AI Agent Testing Intelligence

Test Reality,
Not Not Assumptions.

CogniSwitch Routes mines your conversation data to extract behavioral patterns, transforming them into actionable test scenarios your simulation engine needs.

Compatible with

Coval, Cekura, Maxim

The Simulation Gap

Generic prompts vs. Production reality

Generic Simulation
"Behave as a frustrated customer."

Why it fails

Vague prompts fail to capture when frustration triggers, how it progresses, or which recovery strategies actually work. It's testing against a caricature, not a user.

Routes Intelligence
PATTERN_ID: HRA_AUTH_001

TURN 05: Agent asks name (2nd time)
TURN 06: User spells name (annoyed)
TURN 18: Agent asks name (3rd time)
TURN 19: User: "I ALREADY TOLD YOU"

Why it succeeds

We extract the exact sequence of events that leads to failure. We tell your simulation platform to test specifically for the "Turn 18 Verification Loop" that causes 14% of drop-offs.

Anatomy of a Behavioral Pattern

 A pattern isn't just a transcript. It is a structured logic flow of triggers, progressions, and recovery states.

01

Trigger Condition

The Inciting Incident

Agent asks for same authentication information 2+ times within 20 turns.
02

State Progression

Emotional Decay

Turn 1: Cooperative → Turn 2: Mildly Annoyed → Turn 3: Explicitly Frustrated
03

Recovery Logic

Success/Fail Gates

IF: Acknowledge + Apologize THEN: 82% Success | IF: Ignore THEN: 0% Success

Structured for Automation

We don't deliver PDFs. We deliver executable code. Export extracted patterns directly into CSV, JSON, or YAML formats compatible with your CI/CD pipeline.

Input: 500+ Call Transcripts
Process: Pattern Mining Engine
Output: Coval/Maxim/YAML"
export_route_v1.yaml
scenario_id: HRA_AUTH_001_ADVANCED
name: "Authentication Retry Frustration - 3rd Request"
difficulty: advanced

user_simulation:
  trigger: "Agent asks for last name again (3rd time)"
  emotional_state: explicitly_frustrated
  response_template: "I ALREADY told you THREE TIMES."
  tone: irritated, emphatic

subsequent_behavior:
  - "All responses 40% shorter than baseline"
  - "No volunteered information"

success_criteria:
  - "Agent acknowledges repeated request within 1 turn"
  - "Agent provides apology OR explanation"

frequency: 14% of production calls

The Extraction Methodology

We treat pattern extraction as a scientific process, not a creative writing exercise. Our 4-phase framework ensures coverage and accuracy.

Engagement Timeline: 6 Weeks
Phase 01 [Wk 1-2]

Pattern Framework

We formulate hypotheses based on your use case (e.g., 'Healthcare Prior Auth') and domain knowledge to create a baseline detection framework.

Output: 15-20 Hypothesized Patterns
Phase 02 [Wk 3-4]

Extraction & Mining

We process logs against the framework to identify real-world occurrences, documenting specific triggers and progressions.

Output: Validated Behavioral Library
Phase 03 [Wk 5-6]

Route Creation

We transform the validated patterns into executable simulation specifications (Natural Language, CSV, YAML) ready for import

Output: Importable Test Scenarios

The "What to Test" Layer

We are not a simulation platform. We are the intelligence layer that tells your simulation platform what experiments to run.

Production Data

Logs and Transcripts

CogniSwitch Routes

Behavioral Intelligence Layer

Core Engine
CO
MX

SIM Platforms

Coval, Maxim, Cekura

Market Position

Category Definition: What We Are Not

vs. Simulation Platforms

They Provide
We Provide
The Infrastructure (The Lab)
The Intelligence (The Experiments)
How to Test
What to Test

vs. LLM-as-Judge

They Provide
We Provide
Probabilistic Scoring
Deterministic Specs
If it's good
How to make it good

Impact by the Numbers

80%
Production Edge Case Coverage
2 Weeks
To Validated Pattern Library
50%
Increase in Day-1 Automation

Stop Guessing, Start Mining

Your agents are failing in ways you haven't imagined yet. Let's find those patterns before your customers do.

THE SIMULATION MANIFESTO

The Happy Path Is a Trap.

You tested the 'return policy' question. Did you test it with a 'leap year' condition? Did you test it with 'angry sentiment'?

Your users will. Static evaluation datasets represent 1% of reality. The other 99% is where your reputation dies.

Routes mines your production data to extract the behavioral patterns you didn't know existed—the ones that actually break your agent.

Break your agent before your users do.