Azure AI Search’s introduction of agentic retrieval marks a transformative leap in how enterprises and developers approach the challenge of surfacing highly relevant, contextually precise answers within conversational AI systems. As organizations increasingly rely on intelligent agents, voice assistants, and enterprise chatbots, having the ability to deliver nuanced, timely, and accurate information can spell the difference between user delight and frustration. This new agentic retrieval capability, currently in public preview, aims to solve some of the toughest problems in the search space and sets the stage for broader shifts in the agentic web ecosystem.
Traditional retrieval-augmented generation (RAG) systems brought a powerful improvement to natural language processing by marrying large language models with external document search. These systems worked by embedding user queries, searching relevant databases or knowledge stores, and feeding results back to the AI for response generation. While effective in many scenarios, RAG’s limitation became apparent in multi-turn conversational contexts or with more complex queries that demanded nuanced understanding and synthesis.
Akshay Kokane, a Software Engineer at Microsoft, recently highlighted this in a Medium blog post, observing, “Agentic RAG (ARAG) addresses this gap by introducing dynamic reasoning, intelligent tool selection, and iterative refinement.” This shift toward agentic approaches means that the AI isn’t just performing a static search, but is actively decomposing queries, planning search strategies, and iteratively refining results in response to context—behaving less like a question-answering engine and more like a collaborative research assistant.
Matthew Gotteiner, during a Microsoft Build session, clarified a key trade-off: “The overall speed of agentic retrieval is directly related to the number of subqueries generated.” In other words, a more granular breakdown delivers greater accuracy but may come at a cost to latency, depending on the complexity of the original question.
Official documentation, quickstart cookbooks, and detailed integration guides are provided by Microsoft, ensuring frictionless onboarding even for teams that may be new to agent-based architectures. The holistic design of Azure’s API means seamless integration with broader Azure resources—such as authentication, directory management, and logging—remains straightforward, reducing total development effort for large-scale deployments.
Platforms like SSOJet have emerged to address precisely these needs. Offering robust, API-driven authentication tools (e.g., SAML, OIDC, SCIM, and magic link authentication), SSOJet enables organizations to deploy rich conversational assistants while meeting strict regulatory requirements around access control and data privacy. In modern deployments, these integrations are not merely “nice to have”—they are essential for risk management and policy compliance, especially in highly regulated industries like finance, healthcare, or government.
Central to this future is the Model Context Protocol (MCP), an open-source initiative designed to enable agents to share context and collaborate using common standards. According to Scott, “It means that your imagination gets to drive what the agentic web becomes, not just a handful of companies.” This focus on interoperability and democratized innovation, reminiscent of early web protocols, is purpose-built to avoid vendor lock-in and spur a wave of AI agent-driven advancements.
A key technical challenge in this vision is augmenting agent memory. Richer, more persistent memory enables AI to offer contextually relevant answers not just within a session, but across time and even across platforms. However, Scott acknowledged that building such memory “isn’t cheap”—it comes with increased infrastructure costs, data management challenges, and privacy risks.
Azure’s emerging capabilities in structured retrieval augmentation—allowing agents to pull, persist, and cross-reference conversation snippets—are a concrete step toward this more ambitious future. By investing in context-tracking and memory architectures, Microsoft hopes to facilitate the shift from transactional, one-off interactions to ongoing, value-driven partnerships between users and their digital agents.
It is important for potential adopters to weigh the strengths and risks of Azure AI Search agentic retrieval against both in-house solutions and alternative platforms. As always, proof-of-concept deployments, thorough security reviews, and ongoing benchmarking are advised before committing to mission-critical workflows.
The journey ahead will require continued innovation, transparent standards, and responsible oversight. For now, organizations interested in building the future of AI-powered search can leverage Azure’s agentic retrieval to experiment safely, optimize workflows, and prepare for the coming era in which autonomous agents don’t just answer questions, but transform how we interact with knowledge itself.
Source: Security Boulevard Azure AI Search Introduces Agentic Retrieval for Enhanced Relevance
The Evolution of Retrieval-Augmented Generation
Traditional retrieval-augmented generation (RAG) systems brought a powerful improvement to natural language processing by marrying large language models with external document search. These systems worked by embedding user queries, searching relevant databases or knowledge stores, and feeding results back to the AI for response generation. While effective in many scenarios, RAG’s limitation became apparent in multi-turn conversational contexts or with more complex queries that demanded nuanced understanding and synthesis.Akshay Kokane, a Software Engineer at Microsoft, recently highlighted this in a Medium blog post, observing, “Agentic RAG (ARAG) addresses this gap by introducing dynamic reasoning, intelligent tool selection, and iterative refinement.” This shift toward agentic approaches means that the AI isn’t just performing a static search, but is actively decomposing queries, planning search strategies, and iteratively refining results in response to context—behaving less like a question-answering engine and more like a collaborative research assistant.
How Agentic Retrieval Works
At its core, agentic retrieval in Azure AI Search leverages a large language model to analyze the complete chat thread—not just the latest query. This full-context awareness enables the model to extract the most salient information, interpret ambiguous or evolving user requests, and formulate a plan for targeted information gathering.The Agentic Retrieval Pipeline
The process unfolds in several key stages:- Conversation Analysis: The entire chat history is reviewed so the model can extract context, intent, and any references or follow-ups from the user.
- Query Decomposition: Instead of running a single query, the system decomposes the user’s request into several focused subqueries. This is especially valuable for complex or multi-part questions that span different topics or require pulling data from diverse sources.
- Parallel Search Execution: Each subquery runs in parallel across Azure AI Search’s combined text and vector embedding indices. This hybrid approach harnesses both keyword-based and semantic (meaning-oriented) search capabilities for best-in-class coverage.
- Semantic Reranking: Results from all subqueries are reconciled and reranked in real time by a semantic ranker. This produces a unified “grounding payload” containing the top hits, supporting metadata, and additional context for the AI agent.
- Actionable Responses with Traceability: Finally, the system outputs a detailed activity log summarizing which queries were executed, what resources were accessed, and how the response was constructed. This level of transparency is essential for compliance, debugging, and continuous improvement.
A Quantitative Leap in Answer Relevance
In Microsoft’s own benchmarking, this agentic retrieval approach has shown up to a 40% improvement in answer relevance for conversational AI tasks compared to conventional retrieval-augmented generation. While the company’s internal data and methodology have not been independently published at the time of writing, industry experts generally agree that agent-guided searching, with its blend of decomposition and concurrent execution, is positioned to dramatically raise the bar for enterprise and consumer chatbots alike.Matthew Gotteiner, during a Microsoft Build session, clarified a key trade-off: “The overall speed of agentic retrieval is directly related to the number of subqueries generated.” In other words, a more granular breakdown delivers greater accuracy but may come at a cost to latency, depending on the complexity of the original question.
Real-World Impact
For enterprises, this means that knowledge workers interacting with internal assistants—for helpdesk support, research, policy queries, and more—should receive answers that better reflect the nuance of their questions and the history of their requests. This boost in precision and context-awareness translates into significant time savings, fewer clarifying interactions, and an overall improvement in employee productivity.The Architecture Behind the Agent
Azure AI Search’s agentic retrieval process is anchored by a dedicated “Agent” resource. This agent is tightly integrated with an Azure OpenAI model instance, which handles language understanding, strategic planning, and the delegation of subqueries. Developers can tailor this configuration by:- Adjusting the depth and breadth of query decomposition (simpler planners for faster results, more sophisticated planners for higher accuracy),
- Enabling specific indices for semantic or keyword search,
- And configuring security and compliance boundaries for data access.
Integration and Developer Experience
The agentic retrieval feature is currently in public preview across select regions, with Microsoft offering per-token billing for Azure OpenAI query planning and Azure AI Search’s semantic ranking. Both charges are waived during the initial preview period, making it an attractive opportunity for developers and enterprises to experiment with novel conversational experiences without upfront risk.Official documentation, quickstart cookbooks, and detailed integration guides are provided by Microsoft, ensuring frictionless onboarding even for teams that may be new to agent-based architectures. The holistic design of Azure’s API means seamless integration with broader Azure resources—such as authentication, directory management, and logging—remains straightforward, reducing total development effort for large-scale deployments.
Security, Compliance, and the Role of Third-Party Platforms
As conversational agents become more deeply embedded in enterprise workflows, security and compliance considerations move to the forefront. Advanced integration with identity platforms—covering single sign-on (SSO), multi-factor authentication (MFA), and directory synchronization—ensures that only authorized users and systems can trigger, monitor, or influence agentic retrieval workflows.Platforms like SSOJet have emerged to address precisely these needs. Offering robust, API-driven authentication tools (e.g., SAML, OIDC, SCIM, and magic link authentication), SSOJet enables organizations to deploy rich conversational assistants while meeting strict regulatory requirements around access control and data privacy. In modern deployments, these integrations are not merely “nice to have”—they are essential for risk management and policy compliance, especially in highly regulated industries like finance, healthcare, or government.
Microsoft’s Vision for the Agentic Web
Looking beyond current enterprise deployments, Microsoft is positioning itself at the vanguard of a broader “agentic web.” During the 2025 Microsoft Build conference, Kevin Scott, the company’s Chief Technology Officer, detailed a vision in which agents—regardless of their vendor—can seamlessly interact, reason, and learn from each other across company ecosystems.Central to this future is the Model Context Protocol (MCP), an open-source initiative designed to enable agents to share context and collaborate using common standards. According to Scott, “It means that your imagination gets to drive what the agentic web becomes, not just a handful of companies.” This focus on interoperability and democratized innovation, reminiscent of early web protocols, is purpose-built to avoid vendor lock-in and spur a wave of AI agent-driven advancements.
A key technical challenge in this vision is augmenting agent memory. Richer, more persistent memory enables AI to offer contextually relevant answers not just within a session, but across time and even across platforms. However, Scott acknowledged that building such memory “isn’t cheap”—it comes with increased infrastructure costs, data management challenges, and privacy risks.
Azure’s emerging capabilities in structured retrieval augmentation—allowing agents to pull, persist, and cross-reference conversation snippets—are a concrete step toward this more ambitious future. By investing in context-tracking and memory architectures, Microsoft hopes to facilitate the shift from transactional, one-off interactions to ongoing, value-driven partnerships between users and their digital agents.
Strengths, Innovations, and Enterprise Impact
Azure’s agentic retrieval is notable for several reasons:- Massive Context Awareness: By making the entire conversation history part of the input, Azure’s system is much better at resolving ambiguity, following the arc of a multi-turn conversation, and providing holistic answers. This sets a new baseline for what ‘intelligence’ in AI assistants can look like.
- Parallel and Hybrid Search: Concurrent execution across keyword and vector-based semantic indices ensures high relevance, even as queries grow more complex or span multiple domains. The agentic approach minimizes missed information by casting a wider and more strategic net.
- Transparency and Observability: Detailed logging makes it possible for organizations to understand, trust, and refine agentic retrieval outcomes over time—a critical prerequisite for high-stakes enterprise adoption.
- Developer Agility: Powerful REST API and SDK support, together with opt-in billing and free preview access, makes the technology accessible to both experimental startups and large-scale enterprises.
- Secure-by-Design: Integration hooks for advanced security, compliance, and identity management shine in regulated environments where data privacy and access control are non-negotiable.
Risks, Limitations, and Cautions
No technology is without its caveats or potential pitfalls. The agentic retrieval architecture, as innovative as it is, introduces several risk factors that organizations and developers must keep in mind:- Latency and Scalability: While parallelized subqueries speed up search, more complex queries (generating numerous subqueries) can become bottlenecks. This creates a tuning challenge: maximizing relevance without sacrificing response time, especially as use cases scale to thousands or millions of concurrent sessions.
- Opaque Planning: The “intelligent” decomposition and query-planning logic is largely controlled by proprietary language models. Unless the planning process and generated subqueries are surfaced in a developer-readable way, debugging or bias detection can become difficult.
- Data Privacy: Deep context awareness relies on processing and potentially persisting large amounts of conversation history. Organizations must be vigilant about data retention, compliance, and the accidental exposure of sensitive information.
- Cost Management: As agent memory, subquery planning, and semantic reranking grow in sophistication, so do infrastructure and token costs. Microsoft’s initial free preview is attractive, but robust cost tracking and optimization will be essential post-GA.
- Interoperability Hurdles: While the Model Context Protocol (MCP) offers an exciting path to standardization, real-world adoption depends on buy-in from a wide range of industry stakeholders. The fragmentation of standards could hamper the “agentic web” dream if not managed carefully.
Competitive Landscape
Azure’s move into agentic retrieval is part of a broader arms race among cloud hyperscalers and AI platform vendors. Google’s Vertex AI, Amazon’s Bedrock, and open-source initiatives in the retrieval and agent orchestration space all include variations on agentic, context-aware search and reasoning. However, Azure’s tight integration with OpenAI models, ready-made enterprise connectors, and focus on compliance tooling give it a unique edge in regulated industries and global organizations.It is important for potential adopters to weigh the strengths and risks of Azure AI Search agentic retrieval against both in-house solutions and alternative platforms. As always, proof-of-concept deployments, thorough security reviews, and ongoing benchmarking are advised before committing to mission-critical workflows.
Getting Started and Resources
For enterprises and developers eager to experiment with agentic retrieval, Microsoft provides extensive official resources, including:- API and SDK cookbook guides for fast prototyping
- Integration documentation for linking with existing ChatGPT or custom LLM systems
- Security configuration blueprints for federated, identity-managed environments
The Path Forward: Toward an Agentic Future
Azure AI Search’s agentic retrieval is not merely a new feature—it is a harbinger of the next data-driven wave in conversational AI. By combining the strengths of multi-turn memory, strategic reasoning, and advanced search, Microsoft is not only raising expectations for what chatbots and assistants should deliver, but also laying the groundwork for a more open, collaborative, and intelligent “agentic web.”The journey ahead will require continued innovation, transparent standards, and responsible oversight. For now, organizations interested in building the future of AI-powered search can leverage Azure’s agentic retrieval to experiment safely, optimize workflows, and prepare for the coming era in which autonomous agents don’t just answer questions, but transform how we interact with knowledge itself.
Source: Security Boulevard Azure AI Search Introduces Agentic Retrieval for Enhanced Relevance
