The learning system at a glance
The learning system is what lets the agents get better over time. It stores the history of interactions, analyses it, and uses what it finds to tune how the agents perform.
The continuous learning loop
graph LR
A[📥 Interactions
collected] --> B[💾 Structured
storage]
B --> C[🔍 Pattern
analysis]
C --> D[⚡ Prompt
optimisation]
D --> E[🚀 Knowledge
applied]
E --> F[📊 Feedback and
evaluation]
F --> A
style A fill:#e3f2fd
style C fill:#fff3e0
style E fill:#e8f5e9
Storage
A structured record of every interaction, prompt, response and piece of feedback.
Analysis
Processing that history to find patterns and openings.
Application
Using what it learned to tune prompts and improve responses.
System components
Knowledge base
The system's memory. It holds every interaction between agents and users — prompts, responses, feedback and the metadata that goes with them.
Organised by agent, domain and task type
Fast lookup of the relevant interactions
Tracks changes and how prompts evolve
Protection of sensitive data, and compliance
Pattern analyser
Reads the interaction history for patterns, trends and correlations that can improve how the agents perform.
graph LR
A[📝 History] --> B[🔎 Pattern
identification]
B --> C[📊 Effectiveness
analysis]
C --> D[🏷️ Topic
extraction]
D --> E[😊 Sentiment
analysis]
E --> F[⚠️ Anomaly
detection]
F --> G[📈 Insights]
Prompt optimiser
Takes what the pattern analyser found and improves the prompts, which produces more accurate and more relevant responses.
The learning process
Learning runs as a continuous loop: a handful of connected steps that keep raising how well the agents perform.
Data collection
Every interaction is recorded: prompts, responses, feedback and context.
Structured storage
The data is organised in the knowledge base and indexed properly.
Pattern analysis
The system reads the history for trends and openings.
Prompt optimisation
Prompts are refined automatically on the basis of those findings.
Applying the knowledge
What the system learned is applied to new interactions.
Feedback and evaluation
Those new interactions are evaluated, which feeds the loop again.
Example: tuning a marketing analysis
flowchart TB
A["👤 User asks:
'Analyse our email campaign'"] --> B["🤖 Agent returns a
basic analysis"]
B --> C["❌ User: 'Segmentation
data is missing'"]
C --> D["💾 System stores the
interaction + feedback"]
D --> E["🔍 Analysis finds:
campaign reviews need
segmentation"]
E --> F["⚡ Tuned prompt:
'Analyse, including
metrics and segmentation'"]
F --> G["✅ Similar requests now
get the full
analysis"]
style A fill:#fff3e0
style G fill:#e8f5e9
Learning metrics
Performance metrics
Evolution metrics
Transparency and control
Every optimisation is tested and validated before it ships. The system keeps a full record of the changes, which is what makes it transparent and reversible when it needs to be.