As an alternative of merely delivering info, brokers motive, act, and collaborate—bridging the hole between information and outcomes. Learn extra about agentic AI in Azure AI Foundry.
This weblog put up is the primary out of a six-part weblog collection known as Agent Manufacturing facility which is able to share greatest practices, design patterns, and instruments to assist information you thru adopting and constructing agentic AI.
Past information: Why enterprises want agentic AI
Retrieval-augmented era (RAG) marked a breakthrough for enterprise AI—serving to groups floor insights and reply questions at unprecedented velocity. For a lot of, it was a launchpad: copilots and chatbots that streamlined help and lowered the time spent trying to find info.
Nevertheless, solutions alone not often drive actual enterprise influence. Most enterprise workflows demand motion: submitting varieties, updating data, or orchestrating multi-step processes throughout various methods. Conventional automation instruments—scripts, Robotic Course of Automation (RPA) bots, guide handoffs—usually wrestle with change and scale, leaving groups annoyed by gaps and inefficiencies.
That is the place agentic AI emerges as a game-changer. As an alternative of merely delivering info, brokers motive, act, and collaborate—bridging the hole between information and outcomes and enabling a brand new period of enterprise automation.
Patterns of agentic AI: Constructing blocks for enterprise automation
Whereas the shift from retrieval to real-world motion usually begins with brokers that may use instruments, enterprise wants don’t cease there. Dependable automation requires brokers that mirror on their work, plan multi-step processes, collaborate throughout specialties, and adapt in actual time—not simply execute single calls.
The 5 patterns under are foundational constructing blocks seen in manufacturing in the present day. They’re designed to be mixed and collectively unlock transformative automation.
1. Instrument use sample—from advisor to operator
Trendy brokers stand out by driving actual outcomes. As we speak’s brokers work together straight with enterprise methods—retrieving information, calling Utility Programming Interface (APIs), triggering workflows, and executing transactions. Brokers now floor solutions and in addition full duties, replace data, and orchestrate workflows end-to-end.
Fujitsu remodeled its gross sales proposal course of utilizing specialised brokers for information evaluation, market analysis, and doc creation—every invoking particular APIs and instruments. As an alternative of merely answering “what ought to we pitch,” brokers constructed and assembled total proposal packages, decreasing manufacturing time by 67%.
2. Reflection sample—self-improvement for reliability
As soon as brokers can act, the subsequent step is reflection—the flexibility to evaluate and enhance their very own outputs. Reflection lets brokers catch errors and iterate for high quality with out all the time relying on people.
In high-stakes fields like compliance and finance, a single error might be pricey. With self-checks and evaluate loops, brokers can auto-correct lacking particulars, double-check calculations, or guarantee messages meet requirements. Even code assistants, like GitHub Copilot, depend on inside testing and refinement earlier than sharing outputs. This self-improving loop reduces errors and offers enterprises confidence that AI-driven processes are secure, constant, and auditable.
3. Planning sample—decomposing complexity for robustness
Most actual enterprise processes aren’t single steps—they’re advanced journeys with dependencies and branching paths. Planning brokers handle this by breaking high-level targets into actionable duties, monitoring progress, and adapting as necessities shift.
ContraForce’s Agentic Safety Supply Platform (ASDP) automated its companion’s safety service supply with safety service brokers utilizing planning brokers that break down incidents into consumption, influence evaluation, playbook execution, and escalation. As every part completes, the agent checks for subsequent steps, making certain nothing will get missed. The outcome: 80% of incident investigation and response is now automated and full incident investigation might be processed for lower than $1 per incident.
Planning usually combines software use and reflection, exhibiting how these patterns reinforce one another. A key power is flexibility: plans might be generated dynamically by an LLM or comply with a predefined sequence, whichever matches the necessity.
4. Multi-agent sample—collaboration at machine velocity
No single agent can do all of it. Enterprises create worth via groups of specialists, and the multi-agent sample mirrors this by connecting networks of specialised brokers—every targeted on completely different workflow phases—underneath an orchestrator. This modular design permits agility, scalability, and straightforward evolution, whereas protecting duties and governance clear.
Trendy multi-agent options use a number of orchestration patterns—usually together—to handle actual enterprise wants. These might be LLM-driven or deterministic: sequential orchestration (comparable to brokers refine a doc step-by-step), concurrent orchestration (brokers run in parallel and merge outcomes), group chat/maker-checker (brokers debate and validate outputs collectively), dynamic handoff (real-time triage or routing), and magentic orchestration (a supervisor agent coordinates all subtasks till completion).
JM Household adopted this strategy with enterprise analyst/high quality assurance (BAQA) Genie, deploying brokers for necessities, story writing, coding, documentation, and High quality Assurance (QA). Coordinated by an orchestrator, their growth cycles grew to become standardized and automatic—reducing necessities and check design from weeks to days and saving as much as 60% of QA time.
5. ReAct (Motive + Act) sample—adaptive downside fixing in actual time
The ReAct sample permits brokers to unravel issues in actual time, particularly when static plans fall brief. As an alternative of a set script, ReAct brokers alternate between reasoning and motion—taking a step, observing outcomes, and deciding what to do subsequent. This enables brokers to adapt to ambiguity, evolving necessities, and conditions the place one of the best path ahead isn’t clear.
For instance, in enterprise IT help, a digital agent powered by the ReAct sample can diagnose points in actual time: it asks clarifying questions, checks system logs, assessments attainable options, and adjusts its technique as new info turns into accessible. If the problem grows extra advanced or falls outdoors its scope, the agent can escalate the case to a human specialist with an in depth abstract of what’s been tried.
These patterns are supposed to be mixed. The simplest agentic options weave collectively software use, reflection, planning, multi-agent collaboration, and adaptive reasoning—enabling automation that’s sooner, smarter, safer, and prepared for the true world.
Why a unified agent platform is important
Constructing clever brokers goes far past prompting a language mannequin. When transferring from demo to real-world use, groups rapidly encounter challenges:
- How do I chain a number of steps collectively reliably?
- How do I give brokers entry to enterprise information—securely and responsibly?
- How do I monitor, consider, and enhance agent conduct?
- How do I guarantee safety and identification throughout completely different agent parts?
- How do I scale from a single agent to a workforce of brokers—or connect with others?
Many groups find yourself constructing customized scaffolding—DIY orchestrators, logging, software managers, and entry controls. This slows time-to-value, creates dangers, and results in fragile options.
That is the place Azure AI Foundry is available in—not simply as a set of instruments, however as a cohesive platform designed to take brokers from concept to enterprise-grade implementation.
Azure AI Foundry: Unified, scalable, and constructed for the true world
Azure AI Foundry is designed from the bottom up for this new period of agentic automation. Azure AI Foundry delivers a single, end-to-end platform that meets the wants of each builders and enterprises, combining fast innovation with strong, enterprise-grade controls.
With Azure AI Foundry, groups can:
- Prototype domestically, deploy at scale: Develop and check brokers domestically, then seamlessly transfer to cloud runtime—no rewrites wanted. Take a look at learn how to get began with Azure AI Foundry SDK.
- Versatile mannequin alternative: Select from Azure OpenAI, xAI Grok, Mistral, Meta, and over 10,000 open-source fashions—all by way of a unified API. A Mannequin Router and Leaderboard assist choose the optimum mannequin, balancing efficiency, price, and specialization. Take a look at the Azure AI Foundry Fashions catalog.
- Compose modular multi-agent architectures: Join specialised brokers and workflows, reusing patterns throughout groups. Take a look at learn how to use linked brokers in Azure AI Foundry Agent Service.
- Combine immediately with enterprise methods: Leverage over 1,400+ built-in connectors for SharePoint, Bing, SaaS, and enterprise apps, with native safety and coverage help. Take a look at what are instruments in Azure AI Foundry Agent Service.
- Allow openness and interoperability: Constructed-in help for open protocols like Agent-to-Agent (A2A) and Mannequin Context Protocol (MCP) lets your brokers work throughout clouds, platforms, and companion ecosystems. Take a look at how to connect with a Mannequin Context Protocol Server Endpoint in Azure AI Foundry Agent Service.
- Enterprise-grade safety: Each agent will get a managed Entra Agent ID, strong Function-based Entry Management (RBAC), On Behalf Of authentication, and coverage enforcement—making certain solely the suitable brokers entry the suitable sources. Take a look at learn how to use a digital community with the Azure AI Foundry Agent Service.
- Complete observability: Achieve deep visibility with step-level tracing, automated analysis, and Azure Monitor integration—supporting compliance and steady enchancment at scale. Take a look at learn how to monitor Azure AI Foundry Agent Service.
Azure AI Foundry isn’t only a toolkit—it’s the inspiration for orchestrating safe, scalable, and clever brokers throughout the fashionable enterprise.
It’s how organizations transfer from siloed automation to true, end-to-end enterprise transformation.
Keep tuned: In upcoming posts in our Agent Manufacturing facility weblog collection, we’ll present you learn how to deliver these pillars to life—demonstrating learn how to construct safe, orchestrated, and interoperable brokers with Azure AI Foundry, from native growth to enterprise deployment.
