Real-Time AI Operations Controller with Claude Agents SDK
Build an intelligent operations monitoring system designed for real-time decision-making in large-scale tech infrastructure. Using the Claude Agents SDK, you will implement an agent capable of extended reasoning to monitor system health and resolve anomalies. This system will integrate LiveKit for real-time voice-based operational alerts and use Portkey to maintain high-precision evaluation pipelines and observability for agent decisions. You will incorporate Neurolink to manage the persistent state and context of the operational agent, while using Captum to analyze and debug model decision-making patterns during operation. This setup focuses on the high-stakes requirement of corporate infrastructure monitoring, mirroring concerns faced by platforms managing complex domain-blocking or enterprise policy adherence.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an intelligent operations monitoring system designed for real-time decision-making in large-scale tech infrastructure. Using the Claude Agents SDK, you will implement an agent capable of extended reasoning to monitor system health and resolve anomalies. This system will integrate LiveKit for real-time voice-based operational alerts and use Portkey to maintain high-precision evaluation pipelines and observability for agent decisions. You will incorporate Neurolink to manage the persistent state and context of the operational agent, while using Captum to analyze and debug model decision-making patterns during operation. This setup focuses on the high-stakes requirement of corporate infrastructure monitoring, mirroring concerns faced by platforms managing complex domain-blocking or enterprise policy adherence.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Latency Check
Response under 500ms
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Decision Accuracy
Correctness of diagnosis • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement Claude Agents SDK tool-use patterns for system diagnostics
Connect LiveKit sessions to allow for real-time conversational ops review
Configure Portkey to track request latency and agent accuracy metrics
Use Captum to visualize and interpret feature importance in agent decision chains
Manage persistent operational context using Neurolink memory layers
Design advanced reasoning pipelines that trigger when thresholds are exceeded
How this agent runs
Evaluation of agent responsiveness and diagnostic accuracy.
Challenge input
System logs JSON
Neurolink
Multi-provider AI agent framework
LiveKit
Real-time voice and video infrastructure
Portkey
AI gateway & observability
Evaluated output
JSON decision structure
- Response under 500ms
- Correctness of diagnosis • target: 0.95 • range: 0-1
- Decision Accuracy target: 0.95
- 1 public reference case
- Python execution harness
View technical recipe
Configured tools
- Neurolink · Required
- LiveKit · Optional
- Portkey · Optional
- Portkey · Optional
Evaluation contract
- Latency Check · Weight 1
- Decision Accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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