> ## Documentation Index > Fetch the complete documentation index at: https://docs.poly.ai/llms.txt > Use this file to discover all available pages before exploring further. # 06.2025 > June 2025 release notes. The **June 2025** PolyAI Agent Studio release focuses on version comparison, configuration flexibility, and UI streamlining. Expand the items for details: You can now compare versions across **functions**, **flows**, and **Knowledge** items–not just KBs. version-comparison-v2

**What's new:** * The sidebar UI highlights additions, edits, and deletions * View full publish history per item This update supports better change management for teams working across large or complex agents, especially when multiple people are working on a single item. Bring dynamic, real-time settings to your agent–without touching flows or code. configuration-builder

**How it works:** * Builders define a JSON **schema** to create structured config forms * Managers populate **values** per environment (Draft, Pre-release, Live) * Forms appear in the **Configuration** tab once the schema is published This setup lets teams safely update logic–like hours, routing, or toggles–without risk to underlying functions. **Example uses:** * Opening hours * After-hours message toggles * Fallback numbers * Environment-specific logic flags configuration-builder

You can now invoke actions, like functions and SMS, by typing `/name` directly into the editor. **Improvements include:** * Cleaner UI for selecting actions * Matches top-level categories and partial names * Can better handle nested actions in the UI Disclaimer messages are now cached in the Cache under **Audio Management**. cache-disclaimer

**Why this helps:** * Ensures consistent delivery of disclaimer audio * Reduces response time when disclaimers are reused across flows or agents * Simplifies troubleshooting when disclaimers fail to play This feature is currently in open beta. Reach out to your PolyAI contact for access and setup. smart-analyst

Smart Analyst is a new chatbot in Studio that reviews a random sample of your recent calls and answers natural-language questions about them. **Ask questions like:** * "How long are my calls on average?" * "Which intents failed yesterday?" * "What's the most common escalation reason?" **How it works:** * Reviews call data from the past 24 hours * Summarizes behavior patterns and performance * Lets you explore agent usage conversationally Agent Memory allows agents to persist and recall information across conversations–like user preferences or recent bookings–using a secure key-value store. **How it works:** * Memory is stored per **identifier** (e.g. phone number) * Functions can access memory using `conv.memory` * Values are written using `conv.state` and saved at call end * Multiple identifiers (like phone and email) can be linked to the same profile This is especially useful for repeat callers or omnichannel use cases, where remembering previous interactions improves containment and personalization. **Example use cases:** * Recall a user's previous delivery date or cheese preference * Link web and phone interactions to a shared memory profile * Personalise greetings based on previous contact history **Developer usage:** ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Read memory cheese = conv.memory.get("cheese_type") # Persist memory conv.state["cheese_type"] = "gouda" ``` Before using Agent Memory in production, a Data Protection Impact Assessment (DPIA) may be required. Contact your PolyAI representative for guidance on compliance requirements.

The [Test Suite](/analytics/test-suite/introduction) lets you save real conversations as test cases and re-run them against draft or sandbox versions to check for behavioral drift. **What's new:** * Save test cases from the chat panel or conversation review * Re-run conversations with new parameters like contact name or phone number * Run tests individually or batch test cases in groups * Review success rates in the **Test Runs** tab Use this to catch issues when refining agents, especially when upgrading functions or dealing with variants. smart-analyst

PolyScore introduces automated LLM-based scoring for conversations–directly in Conversation Review. **What it does:** * Assigns a score from **0–10** per call, where higher is better * Weighs multiple behavioral and qualitative signals * Offers fast, standardized feedback across all calls PolyScore also appears as a filterable field in the **Conversations** tab to help teams quickly identify high- or low-performing calls.