Collective Intelligence
Agents that get better with every interaction
Agents that evolve through feedback loops and dynamic knowledge graphs. Every interaction makes your AI team smarter and more aligned with your business.
How Collective Intelligence works
Follow the journey from input to output - click any step to explore.
Observation
Every interaction generates learning signals - explicit feedback (ratings, corrections) and implicit signals (whether users accepted suggestions, asked follow-ups, or rephrased).
Related capabilities
Interactive Feedback Loops
Rate responses, provide corrections, or give natural language feedback. Agents learn from every interaction - thumbs up/down, inline edits, and conversational coaching all work.
Continuous Fine-Tuning
Agents continuously adapt their behavior based on accumulated feedback. No manual retraining needed - improvements are applied gradually and safely with rollback options.
Real-world scenario
Product Knowledge Evolution
After your product launch, the Support agent initially gives basic answers. Within days of handling real tickets, it learns nuanced troubleshooting paths that even human agents hadn't documented.
Frequently Asked Questions
Yes. Every learning update is versioned. You can review the learning log, revert specific updates, or roll back the entire agent to any previous state. We also run safety checks before applying updates.
Simple corrections (factual updates, preferences) apply within minutes. Behavioral pattern changes require 10-20 similar signals before updating, to prevent overreacting to outliers.
By default, learning applies globally. You can configure per-user personalization for individual preferences (formatting, detail level) while keeping shared knowledge global.
Never. Your data and learning signals are strictly isolated to your organization. We never use customer data to train shared models. Each agent's knowledge is yours alone.
Explore Other Abilities