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Challenge

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.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a real-time operational monitor using Claude Agents SDK and Neurolink for high-fidelity reasoning and observability.

Delivery guide

How work is evaluated

Evaluation

Evaluation of agent responsiveness and diagnostic accuracy.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Latency Check

Response under 500ms

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Decision Accuracy

Correctness of diagnosis • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

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

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

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

Evaluation checks
  • Response under 500ms
  • Correctness of diagnosis • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Decision Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Neurolink · Required
  • LiveKit · Optional
Observation
  • Portkey · Optional
Policy Serving
  • Portkey · Optional

Evaluation contract

  • Latency Check · Weight 1
  • Decision Accuracy · Weight 1

Metrics

  • Decision Accuracy · Target 0.95

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: System logs JSON

    Output: JSON decision structure

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Real-Time AI Operations Controller with Claude Agents SDK