What Are Agentic AI Frameworks? (With Examples)

Key Takeaways

• Agentic AI frameworks enable businesses to build autonomous AI Agents capable of decision-making, collaboration, and complex workflow automation.
• Vertical AI frameworks are purpose-built for regulated industries, offering better accuracy, built-in compliance, scalability, and long-term cost savings compared to horizontal frameworks.
• Horizontal AI frameworks are flexible but require extensive customization, making them less suitable for highly regulated industries like banking and insurance.
• Key features of effective agentic AI frameworks include modularity, multi-agent orchestration, reasoning & planning, seamless tool integration, and robust monitoring.
• Using a structured, agentic framework significantly improves operational efficiency, reduces manual effort, ensures compliance, and streamlines complex business processes in finance and insurance.

What Are Agentic AI Frameworks?

Agentic AI frameworks are software systems for building autonomous AI Agents that execute tasks, make decisions, and interact with users or systems. These frameworks incorporate memory, reasoning, planning, and multi-agent coordination. They enable AI to operate independently, adapt dynamically, and integrate with APIs for real-world automation.

Unlike traditional AI models that follow predefined workflows, agentic AI frameworks provide adaptability and intelligence. These collaborative multi-agent systems handle complex decision-making processes that require reasoning and real-time data access. 

These frameworks are particularly useful for automating business processes that involve large volumes of data, multi-step tasks, and compliance considerations. Businesses can reduce manual effort, improve accuracy, and optimize resource allocation by allowing AI agents to operate semi-autonomously.

Horizontal vs. Vertical Agentic AI Frameworks

Generally speaking, we can divide agentic frameworks into horizontal and vertical types. The main difference between horizontal and vertical AI agent frameworks lies in their scope and specialization. 

Choosing between these two frameworks depends on your industry's complexity and regulatory demands.

Horizontal AI Agentic Frameworks

Horizontal frameworks are designed for general AI applications. They offer broad capabilities but require extensive customization for specific tasks. They may be great for experimentation and research but struggle with real-world deployment in regulated industries.

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Horizontal AI Agentic Frameworks Challenges

• Too broad: Horizontal frameworks serve many industries but don’t excel in any. They handle general AI tasks but lack industry-specific optimizations.
• Customization required: These frameworks need extensive tuning to work in regulated fields. Companies must build compliance features from scratch, adding cost and complexity.
• Compliance gaps: Financial services and insurance require strict data security and audit trails. Horizontal AI frameworks lack built-in compliance, making them risky for critical operations.
• Scalability issues: Horizontal frameworks scale across industries but struggle with specialized workflows. They lack deep integration with financial and insurance systems, leading to inefficiencies.

Vertical Agentic AI Frameworks

Vertical agentic AI frameworks, on the other hand, are purpose-built for specific domains. They come with pre-configured capabilities tailored to complex workflows in industries like finance and insurance. 

Instead of forcing enterprises to piece together multiple tools, vertical frameworks provide an integrated, end-to-end solution.

Why Vertical AI Frameworks Are Better

• Purpose-built for the industry: Vertical frameworks are designed for specific workflows. They include pre-built models tailored to financial regulations, risk analysis, and claims processing.
• Stronger compliance: These frameworks come with built-in security and compliance controls. They align with regulations like SOC2, GDPR, and financial reporting standards from the start.
• Higher accuracy: Vertical frameworks use industry-specific data and fine-tuned AI models. This results in fewer errors and better automation for critical decision-making tasks.
• Lower long-term costs: While vertical AI frameworks may cost more upfront, they save money over time. They require less ongoing customization, reduce operational inefficiencies, and ensure compliance from day one.
• Scalability within the industry: Unlike horizontal frameworks that spread thin across industries, vertical frameworks scale deeply within their field. They grow alongside industry-specific needs without sacrificing performance.

For banking and insurance, vertical AI frameworks offer the best solution. They provide better accuracy, compliance, and efficiency. Instead of forcing a horizontal system to fit industry needs, businesses can use a purpose-built AI framework. 

By understanding these trade-offs, businesses can make informed decisions when selecting the right AI agent framework for their operations. In the next section, we’ll explore a real-world example of a vertical AI framework in action.

Example: AgentFlow

AgentFlow is a prime example of a vertical agentic AI framework optimized for regulated industries, ensuring compliance, security, and seamless automation. It orchestrates multiple AI Agents to automate complex workflows while maintaining security and compliance.

How AgentFlow Works

AgentFlow operates as an API-first orchestration layer coordinating four types of specialized agents:

• Process Agents (Unstructured AI & Document AI): Automate document classification and data extraction from over 100 document types, reducing manual effort and error rates.
• Search Agents (Conversational AI & Database AI): Cross-reference enterprise databases, external APIs, and compliance repositories to fetch relevant data in real time.
• Decide Agents (Decision AI): Apply rule-based logic to determine outcomes for insurance claims, loan approvals, and other decision-intensive processes.
• Create Agents (Report AI): Generate structured, audit-ready reports that meet industry-specific regulatory requirements.

Its modular pipeline ensures full automation while preserving human oversight. It consists of six modules that simplify the end-to-end process:

The platform supports seamless scaling from a single agent to managing dozens or even hundreds of agents. By automating repetitive tasks, it enables human teams to focus on high-value strategic initiatives, improving job satisfaction and overall productivity.

AgentFlow addresses common industry challenges, including high manual workloads, bottlenecks, and error rates. With SOC2 Type II compliance and private cloud deployment options, it meets strict regulatory standards in finance and insurance.

Why Should You Use Agentic AI Frameworks?

Agentic frameworks go beyond simple large language models (LLMs) integrations. Instead of just generating text, they offer essential features like memory persistence, state management, and tool integration. These features enable developers to build reliable, autonomous AI systems.

Managing multiple AI Agents without a structured framework is chaotic. Without a unified orchestration layer, enterprises struggle with:

• Disjointed AI Agents that don’t communicate effectively, leading to inefficiencies and inconsistent outputs.
• Security vulnerabilities in loosely integrated tools that lack centralized governance and compliance features.
• High maintenance costs from piecing together multiple AI Agents that require ongoing engineering resources.

Agentic AI frameworks, like AgentFlow solve these issues by offering a centralized architecture for multi-agent collaboration, decision-making, and real-time monitoring. They provide essential components—memory persistence, state management, and built-in tool integration. 

This structured approach ensures AI systems proactively reason, plan, and reliably execute complex tasks rather than simply responding to prompts.

Organizations that adopt a structured agentic AI approach benefit from increased efficiency, reduced errors, and faster process execution across complex workflows. Additionally, AI Agents can adapt dynamically to evolving business conditions, making workflows more resilient to unexpected changes.

Overall, agentic frameworks mark a significant step forward—from basic text-generation models to powerful, enterprise-ready AI Agents.

Key Features of Agentic AI Frameworks

Modularity

Agentic AI frameworks offer a plug-and-play structure where components like memory, reasoning, and execution can be customized or replaced. This allows organizations to scale their AI capabilities without rebuilding their entire automation stack.

Multi-Agent Orchestration

A single AI agent may struggle to handle multi-step tasks, but agentic AI frameworks enable seamless collaboration between multiple AI Agents. Each agent specializes in different tasks, such as data retrieval, decision-making, and reporting, ensuring efficient task delegation and communication.

Reasoning & Planning

Unlike rule-based systems, agentic AI frameworks equip AI Agents with the ability to break down tasks, make strategic decisions, and autonomously adjust actions based on new data. This allows AI Agents to perform complex workflows such as customer support, fraud detection, and compliance monitoring.

Tool & API Integration

Modern businesses rely on various software platforms, databases, and APIs. Agentic AI frameworks connect AI Agents with these external systems to process real-time information, improving automation across different departments. Seamless API integration enables AI Agents to access external knowledge bases, perform transactions, and validate data accuracy.

Monitoring

AI-driven processes require oversight to maintain accuracy and compliance. Real-time dashboards track AI performance, error rates, and system efficiency, ensuring smooth operation and adherence to regulatory requirements. This is where our AgentFlow excels.

With robust monitoring capabilities, businesses can maintain transparency and accountability across AI-driven workflows.

Using AI Agent Frameworks in Real Life

AI agentic frameworks allow highly regulated organizations to build complex AI systems, enable multi-agent collaboration, and achieve true workflow automation. 

Example #1: Banking

Banks leverage agentic AI frameworks to automate complex tasks like loan origination and underwriting, replacing manual processes with efficient, AI-driven workflows:

• Process Agents extract data from loan applications and financial records, eliminating manual entry.
• Search Agents retrieve and verify customer information, including credit histories, identity verification, and financial eligibility.
• Decide Agents cross-reference customer information with regulatory requirements to approve or deny applications.
• Create Agents generate clear, audit-ready underwriting reports with explainable outputs, ensuring compliance with financial regulations.

By automating these steps, banks can reduce processing times from weeks to days. This minimizes fraud risks, standardizes lending decisions, and enhances customer experiences through equitable, faster financial services.

Example #2: Insurance

Insurance companies automate claims processing with AI agent frameworks, streamlining adjudication workflows and dramatically improving efficiency and regulatory compliance:

• Process Agents classify and extract key information from First Notice of Loss (FNOL) submissions via web portals and emails.
• Search Agents perform adjudication and diligence by cross-referencing company databases, compliance repositories, and external sources to verify claim details.
• Decide Agents apply compliance-driven rules to swiftly and accurately determine claim approvals or denials.
• Create Agents automatically generate structured, audit-friendly documentation for claim adjustments, providing transparency for regulatory audits.

Through these complex AI systems, insurers significantly reduce manual errors, speed up claim settlements, and consistently adhere to compliance requirements. As a result, they enhance operational efficiency, reduce costs, and deliver superior customer satisfaction.

Which Agentic AI Frameworks NOT to Use

While several AI agent frameworks exist, not all are suitable for complex enterprise workflows. When comparing horizontal and vertical agentic AI frameworks, we explained why and for whom vertical frameworks are better. 

Examples of horizontal frameworks include LangChain, AutoGen, and CrewAI, all fantastic tools that offer flexible but generalized capabilities for developers. However, these frameworks often lack built-in compliance features, security protocols, and industry-specific optimizations needed for enterprise use.

Why are they not the best choice for specific industries?

• LangChain: Great for developers building chatbots and LLM-powered applications, but it requires significant customization for regulated industries.
• AutoGen: Ideal for research teams and experimental AI projects but lacks enterprise security and compliance features.
• CrewAI: Specializes in human-AI collaboration but is not optimized for process automation in industries requiring strict compliance.

Vertical AI agent frameworks like AgentFlow are purpose-built for enterprises handling sensitive data like the finance and insurance industries. They offer superior security, industry-specific optimizations, and enterprise-grade automation capabilities.

See Our Agentic AI Framework Live

Want to see AgentFlow in action? Book a demo today to learn how it can streamline your enterprise AI workflows, reduce operational costs, and enhance compliance-driven automation.

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