TensorOne Evals

TensorOne Evals is our in-house evaluation system built to benchmark and stress-test the behavior of models, agents, and chains—under structured, adversarial, and failure-prone conditions.

It’s not just about accuracy. It’s about resilience, consistency, reasoning depth, latency, and fallback success in high-complexity, multi-agent environments.

What Makes It Different?

Traditional evals focus on clean input → output comparisons. That’s not enough in dynamic agent systems. TensorOne Evals goes further by:

Tracking multi-hop chains and reasoning paths
Logging fallback logic and retries
Measuring latency at every node
Validating structure, type, and tone of model outputs
Stress-testing chains with adversarial mutations

Evaluation Modes

1. Scenario-Based Evals

Each test is defined as a realistic scenario, not a static prompt. We include:

User intent
Task complexity score
Expected outcome dimensions (e.g., reasoning steps, tone, schema)

{
  "scenario": "Summarize a hostile customer review into a neutral tone",
  "expected_traits": ["accuracy", "politeness", "emotional detachment"]
}

2. Agent Behavior Evaluation

We measure:

Message type distribution (inform, assert, escalate, etc.)
Argument chain length
Loop detection and resolution
Self-consistency across repeated runs

3. Fallback & Redundancy Testing

Each eval can trigger:

Endpoint failure
Schema violations
Timeout simulations
Memory inconsistency

MCP handles fallback routing, and eval logs show which models succeeded and why.

Key Metrics

TensorOne Evals captures and logs:

Metric	Description
`latency_ms`	Time to complete step or full chain
`retry_count`	Number of failed → retried steps
`output_valid`	Boolean if output passed type/schema checks
`deviation_score`	Distance from expected answer format
`fallback_triggered`	Whether a fallback model was used
`agent_loops`	Loop patterns detected in agent replies

All metrics are timestamped, agent-tagged, and stored per-thread.

Tooling and Infra

Eval Orchestrator – Runs tests via CLI or SDK against live endpoints
Mutation Engine – Applies stress changes to prompt structure, memory, or tone
Eval Viewer – Internal UI to filter and inspect runs by model, agent, or scenario
LogSync – Mirrors eval logs to external analytics systems (BigQuery, Supabase, etc.)

Examples

Example Eval: Chain Robustness

Goal: Evaluate whether a 4-agent reasoning chain can recover from a failed summarization step.

scenario: 'Condense multi-agent conversation into a bullet list'
agents: [planner, researcher, critic, summarizer]
failure_injection:
  step: 'critic'
  type: 'schema_violation'
expected_behavior: 'Fallback or reroute to re-critique'

Outcome:

Retry triggered
Latency: 4200ms
Output: Valid
Route: gpt-4 → claude-3 → internal-fallback

Why We Built This

Static evals can't track real-time chain performance
Open-source tools don't capture agent behaviors or state transitions
We need confidence in fallback, retries, and degraded performance modes

TensorOne Evals bridges the gap between AI quality testing and runtime observability, helping us ship models and agents that are robust in production.

TensorOne Evals isn’t just about test coverage. It’s about trust.
Because when your agents are in the wild, you need more than accuracy—you need accountability.