How VibeModel Works

Most AI tools give you a model. VibeModel discovers what your model needs to handle — then composes the architecture to handle it.

INPUT
DISCOVER
COMPOSE
OUTPUT

Four Dimensions. 155+ Patterns.

Every AI agent faces four types of variation: what tasks it handles, what data it processes, what responses it generates, and what tools it calls. VibeModel maps every combination.

Task Patterns

  • ▪Classification
  • ▪Retrieval
  • ▪Generation
  • ▪Reasoning
  • ▪Planning

Data Patterns

  • ▪Structured
  • ▪Unstructured
  • ▪Multimodal
  • ▪Streaming
  • ▪Batch

Response Patterns

  • ▪JSON Output
  • ▪Natural Language
  • ▪SQL Query
  • ▪Code Generation
  • ▪Hybrid

Tool Patterns

  • ▪API Calls
  • ▪Database
  • ▪File I/O
  • ▪Cache
  • ▪External Service

Pattern Combinations

12 Task × 8 Data × 6 Response × 4 Tool

= 155+ Instruction Patterns

Not All Patterns Are Equal

Simple Patterns

42

Single source, high confidence. Direct lookup patterns.

19% of all patterns

Complex Patterns

98

Multi-source cross-referencing. Require orchestration.

45% of all patterns

Fuzzy Patterns

76

Ambiguous, may need human review. These break production agents.

35% of all patterns

The 35% that's fuzzy? That's also your highest-value, most unpredictable traffic. Every competitor ignores it. VibeModel finds it before your users do.

From Patterns to Architecture

1

Derive question patterns

What variations of user requests exist?

2

Derive data combination patterns

What data sources and formats interact?

3

Derive response patterns

What output formats and structures are needed?

4

Combine all patterns

Cross-multiply dimensions into full instruction set

5

Extract meta-patterns

Find the architectural drivers that determine component selection

13 Components. 6 Layers. Composed, Not Templated.

Derived from 216 patterns and 24 meta-patterns. Not a template — composed for your use case.

DATA & CONTEXT

Data Fetching

Parallel AsyncIO Connectors

Data Normalization

Pydantic Models + Custom Mappers

Document Parsing

PDFPlumber + EasyOCR

Retrieval/RAG

Hybrid Vector + Keyword Search

CORE ENGINE & EXECUTION

Orchestration

LangGraph State Machine

Error Handling

Custom Exceptions + Retry + Circuit Breaker

Aggregation & Analytics

Pandas + DuckDB

Confidence Scoring

Bayesian Scoring + Threshold Routing

Human-in-the-Loop

Manual Escalation Queue + Approval Workflow

Anomaly Detection

Scikit-learn z-score + IQR

OUTPUT & OBSERVABILITY

Caching & State

Redis TTL-aware

Output Synthesis

Jinja2 + JSON Schema Renderer

Observation & Logging

Structured JSON Logging + LangSmith

Every Architecture Gets Evaluated

This is a Cybersecurity SOC Triage Agent. Want to see what VibeModel composes for YOUR use case?

Primary Metric

MTTD

PASS
Target: < 2.0 minActual: 1.4 min

Secondary

Threat Match

PASS
Target: > 90%Actual: 94.8%

Tertiary

Containment

PASS
Target: < 10 minActual: 4.2 min

Quaternary

FP Rate

PASS
Target: < 15%Actual: 7.8%
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