Autonomous Service Dispute Resolver using Claude Agents SDK and Fixie
Urban Company's jump in revenue alongside a shift 'into the red' highlights the critical need for operational efficiency, especially in managing service quality and disputes. This challenge focuses on building an autonomous Service Dispute Resolver using the Claude Agents SDK. You will create an agent that handles customer complaints about home services (e.g., plumbing, cleaning) by analyzing technician logs, customer photos of the 'issue,' and service history. The agent must utilize Claude’s 'extended thinking' (Chain of Thought) to decide whether to issue a refund, schedule a free rework, or deny the claim based on Urban Company's service guidelines. You will use Fixie to build the conversational interface and manage the 'side-channel' tools that allow the agent to look up technician ratings and past customer behavior in real-time.
What you are building
The core problem, expected build, and operating context for this challenge.
Urban Company's jump in revenue alongside a shift 'into the red' highlights the critical need for operational efficiency, especially in managing service quality and disputes. This challenge focuses on building an autonomous Service Dispute Resolver using the Claude Agents SDK. You will create an agent that handles customer complaints about home services (e.g., plumbing, cleaning) by analyzing technician logs, customer photos of the 'issue,' and service history. The agent must utilize Claude’s 'extended thinking' (Chain of Thought) to decide whether to issue a refund, schedule a free rework, or deny the claim based on Urban Company's service guidelines. You will use Fixie to build the conversational interface and manage the 'side-channel' tools that allow the agent to look up technician ratings and past customer behavior in real-time.
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, how much each dimension matters, and which criteria separate a passable run from a strong one.
Policy Adherence
Agent must cite a valid clause from the provided service manual.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Resolution Consistency
Variance in decisions across 5 identical runs. • 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
Master the use of `anthropic.labs.agents` to create stateful dispute-resolution loops
Design Fixie 'sidecars' that fetch real-time technician GPS and activity logs from a simulated ERP system
Implement a policy-enforcement layer where the agent must cite specific clauses from the 'Urban Company Service Agreement' PDF
Configure Claude's computer-use or advanced tool-use to verify timestamps on customer-uploaded photos
Optimize cost-per-resolution by strategically using Claude 3.5 Sonnet for reasoning and Haiku for initial triage
Build an automated feedback loop where resolved disputes update a 'Technician Performance Score' via Fixie actions
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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