Self-Learning Agents
Your agent on day 1 vs. day 30
Most voice agents sound the same on call 1,000 as they did on call 1. Stellar agents get better. They analyze every conversation, surface patterns, and adapt, with your approval.
How it works
Every call is analyzed
After each conversation, Stellar analyzes the transcript. It extracts objections, sentiment shifts, and flow issues.
Patterns emerge
Individual insights are aggregated into learnings. When the same objection appears across 5+ calls, it becomes a confirmed pattern.
Suggestions surface
Learnings appear in the Insights tab with confidence scores. You see exactly what the agent wants to change and why.
Approved changes take effect
Approve a suggestion and it becomes part of the agent's behavior on the next call. Dismiss it and the agent moves on.
What the agent learns
Seven categories of improvement, all derived from real call data.
Objection handling
New responses to price, timing, and competitor objections
Question flow
Reordering questions based on what keeps leads engaged
Pacing
Adjusting speed and pause length to match caller patterns
Opening lines
Testing greetings that reduce early hang-ups
Closing technique
Stronger asks when the lead is ready to commit
Rapport building
Small talk patterns that increase call duration
Tone calibration
Matching formality level to the audience
The Insights tab
Every suggestion shows the pattern it detected, how many calls contributed, and a confidence score. Toggle auto-improve on for hands-off operation, or review each change yourself.
You stay in control
Nothing changes without your approval. Every learning goes through an approve/dismiss workflow before it affects agent behavior. You can also enable auto-improve for high-confidence suggestions if you prefer a hands-off approach.
The agent proposes. You decide.
Better calls, automatically
Start with a good agent. Let it become a great one.