stored as ground truth and used to calibrate grader thresholds and prompt parameters over subsequent evaluation cycles. Feedback is append-only and never overwritten by system reconfiguration.
Experiment Engine
The sandbox replays historical conversations against modified agent configurations (prompt changes, system message edits, retrieval parameter adjustments). Experiments produce side-by-side behavioral score comparisons. No live traffic is affected.
Your agents are in production.
Understand what they are doing.
Polaryst is accepting a limited number of design partners for private beta. Request access to get a dedicated onboarding session and a calibrated grader suite for your agent.