Asha Sharma and Rolf Harms detail how signals loops, fine-tuning, and continuous feedback drive world-class AI apps and agents—exploring practical insights from Dragon Copilot, GitHub Copilot, and Azure AI Foundry.

The Signals Loop: Fine-Tuning for World-Class AI Apps and Agents

Autonomous workflows powered by real-time feedback and continuous learning are rapidly reshaping how organizations build, ship, and improve AI applications. In this article, Asha Sharma and Rolf Harms explore why moving beyond off-the-shelf large language models (LLMs)—and embracing continuous fine-tuning and feedback loops—is key to world-class AI app and agent development.

The Evolution: From Prompt Chaining to Feedback-Driven AI

Early AI applications were often built as thin wrappers on pre-trained foundation models, with retrieval-augmented generation (RAG) techniques enabling quick deployment. However, as use cases have grown more sophisticated, this approach is increasingly insufficient. Accuracy, reliability, and engagement often fall short without deeper architecture and adaptation.

Introducing the Signals Loop

Today’s most adaptable AI systems leverage a ‘signals loop,’ incorporating user and product telemetry in real time to continuously refine models. Rather than static deployments, these systems are dynamic—learning from each interaction, iterating model behavior, and driving compounding improvements over time.

  • Fine-tuning models using proprietary and domain-specific data is essential—not optional. As open-source frontier models and techniques like LoRA and distillation democratize ML, organizations gain new opportunities to personalize and optimize performance.
  • Signals loops allow apps to process real-user feedback, automate benchmarking of new models, and update production systems seamlessly for accuracy and engagement.
  • Memory and context-awareness further enhance personalization and quality, particularly for AI agents operating in sensitive or high-stakes environments.

Case Studies: Dragon Copilot and GitHub Copilot

Dragon Copilot, a healthcare-focused copilot, has operationalized the signals loop by continuously fine-tuning on clinical data and leveraging user feedback. Notable results include:

  • Proprietary model performance exceeding base models by ~50%
  • Continuous refinement with each model generation
  • Improved patient documentation quality and clinician productivity

GitHub Copilot, Microsoft’s flagship developer AI assistant, implements signals loops for rapid feedback and model evolution:

  • Mid-training and post-training environments pull telemetry from over 400,000 public code samples
  • Reinforcement learning on synthetic and real data leads to a 30% improvement in code retention and a 35% increase in completion speed
  • Product, client, and UX improvements align with these model enhancements, allowing Copilot to function as a proactive coding partner

Key Principles for AI Product Teams

  1. Fine-tuning unlocks strategy—organization-specific data outperforms plain, generic models.
  2. Feedback, not just foundational models, creates defensible value—signals-driven systems avoid stagnation.
  3. Iteration speed is critical—aligning data pipelines, evaluation, and development workflows sharpens response to user needs.
  4. Agents demand intentional architecture—combining reasoning, memory, feedback, and orchestration.

Azure AI Foundry: Platform for Continuous Adaptation

Azure AI Foundry offers end-to-end tools for:

  • Broad model choice (open, proprietary, managed, or serverless)
  • High reliability and availability for AI workloads
  • Integrated workflows from data to model to deployment to measurement
  • Cost-effective scaling, from experimentation to production

Key Resources:

Conclusion

The signals loop and accessible fine-tuning with Azure AI Foundry position organizations to build adaptive, resilient AI agents and applications. As the field moves beyond prompt chaining and static models, these techniques ensure that AI investments remain future-proof, delivering compounding improvements through feedback and iteration.

This post appeared first on “The Azure Blog”. Read the entire article here