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RobustOps: Multi-Step Email Classification Environment
A minimal evaluation environment for testing robustness, uncertainty handling, and self-correction in AI agents.
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
Problem
Real-world phishing detection is not binary. Signals are often:
- Incomplete
- Conflicting
- Misleading
Agents must:
- Make an initial classification
- Revise decisions when new signals appear
- Handle uncertainty responsibly
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 decisionflag_uncertain: defer decision
Rewards
| Action Outcome | Reward |
|---|---|
| Correct classification | +0.5 |
| Wrong classification | -0.2 |
| Successful revision | +1.0 |
| Failed revision | -0.3 |
| Uncertainty flag | +0.2 |
Example Run
Initial: Signals: ['urgent_tone']
Step 1: classify → wrong → reward -0.2
Step 2: revise → correct → reward +1.0
Final Score: 1.0
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
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
| Agent | Avg Reward |
|---|---|
| Baseline | -0.04 |
| Uncertainty-Aware | 0.20 |
The Uncertainty-Aware Agent consistently outperforms the baseline by leveraging revision and uncertainty handling strategies.
Performance Visualization
How to Run
Install dependencies:
pip install -r requirements.txt
Run evaluation:
python3 evaluate_agents.py
Run baseline only:
python3 run_baseline.py
Run with Docker
Build the image:
docker build -t robustops .
Run the container:
docker run robustops
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.
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.
Future Work
- Adversarial signal injection
- Longer multi-step reasoning chains
- Real-world dataset integration