Industrial Determinism

Deterministic Evaluation

Reproducibility is the cornerstone of scientific research. AI agents are inherently stochastic due to model temperature and randomized data sampling. AgentV provides a rigid framework to “freeze” this randomness, ensuring that every evaluation run can be perfectly replicated.

The Seed Mechanism

AgentV uses a deterministic seeding strategy that applies to Python’s random, numpy, and the LLM Judge temperature (where supported).

Base Seeds

When you start an evaluation campaign with a base seed (e.g., --seed 12345), the harness initializes the global state. This base seed is the root of the “Randomness Tree” for that entire batch.

Run Index Offsets

In multi-run batches (e.g., running a scenario 100 times to calculate Pass@k), using the same seed for every run would result in identical agent paths (if the agent is also seeded). Instead, AgentV applies a predictable offset:

Final Seed = Base Seed + Run Index

• Run 0: Seed 12345
• Run 1: Seed 12346
• Run 2: Seed 12347

This ensures that while each run is unique, the entire batch is perfectly reproducible if started with the same base seed.

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Replicating Failures

A critical requirement for research-grade evaluation is the ability to revisit a specific failure trace with the exact same conditions.

1. Identify the Failure: Locate the specific failed run in your manifest.json.

   {
     "run_id": "run_042",
     "scenario": "loan_risk",
     "seed": 12387,
     "status": "FAIL"
   }

2. Re-run with Exact Seed: Use the specific seed from the manifest to replicate the exact failure.

   agentv evaluate --path industries/finance --agent http://localhost:5001/execute_task --seed 12387

3. Analyze Trajectory: The agent will follow the same reasoning path, allowing you to observe the state drift or planning error in real-time.

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Research Best Practices

Tip

Always Document Base Seeds: When publishing papers, include the base seed and the version of AgentV in your methodology section. This allows other researchers to verify your findings against the same scenario corpus.

Environmental DNA (E-DNA)

Deterministic seeds only handle stochasticity. To handle environment drift, AgentV captures Environmental DNA snapshots. This includes:

• Registry State: The exact configuration of world shims and simulators.
• Provider Versions: The version of extraction engines used (e.g., Dataproc v1.5).
• Tool Fingerprints: SHA3-256 hashes of all available tool definitions.

By combining Deterministic Seeds with Environmental DNA, AgentV achieves “Industrial Parity”—the guarantee that a test run on your local machine will behave exactly like a test run in a secure research lab.