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:

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:

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:

Python Library

from speaker_role_classifier import classify_speakers

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

Ideal for:

AWS Lambda Deployment

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

cdk deploy

This creates:

The serverless deployment demonstrates:

Why This Matters: Business Agility

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

Traditional Approach:

Agentic Approach:

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

Multiple Interfaces

Infrastructure-as-Code

AWS CDK code defines:

This makes deployment reproducible and maintainable.

Configuration Management

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

Serverless Benefits

The AWS Lambda deployment provides:

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:

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?

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:

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:

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