Agent Analytics & Feedback Loop
Measure, Improve, and Adapt — Turn Usage Into Intelligence
The Agent Analytics & Feedback Loop system in Brainloom provides creators with deep insight into how their agents perform in the real world — and offers the tools to continuously improve them based on data, user interactions, and outcomes.
This is more than just analytics. It’s a full-cycle learning system that empowers agents to evolve over time, guided by human input, structured feedback, and behavioral signals — all under the creator’s control.
Understand How Your Agent is Used
Track everything from technical performance to user experience.
Core Metrics
Query Volume: Total number of interactions over time
Active Users: Unique wallet addresses or user IDs interacting with the agent
Session Duration: Time spent per user interaction
Engagement Rate: Interaction depth, follow-up frequency, or multi-turn usage
Retention: Repeat usage patterns, frequency of returning users
Drop-Off Points: Where users abandon conversations or workflows
Performance Metrics
Latency: Average response time per compute tier or region
Error Rate: Frequency of incomplete, failed, or invalid responses
Compute Cost: Resource usage by query, user, or region
Model Usage: Tokens consumed per model per session
Feedback Channels
Incorporate structured and unstructured feedback directly into your agent lifecycle.
Explicit Feedback
User Ratings: Star-based or emoji-free rating system
Comment Submission: Free-form suggestions, complaints, or testimonials
Feedback Forms: Custom question-based input from users
Post-Interaction Surveys: Triggered after specific workflows or tasks
Implicit Feedback
Behavioral Signals: Interaction length, repeat usage, conversation branching
Correction Patterns: When users rephrase, correct, or override agent output
Goal Completion: Whether users achieve intended outcomes (e.g., task finished, file delivered, booking confirmed)
Closed-Loop Learning
Brainloom allows you to design learning loops that help your agents improve over time — while preserving creator oversight and data sovereignty.
Learning Loop Capabilities
Manual Review: Review flagged interactions before applying changes
Training Triggers: Automatically fine-tune based on usage thresholds or error rates
Crowdsourced Review: Open feedback to community reviewers or DAO voters
Data Control: Choose whether feedback is stored locally, encrypted off-chain, or anonymized on-chain
Adaptive Updates
Push updates to logic or model behavior based on validated insights
Improve memory rules based on real-world usage
Train custom model variants with collected interaction datasets
Visual Analytics Dashboard
An interactive dashboard offers real-time and historical data on agent activity.
Dashboard Views
Usage Overview: Query counts, user trends, peak hours
Performance Timeline: Track performance changes across versions
Revenue Correlation: Analyze monetization patterns vs engagement
Agent Comparison: Compare multiple agents or forked variants
Geo Analytics: Visualize where usage is occurring worldwide
Privacy-Aware Data Handling
All analytics features in Brainloom are designed with privacy and data control in mind.
Data Ownership: Only agent creators have access to usage and feedback data unless explicitly shared
User Consent: Enable or disable feedback prompts per agent
Anonymized Logging: Choose if user IDs and interaction logs are stored pseudonymously
On-Chain Anchoring (Optional): Anchor key events or updates without exposing private data
Use Cases
Improve Conversational Agents
Identify where conversations fail or go off-topic
Optimize Automation Agents
Find workflow steps that cause friction or confusion
Validate Product-Market Fit
Measure how often users return, engage, or convert
Test Monetization Models
Compare user behavior across access tiers or price points
Tune LLM Performance
Analyze model behavior under real-world inputs
Connected Tools
Agent History Viewer: Inspect past sessions in full detail
Flagging System: Automatically flag problematic or unusual behavior
Version Comparisons: See how new releases perform vs previous builds
Feedback API: Pull data into external dashboards or analytics platforms
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