Retrieval-augmented generation (RAG) marked a breakthrough for enterprise AI—helping 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.
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—scripts, Robotic Process Automation (RPA) bots, manual handoffs—often struggle with change and scale, leaving teams frustrated by gaps and inefficiencies.
This is where agentic AI emerges as a game-changer. Instead of simply delivering information, agents reason, act, and collaborate—bridging the gap between knowledge and outcomes and enabling a new era of enterprise automation.
While the shift from retrieval to real-world action often begins with agents that can use tools, enterprise needs don’t stop there. Reliable automation requires agents that reflect on their work, plan multi-step processes, collaborate across specialties, and adapt in real time—not just execute single calls.
The five patterns below are foundational building blocks seen in production today. They’re designed to be combined and together unlock transformative automation.
Modern agents stand out by driving real outcomes. Today’s agents interact directly with enterprise systems—retrieving 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.
A diagram of a toolOnce agents can act, the next step is reflection—the ability to assess and improve their own outputs. Reflection lets agents catch errors and iterate for quality without always depending on humans.
A diagram of a reflection patternMost real business processes aren’t single steps—they’re 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.
ContraForce’s 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.
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.
A diagram of a projectNo 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—each focused on different workflow stages—under an orchestrator. This modular design enables agility, scalability, and easy evolution, while keeping responsibilities and governance clear.
A diagram of a multi-agent patternThe 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—taking 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’t clear.
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’s been attempted.
A diagram of a diagramThese patterns are meant to be combined. The most effective agentic solutions weave together tool use, reflection, planning, multi-agent collaboration, and adaptive reasoning—enabling automation that is faster, smarter, safer, and ready for the real world.
Building intelligent agents goes far beyond prompting a language model. When moving from demo to real-world use, teams quickly encounter challenges:
Many teams end up building custom scaffolding—DIY orchestrators, logging, tool managers, and access controls. This slows time-to-value, creates risks, and leads to fragile solutions.
Azure AI Foundry is designed from the ground up for this new era of agentic automation. Azure AI Foundry delivers a single, end-to-end platform that meets the needs of both developers and enterprises, combining rapid innovation with robust, enterprise-grade controls.
With Azure AI Foundry, teams can:
Integrate instantly with enterprise systems: Leverage over 1,400
Azure AI Foundry isn’t just a toolkit—it’s the foundation for orchestrating secure, scalable, and intelligent agents across the modern enterprise.
It’s how organizations move from siloed automation to true, end-to-end business transformation.
Ecosystems, libraries, and foundations to build on. Orchestration frameworks, agent platforms, and development foundations.
