Extended Reasoning Orchestration with Claude Agents SDK and Milvus
Design an enterprise planning architecture using Claude Agents SDK to manage complex document synthesis, inspired by the surge in public interest in classic literature reissues. By utilizing GPT-5.4 Pro as a reasoning engine and Milvus for high-performance retrieval of structured event data, you will build an agent capable of performing multi-step analysis on lengthy texts, ensuring reasoning is persistent and globally indexed across long-horizon projects.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate advanced reasoning agents with Claude Agents SDK and Milvus to analyze and synthesize large-scale document collections.
How work is evaluated
Multi-step reasoning accuracy and retrieval performance.
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.
SynthesisCompleteness
Ensure no key plot points missed
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
RetrievalPrecision
Milvus recall score • target: 0.95 • range: 0.85-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master long-context reasoning using Claude Agents SDK for complex synthesis
Implement persistent memory buffers using Milvus for high-speed retrieval
Orchestrate hybrid reasoning cycles combining GPT-5.4 Pro and Claude Sonnet 4.6.6
Deploy optimized inference pipelines using Novita AI
Build self-correcting agents that verify facts against indexed datasets
Design advanced observability dashboards for multi-model decision traces
Reference links and supporting material
Sample data for 1 tasks
How this agent runs
Multi-step reasoning accuracy and retrieval performance.
Challenge input
Complex document text
Milvus
Vector DB
Zed
High-performance code editor
GPT-5
Policy Serving in the agent workflow.
Evaluated output
Structured synthesis report
- Ensure no key plot points missed
- Milvus recall score • target: 0.95 • range: 0.85-1
- RetrievalPrecision target: 0.95
- 1 public reference case
- Docker execution harness
View technical recipe
Configured tools
- Milvus · Required
- Zed · Optional
- GPT-5 · Optional
Evaluation contract
- SynthesisCompleteness · Weight 1
- RetrievalPrecision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.