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Instead of merely delivering data, brokers cause, act, and collaborate—bridging the hole between data and outcomes. Read extra about agentic AI in Azure AI Foundry.
This weblog submit is the primary out of a six-part weblog sequence known as Agent Factory which is able to share finest practices, design patterns, and instruments to assist information you thru adopting and constructing agentic AI.
Beyond data: Why enterprises want agentic AI
Retrieval-augmented technology (RAG) marked a breakthrough for enterprise AI—serving to groups floor insights and reply questions at unprecedented pace. For many, it was a launchpad: copilots and chatbots that streamlined help and lowered the time spent looking for data.
However, solutions alone not often drive actual enterprise affect. Most enterprise workflows demand motion: submitting types, updating data, or orchestrating multi-step processes throughout numerous programs. Traditional automation instruments—scripts, Robotic Process Automation (RPA) bots, handbook handoffs—typically battle with change and scale, leaving groups pissed off by gaps and inefficiencies.
This is the place agentic AI emerges as a game-changer. Instead of merely delivering data, brokers cause, act, and collaborate—bridging the hole between data and outcomes and enabling a brand new period of enterprise automation.
Patterns of agentic AI: Building blocks for enterprise automation
While the shift from retrieval to real-world motion typically begins with brokers that may use instruments, enterprise wants don’t cease there. Reliable automation requires brokers that replicate on their work, plan multi-step processes, collaborate throughout specialties, and adapt in actual time—not simply execute single calls.
The 5 patterns beneath are foundational constructing blocks seen in manufacturing right now. They’re designed to be mixed and collectively unlock transformative automation.
1. Tool use sample—from advisor to operator
Modern brokers stand out by driving actual outcomes. Today’s brokers work together instantly with enterprise programs—retrieving information, calling Application Programming Interface (APIs), triggering workflows, and executing transactions. Agents now floor solutions and in addition full duties, replace data, and orchestrate workflows end-to-end.
Fujitsu reworked its gross sales proposal course of utilizing specialised brokers for information evaluation, market analysis, and doc creation—every invoking particular APIs and instruments. Instead of merely answering “what should we pitch,” brokers constructed and assembled total proposal packages, lowering manufacturing time by 67%.
2. Reflection sample—self-improvement for reliability
Once brokers can act, the following step is reflection—the power 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 could be expensive. With self-checks and evaluation loops, brokers can auto-correct lacking particulars, double-check calculations, or guarantee messages meet requirements. Even code assistants, like GitHub Copilot, depend on inner 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 Security Delivery Platform (ASDP) automated its companion’s safety service supply with safety service brokers utilizing planning brokers that break down incidents into consumption, affect evaluation, playbook execution, and escalation. As every section completes, the agent checks for subsequent steps, making certain nothing will get missed. The consequence: 80% of incident investigation and response is now automated and full incident investigation could be processed for lower than $1 per incident.
Planning typically combines software use and reflection, displaying how these patterns reinforce one another. A key power is flexibility: plans could be generated dynamically by an LLM or comply with a predefined sequence, whichever suits the necessity.
4. Multi-agent sample—collaboration at machine pace
No single agent can do all of it. Enterprises create worth by means of groups of specialists, and the multi-agent sample mirrors this by connecting networks of specialised brokers—every centered on completely different workflow levels—beneath an orchestrator. This modular design allows agility, scalability, and straightforward evolution, whereas retaining obligations and governance clear.
Modern multi-agent options use a number of orchestration patterns—typically together—to deal with actual enterprise wants. These could be LLM-driven or deterministic: sequential orchestration (corresponding 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 Family adopted this strategy with enterprise analyst/high quality assurance (BAQA) Genie, deploying brokers for necessities, story writing, coding, documentation, and Quality Assurance (QA). Coordinated by an orchestrator, their growth cycles grew to become standardized and automatic—chopping necessities and take a look at design from weeks to days and saving as much as 60% of QA time.
5. ReAct (Reason + Act) sample—adaptive downside fixing in actual time
The ReAct sample allows brokers to resolve issues in actual time, particularly when static plans fall brief. Instead of a set script, ReAct brokers alternate between reasoning and motion—taking a step, observing outcomes, and deciding what to do subsequent. This permits brokers to adapt to ambiguity, evolving necessities, and conditions the place the most effective 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 potential options, and adjusts its technique as new data turns into out there. 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 best 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 actual world.
Why a unified agent platform is crucial
Building 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 elements?
- How do I scale from a single agent to a workforce of brokers—or hook up 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.
This 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 thought to enterprise-grade implementation.
Azure AI Foundry: Unified, scalable, and constructed for the actual 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 speedy innovation with sturdy, enterprise-grade controls.
With Azure AI Foundry, groups can:
- Prototype regionally, deploy at scale: Develop and take a look at brokers regionally, then seamlessly transfer to cloud runtime—no rewrites wanted. Check out methods to get began with Azure AI Foundry SDK.
- Flexible mannequin selection: Choose from Azure OpenAI, xAI Grok, Mistral, Meta, and over 10,000 open-source fashions—all by way of a unified API. A Model Router and Leaderboard assist choose the optimum mannequin, balancing efficiency, price, and specialization. Check out the Azure AI Foundry Models catalog.
- Compose modular multi-agent architectures: Connect specialised brokers and workflows, reusing patterns throughout groups. Check out methods to use related brokers in Azure AI Foundry Agent Service.
- Integrate immediately with enterprise programs: Leverage over 1,400+ built-in connectors for SharePoint, Bing, SaaS, and enterprise apps, with native safety and coverage help. Check out what are instruments in Azure AI Foundry Agent Service.
- Enable openness and interoperability: Built-in help for open protocols like Agent-to-Agent (A2A) and Model Context Protocol (MCP) lets your brokers work throughout clouds, platforms, and companion ecosystems. Check out how to connect with a Model Context Protocol Server Endpoint in Azure AI Foundry Agent Service.
- Enterprise-grade safety: Every agent will get a managed Entra Agent ID, sturdy Role-based Access Control (RBAC), On Behalf Of authentication, and coverage enforcement—making certain solely the best brokers entry the best assets. Check out methods to use a digital community with the Azure AI Foundry Agent Service.
- Comprehensive observability: Gain deep visibility with step-level tracing, automated analysis, and Azure Monitor integration—supporting compliance and steady enchancment at scale. Check out methods to monitor Azure AI Foundry Agent Service.
Azure AI Foundry isn’t only a toolkit—it’s the muse 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.
Stay tuned: In upcoming posts in our Agent Factory weblog sequence, we’ll present you methods to deliver these pillars to life—demonstrating methods to construct safe, orchestrated, and interoperable brokers with Azure AI Foundry, from native growth to enterprise deployment.