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# Azure AI Foundry for Developers: Open Tools, Scalable Agents, Real Impact

Stop building rigid AI apps that will be obsolete in two years. Learn how to orchestrate flexible AI agents like microservices using Azure AI Foundry.

- **Speakers:** [Oliver Will](https://www.wearedevelopers.com/@oliver-will)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 23:33
- **URL:** https://www.wearedevelopers.com/videos/1541-azure-ai-foundry-for-developers-open-tools-scalable-agents-real-impact

## Summary

The video explores the evolution of AI development from isolated chatbots to flexible, multi-agent systems, framing Azure AI Foundry and Semantic Kernel as essential tools for enterprise orchestration. Acknowledging that generative AI technology moves too rapidly to rigidly future-proof, the speaker advises that when developers ask how to build something that won't be obsolete in two years, the realization is that "it will be outdated, and we have to think about how to build something that is flexible enough to handle that." To achieve this agility, the speaker advocates for a modular approach where AI agents operate similarly to microservices. This domain-driven structure allows developers to seamlessly swap or upgrade discrete agents—whether built with Azure, OpenAI, or other platforms—without breaking the overarching architecture.

A crucial theme is the shift from purely technical implementation to driving user-centric business value. By decoupling back-end AI components, organizations can empower both developers and non-technical business users to create, reuse, and orchestrate models across varied tools like Copilot Studio. However, true scalability relies on more than just the foundational models; it demands a robust underlying data infrastructure. Integrating optimal storage platforms—ranging from Azure AI Search for complex vector queries to Postgres for cost-efficiency or Redis for low-latency caching—is cited as a non-negotiable prerequisite, as AI projects consistently fail without a solid data backbone.

To bridge the gap between rapid product iteration and enterprise reliability, integrating automated AI evaluation directly into DevOps pipelines is highly recommended. As teams experiment with newer or cheaper models, automated CI/CD checks can continuously measure critical KPIs in pull requests to ensure that accuracy, factual coherence, and native content safety remain uncompromised. Ultimately, prioritizing flexible code structuring, rigorous automated testing, and comprehensive observability within Azure AI Foundry empowers engineering teams to build resilient, production-ready AI applications equipped to adapt rapidly to industry changes.

**Keywords:** azure ai foundry, semantic kernel, multi-agent orchestration, ai microservices architecture, automated llm evaluation, devops ai pipelines, azure ai search, postgres vector database, copilot studio integration, content safety moderation, ai data infrastructure, llm performance testing, enterprise ai governance

## Chapters

1. **Securing enterprise data with custom AI deployments** (01:09) — Customer privacy concerns with public intelligence models drove the creation of secure corporate environments.
1. **Balancing technology choices with maximum user impact** (02:32) — Rapid changes in intelligence frameworks require building flexible architectures instead of rigidly static code.
1. **Shifting from static architectures to agentic microservices** (05:02) — Modular intelligent agents provide independent functionality that is easier to maintain than rigid backends.
1. **Utilizing enterprise frameworks for safe model development** (07:41) — Development platforms supply necessary content safety and framework tools to securely construct diverse models.
1. **Orchestrating multi-agent systems across different interoperable SDKs** (10:41) — Combining integration frameworks allows seamless interoperability between distinct conversational agents and structured data tools.
1. **Selecting appropriate data resources for AI performance** (16:05) — Choosing the right foundational storage solution optimizes processing speed and execution costs for conversational services.
1. **Automating large language model evaluations in DevOps** (17:26) — Integrating continuous quality monitoring into deployment pipelines ensures reliable and cost-effective system outputs.
1. **Establishing solid data platforms and governance practices** (22:15) — A successful agent deployment depends directly on organized foundational data infrastructure and built-in observability capabilities.

## Related Moments

- [Managing AI development with Azure AI Foundry](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) (from "Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure")
- [Scaling operations using Azure AI Foundry tools](https://www.wearedevelopers.com/videos/1535-from-traction-to-production-maturing-your-genaiops-step-by-step) (from "From Traction to Production: Maturing your GenAIOps step by step")
- [Platform engineering as the foundation for scaling AI tools](https://www.wearedevelopers.com/videos/100266-ai-won-t-fix-your-engineering-culture) (from "AI Won't Fix Your Engineering Culture")
- [Introduction to building reliable AI agents in production](https://www.wearedevelopers.com/videos/1523-the-ai-agent-path-to-prod-building-for-reliability) (from "The AI Agent Path to Prod: Building for Reliability")
- [Resolving developer challenges in AI agent implementation](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) (from "Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure")
- [Fusing developer experience and platform engineering for agentic SDLC](https://www.wearedevelopers.com/videos/100266-ai-won-t-fix-your-engineering-culture) (from "AI Won't Fix Your Engineering Culture")

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