Grievance Resolution Agent

About the Project

Inspiration

Consumer complaints are often more complicated than simply submitting a form and waiting for a response. A complaint may need to be understood, categorized, researched, prioritized, and followed up before it can be resolved. We wanted to explore whether an AI agent could take responsibility for this workflow instead of requiring a human to perform every step manually.

This led us to build Grievance Resolution Agent, an AI-powered agent designed to help handle consumer complaints from initial submission toward resolution while keeping humans involved when judgment or approval is required.

What the Agent Does

The agent accepts a consumer complaint and works through the resolution process step by step. It can:

Understand and summarize the complaint.
Identify the relevant complaint category and important details.
Determine the urgency and recommend a priority.
Search a configured knowledge base for relevant policies, procedures, and resolution information.
Identify potentially related complaints or recurring issues.
Decide what actions should be taken next based on the available information.
Draft an appropriate response for the consumer.
Prepare or trigger workflow actions such as assigning the case or requesting additional information.
Monitor the case and identify situations that require escalation.
Ask a human officer for approval when an action requires human judgment.

Rather than functioning as a simple chatbot, the agent is designed to reason through a task, use tools, maintain context, and take multiple actions toward an outcome.

How We Built It

The project is built around the Strands Agents SDK. The agent is given a set of specialized tools that allow it to interact with the grievance-management environment rather than only generating text.

The workflow combines an AI reasoning layer with structured application services. Complaint information is maintained in a database, while the agent can invoke tools for operations such as retrieving complaint information, searching relevant knowledge, analyzing related cases, updating workflow information, and preparing responses.

A typical workflow is:

Complaint → Understand → Analyze → Research → Decide → Act → Verify → Escalate or Resolve

The agent does not blindly execute every decision. Actions that can have significant consequences are designed to remain subject to human review.

Handling AI Errors

A major design consideration was that an AI system can make incorrect classifications, recommendations, or decisions. We therefore treat AI outputs as recommendations rather than unquestionable truth.

The system can use confidence or validation checks and route uncertain cases to a human officer. Human officers can review, correct, or override the agent's recommendations before important actions are finalized.

This human-in-the-loop approach allows the agent to automate repetitive work while keeping accountability with the responsible human.

Challenges

One of the main challenges was moving from a conventional application workflow to a genuinely agentic workflow. A normal application follows predetermined steps, whereas an agent needs to determine which information it needs, which tools to use, and what action should come next.

Other challenges included designing reliable tool interactions, maintaining structured complaint state, preventing incorrect automated actions, handling ambiguous complaint descriptions, and deciding when the agent should stop and request human intervention.

What We Learned

Building this project helped us understand the difference between adding an LLM to an application and building an actual AI agent. We learned how tool use, structured state, reasoning, workflow control, and human oversight can work together to allow an AI system to perform meaningful multi-step tasks.

Our goal is not to replace consumer grievance officers. Instead, the goal is to give them an agent that can take care of the repetitive information-processing and coordination work, allowing humans to focus on cases that genuinely require their judgment.