Yina Arenas introduces agentic AI concepts and enterprise-ready architectural patterns in Azure AI Foundry, providing clear guidance on building, deploying, and securing intelligent agents for the modern enterprise.

Agent Factory: The New Era of Agentic AI—Common Use Cases and Design Patterns

By Yina Arenas

Agentic AI moves beyond traditional information delivery by enabling agents to reason, act, collaborate, and adapt—bridging the gap between knowledge and tangible business outcomes. This detailed overview inaugurates a six-part ‘Agent Factory’ series focusing on design patterns, use cases, and tools for adopting agentic AI with Azure AI Foundry.

Why Enterprises Need Agentic AI

Retrieval-augmented generation (RAG) models marked a leap in enterprise AI by surfacing information quickly, but most organizational tasks demand reliable action—such as filling forms, updating records, or orchestrating multi-step processes. While legacy automation (scripts, RPA bots) often break at scale, agentic AI introduces intelligent agents that reason, make decisions, and coordinate complex workflows robustly.

Key agentic AI capabilities:

  • Direct interaction with enterprise systems (APIs, workflows)
  • Task automation end-to-end (retrieval, analysis, action)
  • Adaptability and learning through reflection
  • Multi-step planning and decomposition of complex goals
  • Collaboration among specialist agents

Common Patterns of Agentic AI

1. Tool Use Pattern Agents interact with APIs and business systems, moving from recommendations to direct execution—for example, automating proposal generation (as in Fujitsu’s sales process), or updating enterprise records.

2. Reflection Pattern Agents assess and improve their own outputs, catching errors and iteratively refining results before human handoff. This pattern enhances reliability and is especially useful in compliance-heavy or mission-critical environments. Even advanced code assistants like GitHub Copilot leverage internal feedback loops.

3. Planning Pattern Planning agents decompose complex tasks into actionable steps, tracking progress and responding flexibly to changes. ContraForce’s Agentic Security Delivery Platform (ASDP) used planning agents to automate incident investigation and response, reducing effort and improving robustness.

4. Multi-Agent Pattern Mirroring real-world specialist teams, this pattern connects networks of agents—each with specific roles—coordinated via various orchestration methods. For example, in software lifecycle automation, specialized agents handle requirements, coding, documentation, and QA with notable efficiency gains (as seen in JM Family’s BAQA Genie).

5. ReAct (Reason + Act) Pattern ReAct agents alternate between reasoning and action, responding to real-time feedback for greater problem-solving agility. Useful in complex or ambiguous scenarios like IT support triage, where adaptive responses are essential.

These patterns are often combined to build robust, flexible solutions.

The Need for Unified Agent Platforms

Transitioning from demos to production agentic AI reveals new challenges:

  • Chaining multi-step processes reliably
  • Securing access to business data
  • Monitoring and continuous improvement
  • Managing identity and permissions
  • Scaling from single agents to multi-agent orchestration

Many teams build custom infrastructure for orchestration, security, and monitoring, but this approach increases risk and slows deployment.

Azure AI Foundry: Enterprise-Grade Agentic AI

Azure AI Foundry addresses these challenges with a comprehensive platform:

  • Local prototyping and seamless cloud deployment (via SDKs)
  • Flexible access to over 10,000+ open and proprietary models (Azure OpenAI, xAI Grok, Mistral, Meta, and more)
  • Modular composition of multi-agent architectures
  • 1,400+ built-in connectors for business systems, supporting integration with SharePoint, Bing, SaaS, and enterprise apps
  • Open interoperability (Agent-to-Agent protocol, Model Context Protocol)
  • Enterprise security (managed Entra Agent ID, RBAC, policy enforcement, virtual network options)
  • Comprehensive observability (step-level tracing, Azure Monitor integration)

Check out:

Azure AI Foundry enables organizations to orchestrate secure, scalable AI agents—moving from isolated automations to true end-to-end digital transformation.

Stay tuned: Upcoming posts in the Agent Factory blog series will delve deeper into building, securing, and orchestrating interoperable agents using Azure AI Foundry—from local development to enterprise deployment.

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