Multi-Agent Editorial Integrity Suite with CrewAI
Create a multi-agent team using CrewAI to analyze content originality and publication standards. In light of concerns regarding copied material and restructuring in digital media, this agent team will act as a 'content integrity unit'. Agents will be assigned specific roles: a Researcher, a Fact-Checker, and a Synthesis Expert. They will use Claude Sonnet 4.6.6 to perform deep semantic comparisons and audit content workflows, ensuring that all published work maintains high craftsmanship standards.
What you are building
The core problem, expected build, and operating context for this challenge.
Create a multi-agent team using CrewAI to analyze content originality and publication standards. In light of concerns regarding copied material and restructuring in digital media, this agent team will act as a 'content integrity unit'. Agents will be assigned specific roles: a Researcher, a Fact-Checker, and a Synthesis Expert. They will use Claude Sonnet 4.6.6 to perform deep semantic comparisons and audit content workflows, ensuring that all published work maintains high craftsmanship standards.
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.
ConsistencyCheck
Agent team converges on same audit score
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
AuditPrecision
F1 score on content evaluation • target: 0.85 • 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
Master CrewAI hierarchical role definition for content integrity teams
Integrate Claude Sonnet 4.6.6 for high-fidelity reasoning in editorial analysis
Deploy LangWatch for observability and drift monitoring in content audit workflows
Utilize Upstage SDK for advanced parsing of unstructured news article inputs
Implement Bito AI assistant hooks to facilitate developer and editor interaction with the agent team
Build a structured agent collaboration pattern using /dev/agents patterns
How this agent runs
Evaluation of agent performance in detecting semantic similarity and authorship irregularities
Challenge input
Document raw text
CrewAI
Framework for orchestrating
LangWatch
LLM monitoring and analytics
Evaluated output
Audit report JSON
- Agent team converges on same audit score
- F1 score on content evaluation • target: 0.85 • range: 0-1
- AuditPrecision target: 0.85
- Docker execution harness
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
- LangWatch · Optional
Evaluation contract
- ConsistencyCheck · Weight 1
- AuditPrecision · 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
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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