Kinfey Lo presents a comprehensive overview of agentic development with Microsoft Agent Framework, detailing how DevUI, AG-UI, and OpenTelemetry accelerate building, debugging, and deploying advanced AI agents in Python and .NET.

Agentic Development with Microsoft Agent Framework: AG-UI, DevUI & OpenTelemetry Deep Dive

Kinfey Lo explores best practices for building intelligent, multi-agent applications in the era of Agentic AI using Microsoft’s Agent Framework. The article introduces a progressive stack—the “Golden Triangle”: DevUI for visual debugging, AG-UI for rapid front-end integration, and OpenTelemetry for performance insights—illustrated via the GHModel.AI sample.

Key Agentic Development Challenges

  • Black-Box Execution: Difficulty in understanding agent decisions and stuck states.
  • Interaction Silos: The friction of productizing agents into demos and stakeholder-facing UIs.
  • Performance Blind Spots: Lacking clarity into performance bottlenecks and token consumption.

Phase 1: Model Creation and Prototyping

  • Utilize GitHub Models for zero-barrier access to GPT-4o, Llama 3, and more—minimizing setup complexity.
  • Switch model providers easily with Agent Framework abstractions in Python or .NET.
  • Example code snippets show agent definitions and workflow setup, emphasizing clean, declarative agent creation.

Phase 2: Debugging with DevUI

  • DevUI provides:
    • Chain-of-thought visualization, making each step of agent reasoning and action transparent.
    • Real-time memory and state inspection, diagnosing context overflow and hallucinations.
  • Available for Python and .NET agents, instantly accessible at local endpoints (http://localhost:8090 / https://localhost:50516/devui).

Phase 3: Demoing and User Interaction via AG-UI

  • AG-UI enables standardized front-end agent protocols—no need for custom React UIs.
  • Supports streaming responses, backend tool rendering, human-in-the-loop actions, and CopilotKit integration.
  • Simple Python and .NET server examples demonstrate AG-UI endpoint registration.

Phase 4: Observability with OpenTelemetry

  • Integrated OpenTelemetry provides distributed tracing and cost monitoring.
  • Enables flame graphs, latency breakdowns, and token metrics essential for transitioning from prototyping to production (e.g., moving to Azure OpenAI).
  • Quick setup examples for Python and .NET; supports visualization tools Aspire Dashboard, Azure Application Insights, Grafana.

Best Practice Architecture

Layer Tool Purpose
Model Layer GitHub Models Rapid AI prototyping
Debug Layer DevUI Visual agent reasoning, fast iteration
Presentation AG-UI/CopilotKit Out-of-box user interfaces
Observability OpenTelemetry Performance transparency, cost control

Resources

This stack ultimately empowers developers to build, debug, and deploy multi-agent applications efficiently, reducing friction at every step of the process.

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