Self-Learning
Agents That Improve Themselves
This is what makes Beam different. Mark what went wrong, get a better prompt in 30 seconds. 5% accuracy to 100% without manual work. Agents learn domain expertise automatically. Industry conventions, formulas, best practices. They self-correct when they learn wrong behaviors. Continuous improvement without maintenance. VW specifically chose us because of self-learning.
Self-Learning
Agents that get better every day
Mark what went wrong, get a better prompt in 30 seconds. 5% accuracy to 100% without manual work. Agents learn domain expertise automatically. Self-correct when they learn wrong behaviors. Continuous improvement without maintenance.
5% to 100% in minutes
Mark what went wrong. AI analyzes the failure, identifies patterns, rewrites the prompt. Validation tests before deploy. Transformation in seconds, not weeks.
View details
Self-corrects learned behaviors
Learned something wrong? The system identifies and corrects it. Continuous refinement, not permanent mistakes.
View details
Learns domain expertise automatically
The system learns industry conventions, formulas, best practices from your feedback. Not just prompt fixes. Real domain knowledge accumulation.
View details
Thumbs up/down is enough
Simple feedback drives improvement. Users don't need to explain what went wrong. Just indicate failure. AI figures out the rest.
View details
Validation before deployment
Every prompt change is tested against failed cases before going live. No regressions. Verified improvements only.
View details
Learns from every interaction
Production feedback continuously improves agents. The more you use them, the better they get. Self-improving by default.
View details
No manual prompt engineering
AI handles prompt optimization. You focus on outcomes, not prompt syntax. Engineering expertise not required.
View details
Track improvement over time
See accuracy trends. Weekly, monthly, quarterly. Prove that agents are getting better. Data for stakeholders.
View details
The Learning Hub for continuous improvement
The Learning Hub tracks tool performance across all workflow nodes, showing accuracy scores for every tool in your agents. Tools below 90% threshold are highlighted as needing attention. Click
Per-tool accuracy tracking
Pre-built actions ready
AI-powered prompt rewriting
How learning works from failure to improvement
When an output fails, the system captures the full context: input data, prompt used, output produced, and the failure signal (thumbs down, rejection, correction). AI analyzes this against successful outputs to identify patterns: what's different about failing cases? What patterns correlate with success? Based on this analysis, the prompt is rewritten with clearer instructions, better examples, or additional constraints. The new prompt is tested against historical failures before deployment. Only verified improvements go live.
Full context capture on failure
Pattern analysis across examples
Targeted prompt rewriting
Domain expertise learning beyond prompts
Self-learning goes beyond prompt optimization. The system learns domain expertise from your feedback: industry conventions (invoice formats, date standards), business rules (approval thresholds, routing logic), technical patterns (API response structures, error handling). This knowledge accumulates over time. An agent processing automotive invoices learns automotive-specific conventions. Healthcare agents learn HIPAA-relevant patterns. Your domain expertise becomes embedded in your agents.
Industry convention learning
Business rule extraction
Technical pattern recognition
Self-correction for fixing wrong learning
Learning systems can learn incorrect patterns. A few mislabeled examples can teach the wrong behavior. Beam's self-learning includes self-correction: the system monitors for patterns it learned that consistently fail, identifies potential incorrect learning, and reverts or adjusts. If a learned pattern starts producing failures, the system recognizes the correlation and corrects course. Not permanent mistakes. Continuous refinement.
Pattern monitoring against outcomes
Incorrect learning detection
Automatic correction of wrong patterns
Feedback mechanisms for simple improvement
Self-learning is powered by feedback, but feedback doesn't need to be complex. Thumbs up/down provides a simple binary signal. This worked, this didn't. Approval workflows mean approvals are positive feedback, rejections are negative. Corrections let you edit the output to show what it should have been. Natural language lets you describe what went wrong in plain text. Any feedback type works. The system extracts learning signal from whatever you provide.
Thumbs up/down
Approval-based feedback
Output corrections
Validation and testing for verified improvements
Before any prompt change goes live, validation testing runs automatically. The new prompt is tested against historical failure cases. If it doesn't improve those cases, it doesn't deploy. Regression testing checks that improvements don't break previously working cases. Only verified improvements reach production. Track validation results: how many failures were fixed, any regressions detected, confidence score for the change.
Automatic validation against failures
Regression testing
Only verified improvements deploy
Improvement tracking to prove progress
Track accuracy trends over time: daily, weekly, monthly, quarterly. See improvement curves for each agent and tool. Compare pre/post optimization performance. Identify patterns: which tools improve fastest? Which need more attention? Generate reports for stakeholders showing measurable improvement. When someone asks
Accuracy trends over time
Pre/post optimization comparison
Tool-level improvement tracking
Enterprise learning that scales
As your agent fleet grows, learning stays manageable. Workspace-level learning: improvements apply across relevant agents. Cross-agent pattern sharing: a fix for one invoice agent can improve all invoice agents. Learning governance: control which improvements auto-deploy vs require review. Multi-tenant isolation: learning for one client doesn't affect others. Enterprise-scale learning without enterprise-scale complexity.
Workspace-level learning
Cross-agent pattern sharing
Learning governance controls
