ResolveLoop AI — From Customer Problems to Resolved Cases

Inspiration

Small businesses often handle customer support across order management systems, shipping updates, store policies, emails, and task lists. When a customer reports a delayed order, a support representative may need to check several sources, determine the correct response, draft an email, obtain approval, and remember to follow up.

We wanted to explore a better approach: What if an AI agent could handle the entire support workflow instead of simply answering questions?

That idea inspired ResolveLoop AI, an AI-powered customer support agent designed around the principle that a support case is not complete until the next steps have been carried out and verified.

What It Does

ResolveLoop AI helps support teams investigate customer issues, make evidence-based recommendations, and manage the resolution process.

For example, when a customer asks about a delayed order, the agent can:

Investigate: Retrieve order details and review shipping events.
Understand: Check store policies and identify the options supported by the available evidence.
Recommend: Explain the issue and propose an appropriate next step.
Prepare: Draft a customer email based on the verified information.
Request approval: Pause for human review before consequential actions.
Follow through: Create a follow-up task, track the case, and verify the recorded outcome.

Rather than treating AI as a conversational interface alone, ResolveLoop AI is designed to connect reasoning, tool use, human oversight, and persistent case management in one workflow.

How We Plan to Build It

The project uses a React and TypeScript frontend with a Python backend powered by FastAPI. The backend manages the workflow, validates proposed actions, exposes tools to the agent, and persists case information in a database.

The planned architecture separates the AI provider from the core business logic. This allows us to develop and test the workflow using a deterministic mock provider, with an optional compatible AI service when available.

The application is designed around an end-to-end order-support scenario using sample data. Its core components include order lookup, shipping information, policy checks, response drafting, approval handling, follow-up management, and an audit trail.

We are also exploring how an Alexa+-style agent experience and tool integration can make the workflow more natural and useful.

What Makes It Different

Many AI support tools focus on generating answers. ResolveLoop AI focuses on closing the loop.

A recommendation should be grounded in available evidence. Important actions should require appropriate approval. Results should be recorded, and the agent should check whether the intended outcome was achieved.

This approach aims to make AI assistance more transparent, accountable, and useful for real support operations.

Challenges We Are Tackling

The main challenges include coordinating multiple tools in a reliable sequence, preventing the AI from inventing order or policy information, handling actions safely, preserving state across sessions, and making the complete workflow easy to understand.

We are addressing these challenges by separating AI reasoning from deterministic business rules, validating actions on the backend, recording workflow events, and designing explicit approval and verification steps.

Another challenge is creating a useful agent experience without making the application unnecessarily complicated. Our focus is one complete, demonstrable customer-support journey rather than a large collection of disconnected features.

What We Hope to Learn

Through this project, we aim to learn how agentic AI, tool integration, and human-in-the-loop controls can work together in a practical application. We also want to explore how an Alexa+-style experience can help users delegate multi-step tasks while retaining control over important decisions.