{"id":45070,"date":"2025-08-13T08:00:00","date_gmt":"2025-08-13T15:00:00","guid":{"rendered":""},"modified":"2026-04-07T12:01:29","modified_gmt":"2026-04-07T19:01:29","slug":"agent-factory-the-new-era-of-agentic-ai-common-use-cases-and-design-patterns","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/agent-factory-the-new-era-of-agentic-ai-common-use-cases-and-design-patterns\/","title":{"rendered":"Agent Factory: The new era of agentic AI\u2014common use cases and design patterns"},"content":{"rendered":"\n

This blog post is the first out of a six-part blog series called Agent Factory<\/a> which will share best practices, design patterns, and tools to help guide you through adopting and building agentic AI.<\/em><\/p>\n\n\n\n

Beyond knowledge: Why enterprises need agentic AI<\/h2>\n\n\n\n

Retrieval-augmented generation (RAG) marked a breakthrough for enterprise AI\u2014helping teams surface insights and answer questions at unprecedented speed. For many, it was a launchpad: copilots and chatbots that streamlined support and reduced the time spent searching for information.<\/p>\n\n\n\n

However, answers alone rarely drive real business impact. Most enterprise workflows demand action: submitting forms, updating records, or orchestrating multi-step processes across diverse systems. Traditional automation tools\u2014scripts, Robotic Process Automation (RPA) bots, manual handoffs\u2014often struggle with change and scale, leaving teams frustrated by gaps and inefficiencies.<\/p>\n\n\n\n

This is where agentic AI emerges as a game-changer. Instead of simply delivering information, agents reason, act, and collaborate\u2014bridging the gap between knowledge and outcomes and enabling a new era of enterprise automation.<\/p>\n\n\n\n

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Build agentic AI with Azure AI Foundry<\/a><\/div>\n<\/div>\n\n\n\n

Patterns of agentic AI: Building blocks for enterprise automation<\/h2>\n\n\n\n

While the shift from retrieval to real-world action often begins with agents that can use tools, enterprise needs don\u2019t stop there. Reliable automation requires agents that reflect on their work, plan multi-step processes, collaborate across specialties, and adapt in real time\u2014not just execute single calls.<\/p>\n\n\n\n

The five patterns below are foundational building blocks seen in production today. They\u2019re designed to be combined and together unlock transformative automation.<\/p>\n\n\n\n

1. Tool use pattern\u2014from advisor to operator<\/h3>\n\n\n\n

Modern agents stand out by driving real outcomes. Today\u2019s agents interact directly with enterprise systems\u2014retrieving data, calling Application Programming Interface (APIs), triggering workflows, and executing transactions. Agents now surface answers and also complete tasks, update records, and orchestrate workflows end-to-end.<\/p>\n\n\n\n

Fujitsu<\/strong><\/a> transformed its sales proposal process using specialized agents for data analysis, market research, and document creation\u2014each invoking specific APIs and tools. Instead of simply answering \u201cwhat should we pitch,\u201d agents built and assembled entire proposal packages, reducing production time by 67%.<\/p>\n\n\n

\"A<\/figure>\n\n\n\n

2. Reflection pattern\u2014self-improvement for reliability<\/h3>\n\n\n\n

Once agents can act, the next step is reflection\u2014the ability to assess and improve their own outputs. Reflection lets agents catch errors and iterate for quality without always depending on humans.<\/p>\n\n\n\n

In high-stakes fields like compliance and finance, a single error can be costly. With self-checks and review loops, agents can auto-correct missing details, double-check calculations, or ensure messages meet standards<\/a>. Even code assistants, like GitHub Copilot<\/a>, rely on internal testing and refinement before sharing outputs. This self-improving loop reduces errors and gives enterprises confidence that AI-driven processes are safe, consistent, and auditable.<\/p>\n\n\n

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3. Planning pattern\u2014decomposing complexity for robustness<\/h3>\n\n\n\n

Most real business processes aren\u2019t single steps\u2014they\u2019re complex journeys with dependencies and branching paths. Planning agents address this by breaking high-level goals into actionable tasks, tracking progress, and adapting as requirements shift.<\/p>\n\n\n\n

ContraForce\u2019s<\/strong> Agentic Security Delivery Platform (ASDP) automated its partner’s security service delivery with security service agents using planning agents that break down incidents into intake, impact assessment, playbook execution, and escalation. As each phase completes, the agent checks for next steps, ensuring nothing gets missed. The result: 80% of incident investigation and response is now automated and full incident investigation can be processed for less than $1 per incident.<\/p>\n\n\n\n

Planning often combines tool use and reflection, showing how these patterns reinforce each other. A key strength is flexibility: plans can be generated dynamically by an LLM or follow a predefined sequence, whichever fits the need. <\/p>\n\n\n

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4. Multi-agent pattern\u2014collaboration at machine speed<\/h3>\n\n\n\n

No single agent can do it all. Enterprises create value through teams of specialists, and the multi-agent pattern mirrors this by connecting networks of specialized agents\u2014each focused on different workflow stages\u2014under an orchestrator. This modular design enables agility, scalability, and easy evolution, while keeping responsibilities and governance clear.<\/p>\n\n\n\n

Modern multi-agent solutions use several orchestration patterns<\/a>\u2014often in combination\u2014to address real enterprise needs. These can be LLM-driven or deterministic: sequential orchestration<\/strong> (such as agents refine a document step by step), concurrent orchestration<\/strong> (agents run in parallel and merge results), group chat\/maker-checker<\/strong> (agents debate and validate outputs together), dynamic handoff<\/strong> (real-time triage or routing), and magentic orchestration<\/strong> (a manager agent coordinates all subtasks until completion).<\/p>\n\n\n\n

JM Family<\/a> adopted this approach with business analyst\/quality assurance (BAQA) Genie, deploying agents for requirements, story writing, coding, documentation, and Quality Assurance (QA). Coordinated by an orchestrator, their development cycles became standardized and automated\u2014cutting requirements and test design from weeks to days and saving up to 60% of QA time.<\/p>\n\n\n

\"A<\/figure>\n\n\n\n

5. ReAct (Reason + Act) pattern\u2014adaptive problem solving in real time<\/h3>\n\n\n\n

The ReAct pattern enables agents to solve problems in real time, especially when static plans fall short. Instead of a fixed script, ReAct agents alternate between reasoning and action\u2014taking a step, observing results, and deciding what to do next. This allows agents to adapt to ambiguity, evolving requirements, and situations where the best path forward isn\u2019t clear.<\/p>\n\n\n\n

For example, in enterprise IT support, a virtual agent powered by the ReAct pattern can diagnose issues in real time: it asks clarifying questions, checks system logs, tests possible solutions, and adjusts its strategy as new information becomes available. If the issue grows more complex or falls outside its scope, the agent can escalate the case to a human specialist with a detailed summary of what\u2019s been attempted.<\/p>\n\n\n

\"A<\/figure>\n\n\n\n

These patterns are meant to be combined. The most effective agentic solutions weave together tool use, reflection, planning, multi-agent collaboration, and adaptive reasoning\u2014enabling automation that is faster, smarter, safer, and ready for the real world.<\/p>\n\n\n\n

Why a unified agent platform is essential<\/h2>\n\n\n\n

Building intelligent agents<\/a> goes far beyond prompting a language model. When moving from demo to real-world use, teams quickly encounter challenges:<\/p>\n\n\n\n