The Microsoft Agent Framework is an open-source toolkit for building, orchestrating, and deploying AI agents and multi-agent workflows in Python and .NET. It enables everything from simple chatbots to complex multi-agent systems using graph-based orchestration, streaming data flows, and human-in-the-loop interactions. The framework includes experimental labs (for benchmarking and reinforcement learning), a DevUI for interactive debugging, and migration paths from Semantic Kernel and AutoGen.
Key Takeaways:
Supports Python and .NET, bridging enterprise and open-source ecosystems.
Graph-based workflows enable modular, multi-agent orchestration with advanced features (streaming, checkpointing, time-travel).
Includes DevUI for agent development, testing, and debugging.
AF Labs provides cutting-edge experimental features (RL, benchmarking, research tools).
Designed to scale from single agents to enterprise-grade multi-agent systems.
Provides migration support for teams moving from Semantic Kernel or AutoGen.
AI Versus the Archives : Real Document Intelligence
At Enterprise Search & Discovery 2025, Michael Cizmar (CEO, MC+A) will present AI Versus the Archives: What the Kennedy Files Taught Us About Document Intelligence. This session reveals how generative AI and document intelligence pipelines applied to the JFK assassination files uncovered patterns, inconsistencies, and connections hidden for decades. More importantly, it highlights what these methods mean for organizations tackling legal discovery, compliance, and investigations today.
Key Takeaways:
AI reveals what humans miss: uncover connections and inconsistencies in massive, unstructured archives.
Real-world pipelines: lessons in handling noisy, incomplete data with transparency and trust.
Direct impact: methods extend beyond archives into discovery, compliance, and enterprise content analysis.
Actionable insight: document intelligence is not academic — it’s a competitive advantage.
Elastic has released Relevance Studio, an open-source toolkit for managing the lifecycle of search relevance engineering in Elasticsearch. Designed for the era of generative AI, it helps developers and search teams fine-tune lexical, semantic, and vector search strategies — ensuring users get the right answer faster.
Key Takeaways:
Purpose-built for AI-driven search: bridges the gap between GenAI expectations and search infrastructure.
Supports all retrieval modes: vector (kNN), semantic (ELSER), and lexical (BM25, synonyms, text analysis).
Lifecycle management: gives teams a structured way to design, test, and optimize search relevance.
Battle-tested foundation: built on Elasticsearch, ensuring scale and flexibility for enterprise workloads.
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