RobustOps2.O

RobustOps: Multi-Step Email Classification Environment

A minimal evaluation environment for testing robustness, uncertainty handling, and self-correction in AI agents.

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Overview

RobustOps is an evaluation environment for testing how AI agents behave under uncertainty, noise, and multi-step decision constraints in phishing detection tasks.

The goal is to evaluate whether agents can recover from incorrect decisions and adapt under noisy and ambiguous conditions.

Unlike traditional single-step classification tasks, this environment introduces:

• Noisy signals
• Ambiguous inputs
• Multi-step decision making
• Self-correction opportunities

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Problem

Real-world phishing detection is not binary. Signals are often:

• Incomplete
• Conflicting
• Misleading

Agents must:

1. Make an initial classification
2. Revise decisions when new signals appear
3. Handle uncertainty responsibly

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Environment Design

Each episode simulates an email classification scenario.

Signals

• urgent_tone
• suspicious_domain
• spoofed_sender
• benign_context (noise)

Actions

• classify: initial decision (spam / not_spam)
• revise: update decision
• flag_uncertain: defer decision

Rewards

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Example Run

Initial: Signals: ['urgent_tone']
Step 1: classify → wrong → reward -0.2
Step 2: revise → correct → reward +1.0
Final Score: 1.0

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Failure Case

Initial: Signals: ['benign_context']
Step 1: classify → not_spam → reward -0.2
Step 2: revise → still not_spam → reward -0.3
Final Score: 0.0

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Evaluation

We compare two agents:

• Baseline Agent: makes immediate classification without revision
• Uncertainty-Aware Agent: revises decisions based on feedback

Experiments were conducted over 50 episodes with stochastic signal generation.

Results

The Uncertainty-Aware Agent consistently outperforms the baseline by leveraging revision and uncertainty handling strategies.

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Performance Visualization

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How to Run

Install dependencies:

pip install -r requirements.txt

Run evaluation:

python3 evaluate_agents.py

Run baseline only:

python3 run_baseline.py

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Run with Docker

Build the image:

docker build -t robustops .

Run the container:

docker run robustops

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Why This Matters

This environment evaluates:

• Robustness to noisy inputs
• Ability to self-correct
• Decision-making under uncertainty
• Multi-step reasoning

These are critical gaps in current LLM evaluation benchmarks.

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Key Takeaway

Robust performance in real-world settings is not just about making correct predictions, but about knowing when not to make a decision.

Agents that handle uncertainty and revise decisions outperform those that rely on immediate classification under noisy conditions.

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Future Work

• Adversarial signal injection
• Longer multi-step reasoning chains
• Real-world dataset integration

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