Reasoning Engine Overview
What Is the Reasoning Engine?
The Reasoning Engine is an AI-powered analysis platform that takes structured data -- time-series metrics, categorical measurements, numeric datasets -- and produces validated, actionable insights through a coordinated team of specialized AI agents. Each agent handles one analytical discipline (statistics, pattern recognition, correlation, narrative generation, fact-checking), and they collaborate in a defined pipeline to deliver results no single model could produce alone.
The engine is format-agnostic: it accepts data from any source format through intake adapters and delivers results in any target format through output adapters. The core analytical logic remains unchanged regardless of where data originates or where results are consumed.
Who Is This For?
Product teams use the Reasoning Engine to power data-driven features without building analytical logic from scratch. Provide structured data and a question, and the engine returns a validated narrative with confidence scores, key findings, and actionable recommendations.
Integration teams use it as a composable analytical building block. Connect your data source via an intake adapter and your output target via an output adapter, and the same pipeline works across applications without modification to the core engine.
Data analysts use the engine's structured output -- temporal statistics, correlation coefficients, detected patterns -- as a foundation for further analysis or reporting.
What Makes It Different?
| Approach | Limitation | Reasoning Engine |
|---|---|---|
| Single LLM call | One model does everything; hard to tune individual aspects | 10 registered agents, each independently tunable |
| Rule-based analytics | Rigid; cannot handle nuance or unexpected patterns | Dual-mode processing: algorithmic baselines + AI enhancement |
| Black box analysis | No verification of output accuracy | Built-in validation agent fact-checks every claim against source data |
| Hardcoded formats | New data sources require core changes | Adapter pattern: add new formats without touching the engine |
Pipeline at a Glance
The engine executes a 7-step sequential pipeline. Each step builds on the accumulated output of prior stages:
| Step | What Happens | Agents Involved |
|---|---|---|
| 0. Preflight | Validates input structure, optionally determines which steps are needed | Step Orchestrator (optional) |
| 1. Temporal Analysis | Computes statistics and identifies trends across time-series data | Temporal Agent |
| 2. Pattern Recognition | Detects behavioral patterns, recurring cycles, and warning indicators | Pattern Agent |
| 3. Correlation Analysis | Computes and interprets statistical relationships between metrics | Normalizer + Analysis Agents |
| 4. Domain Knowledge | Retrieves relevant external knowledge based on detected findings | Query Generator + Domain Retrieval Agents |
| 5. Explanation | Synthesizes all findings into a structured, user-facing narrative | Explanation Agent |
| 6. Validation | Fact-checks every claim in the narrative against the computed data | Validation Agent |
Every step supports dual-mode processing: deterministic algorithms execute first (reliable, reproducible), then AI agents layer semantic understanding on top (nuanced, contextual). The pipeline always produces a baseline result even if an agent is temporarily unavailable.
Format-Agnostic Architecture
The engine does not know or care where data comes from or where results go. Adapters handle all format-specific logic at both ends of the pipeline:
Intake adapters transform source-specific data into a canonical schema the engine understands. Output adapters transform the canonical result into whatever format the consumer requires. Multiple applications can share the same analytical pipeline -- only the adapters change.
Supported Adapter Types
Intake:
- JSON Array Adapter -- nested object arrays
- API Payload Adapter -- structured API responses
- CSV Adapter -- tabular data from external systems
- Auto-detection -- the system can automatically select the correct adapter based on input structure
Output:
- Dashboard Formatter -- JSON optimized for UI rendering
- Report Formatter -- structured data for PDF generation
- API Response Formatter -- REST-friendly JSON
- Plain Text Formatter -- human-readable summaries
Canonical Input Structure
The engine accepts a standardized input regardless of the original source format:
| Field | Type | Required | Description |
|---|---|---|---|
objective | string | Yes | The analytical question to answer |
series | array | No | Time-series data (timestamp + value pairs per metric) |
datasets | array | No | Numeric arrays for correlation analysis |
hints | string[] | No | Additional context to guide the analysis |
outputProfile | string | No | Target output format (e.g., dashboard, text, api-v1) |
At least one of series or datasets must be provided alongside the objective.
Example Input
{
"objective": "Help me understand my stress patterns this week",
"series": [
{
"id": "stress_level",
"label": "Daily Stress",
"points": [
{ "t": "2025-01-06T00:00:00Z", "v": 3.5 },
{ "t": "2025-01-07T00:00:00Z", "v": 4.0 },
{ "t": "2025-01-08T00:00:00Z", "v": 4.5 },
{ "t": "2025-01-09T00:00:00Z", "v": 3.8 },
{ "t": "2025-01-10T00:00:00Z", "v": 4.2 }
]
},
{
"id": "sleep_quality",
"label": "Sleep Quality",
"points": [
{ "t": "2025-01-06T00:00:00Z", "v": 7 },
{ "t": "2025-01-07T00:00:00Z", "v": 6 },
{ "t": "2025-01-08T00:00:00Z", "v": 5 },
{ "t": "2025-01-09T00:00:00Z", "v": 6 },
{ "t": "2025-01-10T00:00:00Z", "v": 5 }
]
}
],
"hints": ["User reports difficulty sleeping on work nights"],
"outputProfile": "dashboard"
}
Output Structure
The engine produces a structured analytical result. The key fields are listed below. For the complete schema with nested field definitions and a JSON example, see Output Schema.
| Field | Description |
|---|---|
taskId | Unique task identifier for tracing |
objective | Original analysis objective |
temporal | Computed statistics and trend assessments per metric |
pattern | Detected behavioral patterns with confidence scores |
correlation | Pairwise metric relationships with coefficients |
domain | External knowledge relevant to detected findings |
explanation | Synthesized narrative (2-4 paragraphs), recommendations, and sources |
validation | Fact-check results (pass/fail with specific issues) |
result | Summary, key insights, recommendations, and overall confidence score |
executedSteps | Which pipeline steps ran, were skipped, or failed |
timing | Execution duration |
Confidence Levels
| Level | Data Requirement | Output Characteristics |
|---|---|---|
| Low | Fewer than 5 data points | Preliminary language, explicit uncertainty caveats |
| Medium | 5 to 14 data points | Moderate certainty, patterns noted with limitations |
| High | 14+ data points | Strong confidence, specific claims backed by robust data |
Key Design Principles
Dual-Mode Processing
Every analysis step runs deterministic algorithms first, then layers AI enhancement on top. This guarantees a baseline result even when agents are unavailable, and ensures reproducible statistical foundations beneath the AI-generated interpretations.
Fail-Safe Pipeline
When any agent fails or returns invalid output, the pipeline continues with degraded results rather than terminating. The failed step is logged and downstream agents operate on available data. The validation agent flags the gap so consumers are aware of reduced coverage.
Selective Execution
The Step Orchestrator Agent evaluates the user's question against the available data to determine which steps are needed. A simple statistical query skips pattern detection, correlation, and domain retrieval -- reducing latency and eliminating unnecessary API cost.
Configurable Agent Behavior
Each agent's system prompt and model configuration can be updated without redeployment. Changes take effect immediately through automatic cache invalidation. Different agents can target different model deployments, enabling cost and quality optimization per analytical stage.