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Abstract

This case study examines ATLAS performance on Site Reliability Engineering (SRE) tasks, specifically root cause analysis in distributed Kubernetes environments. The system demonstrates systematic debugging improvements, reducing investigation time from 45 minutes to 3 minutes through iterative teaching protocols.

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Problem Formulation

Standard language models exhibit suboptimal performance on systematic debugging tasks due to:
  • Lack of structured investigation patterns - Models default to surface-level diagnostics
  • Insufficient evidence gathering - Premature hypothesis formation without comprehensive data collection
  • No learning transfer - Previous incident resolutions don’t inform future investigations
  • Inefficient exploration - Redundant or irrelevant command sequences increase time-to-resolution

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Experimental Setup

Task: Diagnose service mesh configuration errors causing 503 errors in a Kubernetes cluster Data Source: Scenarios from ITBench, providing reproducible incidents on Kubernetes environments Baseline Model: Standard 8B parameter instruction-tuned LLM ATLAS Configuration: Two-pass adaptive teaching protocol with 50-token diagnostic probe Evaluation Metric: Correct root cause identification within resource constraints

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Results: Progressive Performance Improvement

Student’s Flawed Approach:
kubectl get pods
kubectl describe pod webapp-xxx
kubectl logs webapp-xxx
Result: Surface-level investigation, misses root cause in service mesh configurationPerformance: 23% accuracy

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The Magic: How ATLAS Makes This Possible

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Three-Phase Learning Loop

1

Plan Phase

Student LLM proposes investigation strategy
"I'll check pod status and logs"  # Naive approach
2

Teach Phase

ATLAS teacher reviews and corrects the plan
"First verify service mesh configuration, then check traffic policies"
3

Execute Phase

Student applies corrected strategy and learns
# Executes improved investigation
# Stores as reusable "Skill Capsule"

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Skill Capsules: Compounding Intelligence in Action

Each corrected procedure becomes a Skill Capsule - a reusable investigation pattern that improves future performance:

Service Mesh Debug Capsule

Systematic Istio troubleshooting procedure learned from iterations 1-2

mTLS Conflict Capsule

Policy conflict detection pattern learned from iteration 3

Traffic Flow Capsule

End-to-end validation sequence learned from iteration 4

Root Cause Capsule

Evidence-based diagnosis framework from all iterations

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Real-World Impact

3 min vs 45 min

15x faster incident resolution

Improved Accuracy

Systematic root cause identification

50% Fewer Tokens

More efficient investigation paths

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Implementation Code

Here’s how to implement this SRE enhancement in your environment: 
from transformers import AutoModelForCausalLM, AutoTokenizer
from examples.utils.atlas_inference import ATLASInference

# Load ATLAS teacher trained on SRE procedures
teacher = AutoModelForCausalLM.from_pretrained(
    "Arc-Intelligence/ATLAS-8B-Thinking",
    trust_remote_code=True
)

# Your existing SRE bot or LLM
sre_model = AutoModelForCausalLM.from_pretrained(
    "your-sre-model",
    trust_remote_code=True
)

# Create ATLAS-enhanced SRE investigator
atlas_sre = ATLASInference(
    student_model=sre_model,
    teacher_model=teacher,
    probe_token_limit=50
)

# Incident investigation
incident = "Kubernetes service returning 503 errors"
result = atlas_sre.run_full_protocol(incident)

# Get expert investigation plan
investigation_plan = result["guided_response"]

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Try It Yourself

Run the Demo

Deploy the SRE configuration in your environment

View the Code

Complete implementation on GitHub

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Why This Matters

Traditional approaches to improving LLM performance require:
  • Massive retraining on domain-specific data
  • Expensive fine-tuning for each use case
  • Complex prompt engineering that breaks easily
ATLAS provides:
  • Immediate improvement without retraining
  • Systematic skill acquisition through teaching
  • Reusable knowledge via Skill Capsules
  • Compounding returns as skills build on each other

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Technical Deep Dive

Skill Capsules are learned procedures stored as structured knowledge:
{
  "skill": "service_mesh_debug",
  "trigger": "503 errors in Kubernetes",
  "procedure": [
    "istioctl analyze",
    "check virtualservice configuration",
    "verify destination rules",
    "validate mTLS policies"
  ],
  "success_rate": 0.88
}
These capsules are retrieved and applied when similar problems arise.
ATLAS uses a two-pass protocol:
  1. Diagnostic Probe (~50 tokens): Assess current capability
  2. Targeted Correction (~200 tokens): Provide precise guidance
This minimal intervention approach ensures efficiency while maximizing improvement.
The 165% gain in 2 hours happens through:
  1. Reflective Mutation: Automatic reward engineering
  2. Policy Gradient Updates: Continuous improvement
  3. Skill Consolidation: Converting lessons into reusable patterns
Total cost: ~$10 in API calls

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Next Steps

Deploy in Production

Scale this approach across your SRE team

Custom Training

Train ATLAS on your specific procedures

More Examples

Explore other use cases