Speaker Role Classifier

Agility Through Agentic Programming

We recently faced a real-world problem: classifying speaker roles in conversation transcripts. The challenge wasn't just the classification itself—it was the ambiguity in how speaker roles might be named and the variety of scenarios we'd need to handle.

In traditional programming, this would require:

• Extensive scenario mapping and edge case handling
• Complex branching logic for different naming conventions
• Ongoing maintenance as new scenarios emerged
• Significant development time before delivering value

Instead, we applied the "give an agent a tool" paradigm. By giving an AI agent access to the transcript and letting it figure out the speaker roles, we delivered a working solution quickly and moved on. This is the power of agentic programming: flexibility and speed.

The Problem: Ambiguous Speaker Roles

When you have a conversation transcript where speakers are identified but not labeled, you need to determine their roles. A typical transcript might look like:

Speaker 0: Thank you for calling. How can I help you today?
Speaker 1: Hi, I'm having trouble with my account.
Speaker 0: I'd be happy to help with that. Can you provide your account number?

The challenge: speaker roles could be named anything—"Agent", "Representative", "Support", "Customer", "Caller", "Client"—and the number of speakers could vary. Building explicit logic to handle all these scenarios would be time-consuming and brittle.

With an AI agent, you just describe what you need, and it figures out the rest.

The Solution: Give an Agent a Tool

The Speaker Role Classifier gives an AI agent access to the transcript and asks it to classify speaker roles. The agent analyzes:

• Conversational patterns: Who initiates, who responds, who asks questions
• Language style: Professional vs. casual language
• Turn-taking: The flow of conversation reveals relationships
• Context clues: References that indicate role

The agent figures out the roles and returns labeled results:

{
  "speaker_0": "Agent",
  "speaker_1": "Customer"
}

No complex branching logic. No exhaustive scenario mapping. Just give the agent the tool (access to the transcript) and let it solve the problem.

Multiple Deployment Options

Command-Line Interface

speaker-role-classifier transcript.json

Perfect for:

• Batch processing of transcripts
• Integration into data pipelines
• Local development and testing
• Quick validation of results

Python Library

from speaker_role_classifier import classify_speakers

transcript = load_transcript("call.json")
roles = classify_speakers(transcript)
print(roles)

Ideal for:

• Integration into Python applications
• Data science workflows
• Custom processing pipelines
• Jupyter notebook analysis

AWS Lambda Deployment

The project includes complete AWS CDK infrastructure code for serverless deployment:

cdk deploy

This creates:

• Lambda function with proper IAM roles
• API Gateway endpoint for HTTP access
• CloudWatch logging and monitoring
• Scalable, pay-per-use architecture

The serverless deployment demonstrates:

• Production-ready infrastructure: Following AWS Well-Architected principles
• Infrastructure-as-code: Reproducible, version-controlled deployments
• Serverless architecture: Automatic scaling without server management
• Enterprise patterns: Proper security, logging, and monitoring

Why This Matters: Business Agility

This project demonstrates a fundamental shift in how we build software:

Traditional Approach:

• Weeks analyzing all possible scenarios
• Complex code with extensive branching logic
• Ongoing maintenance as new edge cases emerge
• Slow time-to-value

Agentic Approach:

• Give the agent the right tools
• Let it figure out the scenarios
• Deliver working solution quickly
• Move on to the next problem

This isn't just about speaker classification—it's about development speed and business agility. When you can solve problems in days instead of weeks, you can respond to business needs faster and deliver more value.

Technical Architecture

The tool demonstrates several best practices:

Modular Design

• Core classification logic separated from deployment concerns
• Easy to test and validate independently
• Reusable across different deployment targets

Multiple Interfaces

• CLI for command-line usage
• Library API for programmatic access
• Lambda handler for serverless deployment
• Each interface wraps the same core logic

Infrastructure-as-Code

AWS CDK code defines:

• Lambda function configuration
• IAM roles and permissions
• API Gateway setup
• CloudWatch log groups

This makes deployment reproducible and maintainable.

Configuration Management

Environment variables and configuration files separate deployment-specific settings from code, enabling:

• Different configurations for dev/staging/production
• Easy model version updates
• Flexible API key management

Serverless Benefits

The AWS Lambda deployment provides:

• Automatic scaling: Handle one call or one million
• Pay-per-use pricing: Only pay for actual processing time
• Zero server management: No infrastructure to maintain
• High availability: Built-in redundancy and failover
• Global deployment: Deploy to multiple regions easily

This is intelligent automation with enterprise-grade operational maturity.

The Paradigm Shift

This tool emerged from a real-world need we had to solve quickly. Rather than spending weeks building a traditional solution with complex logic, we applied the agentic programming paradigm and delivered a working solution in a fraction of the time.

This represents a fundamental shift in software development:

• From explicit programming to delegation: Tell the agent what you need, not how to do it
• From scenario mapping to flexible reasoning: Agents adapt to new scenarios automatically
• From maintenance burden to resilience: Agents handle edge cases without code changes
• From slow iteration to rapid delivery: Ship solutions in days, not weeks

For more on this paradigm, see our article on giving an agent a tool.

Production-Ready Thinking

What makes this a production-ready tool rather than just a demo?

• Multiple deployment options: Choose what fits your architecture
• Infrastructure-as-code: Reproducible deployments
• Error handling: Graceful handling of edge cases
• Logging and monitoring: CloudWatch integration for observability
• Configuration management: Separate code from configuration
• Documentation: Clear usage examples and API documentation

These aren't just nice-to-haves—they're essential for systems that run in production environments.

Open Source

The complete project is available on GitHub, including:

• CLI and library code
• AWS CDK infrastructure definitions
• Example transcripts and usage patterns
• Deployment documentation

We built this to solve a real problem in our call center QA work, and we're sharing it so others can solve similar problems without starting from scratch.

Agentic Programming in Practice

Speaker Role Classifier exemplifies the agentic programming paradigm:

• Solve real problems quickly: Built and deployed in a fraction of traditional development time
• Flexible by design: Handles scenarios we didn't explicitly program for
• Production-ready: Not just a demo, but a deployable system with CLI, library, and serverless options
• Infrastructure-as-code: Reproducible, maintainable deployments with AWS CDK
• Open source: Share the approach so others can move fast too

This is how agentic programming enables business agility—by letting you focus on problems, not implementation details.