Agent Factory: The new era of agentic AI—common use cases and design patterns

Overview

Agentic AI is revolutionising enterprise automation by enabling AI agents to reason, act, and collaborate, thus bridging the gap between knowledge and actionable outcomes. Azure AI Foundry offers a unified platform to facilitate the development and deployment of these agents, ensuring robust, scalable, and secure solutions.

Key Insights

Tool Use Pattern: Agents interact with enterprise systems to execute tasks and orchestrate workflows, reducing production time by up to 67% in some cases, such as Fujitsu's sales proposal process.
Reflection Pattern: Agents self-improve by assessing their outputs, reducing errors in high-stakes fields like finance and compliance.
Planning Pattern: Agents decompose complex tasks into actionable steps, automating up to 80% of incident investigations, as seen with ContraForce's platform.
Multi-Agent Pattern: Networks of specialized agents collaborate to enhance agility and scalability, cutting QA time by up to 60% in some implementations.
ReAct Pattern: Agents adapt to real-time changes, improving problem-solving capabilities in dynamic environments.

BFSI Relevance

Why Relevant: Agentic AI can significantly enhance automation in BFSI sectors, reducing operational costs and improving process efficiency.
Primary Sector: Financial Services
Subsectors: Asset Management, Claims Processing, Compliance
Actionable Implications:
- Implement agentic AI to automate complex workflows and reduce manual intervention.
- Use Azure AI Foundry to develop secure and scalable AI solutions.
- Leverage multi-agent systems to improve collaboration and efficiency across departments.