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PDF stands for Predictability Test Framework

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Objective

To ensure Symphony's agent-driven pipeline operates reliably and deterministically across multiple runs, we propose a formal Predictability Testing Framework. This framework will repeatedly execute defined tasks across agents and evaluate whether the outputs remain consistent, structured, and within acceptable variance thresholds.

Note: The Conductor model may adopt different or adaptive approaches to predictability testing compared to other agents. As the central orchestrator, it may simulate alternate execution paths, perform meta-evaluation of outcomes, or dynamically adjust testing strategies based on agent history and feedback.

1. Motivation

Symphony’s architecture relies on a network of intelligent agents (Enhancer, Planner, Feature, etc.) that generate artifacts at each step. While individual models may be stochastic (e.g., LLMs), Symphony as a system must behave predictably:

• Outputs should be stable across iterations.
• Any variance should be measurable and explainable.
• Failures or drift should be detected early.

Predictability Testing ensures agent interactions and orchestration produce reliable and reproducible outputs over time.

2. Predictability Goals

• Determinism Range: Output artifacts should match baseline in >95% of runs.
• Output Equivalence: Artifacts should be logically equivalent, even if token-level variation exists.
• Error Stability: Failure cases should not increase with iteration count.
• Behavior Consistency: Agents should respect the same contracts and flow regardless of prompt re-entry.

3. Test Architecture

3.1 Test Scenarios