What are LLM Agents? Vertical vs. Horizontal Agents
What are LLM agents? They're AI tools that automate complex workflows with less human input. Boost efficiency and reduce errors. See the real-life examples.
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LLM agents are AI systems that use large language models (LLMs) to perform tasks autonomously. They combine language understanding with external tools or actions to complete goals like answering queries, writing code, or managing workflows without constant human guidance.
But how do they actually get things done?
LLM agents work by using large language models to plan, reason, and take actions based on prompts and user input. They interpret specialized tasks, break them into steps, and autonomously choose actions like calling APIs, running code, or generating text. LLM agents rely on both long term and short term memory, context, and tools to complete complex workflows.
Key Capabilities of LLM Agents
LLM based agents go beyond simple task execution. They can break down goals into smaller, manageable goals and manage multi-step workflows by following complex instructions.
“The agent can structure, draft, and follow up. It’s not just task completion.” — Suzanne Rabicoff
This means that multiple agents can organize information, generate drafts or responses, and handle follow-up actions to ensure workflow processes are automated end-to-end.
Additionally, LLM agents can integrate with APIs into existing systems and workflows, search databases, and access the agent’s internal logs and external sources. This gives them flexibility to solve a wide range of problems autonomously while adapting to new information as needed.
So, how do LLM agents work and how do they automate workflows? Check out these real-life examples.
Examples
Loan Servicing
For loan servicing, LLM agents can automate and optimize the entire borrower management lifecycle:
Monitoring borrower behavior - Database AI agent continuously monitors borrower data, such as payment histories and credit scores, to maintain an up-to-date risk profile for each customer. Decision AI agent analyzes these data streams to identify patterns and flag potential risks, such as missed payments or declining creditworthiness.
Generating alerts and insights - While risk or opportunities are detected, Decision AI recommends the most effective communication channel for borrower outreach. Conversational AI agent then generates personalized communications, reminders, support messages, or important updates for the borrower at the right time.
Decision-making support - As situations arise, such as potential defaults or requests for loan modifications, the Decision AI agent provides loan officers with actionable recommendations. These can include initiating collection efforts, offering restructuring options, or escalating cases for manual review, ensuring every action is compliant and data-driven.
Reporting and data analysis - Report AI agent compiles comprehensive analytics on resolution rates, borrower engagement, and other key metrics. These reports help financial institutions track servicing performance, identify trends, and continuously improve their strategies.
Such agentic workflow automation increases operational efficiency, reduces manual workload, and improves borrower engagement. It also ensures regulatory compliance and keeps loan officers in the loop for complex cases.
Insurance Underwriting
For insurance underwriting, multiple LLM agents coordinate to manage everything from application intake to policy generation:
Document classification &triage - Policy applications and documents are ingested. Document AI agent classifies and extracts key information, while the Decision AI agent triages submissions to determine which require immediate attention.
Diligence, verification, and follow-up - Database AI agent verifies applicant data and performs due diligence checks. Conversational AI agent helps internal teams follow up on missing documents or needed clarifications.
Underwriting decision - Decision AI agent analyzes all available data to make an underwriting decision. It can approve, deny, or escalate the complex decisions to human underwriters for review.
Policy generation - Report AI agent automatically generates policy documents, ensuring that every step of the process is tracked and audit-ready.
Such an approach speeds up underwriting, reduces errors, and ensures compliance while allowing underwriters to focus on high-risk cases.
Vertical vs. Horizontal LLM Agents
LLM agents are categorized as horizontal or vertical agents.
Horizontal agents are general agents built for handling various tasks across many domains, but often at a surface level. A good example of this would be a capable assistant who knows a little bit of everything and can help automate light tasks.
But what are vertical LLM agents?
Vertical LLM agents are specialized. They are designed for specific industries or use cases, such as insurance underwriting, claims processing, or financial reporting. They have a deeper context, valuable domain knowledge, and they’re tailored to those tasks.
Therefore, vertical agents always deliver higher-quality outputs, they can handle more complex workflows rather than just surface tasks, and they make fewer mistakes because they can understand the context and nuances of a particular field.
Top 3 Benefits of LLM Agents
Advanced Autonomous Automation
LLM agents can automate complex workflow processes end-to-end, minimizing human interaction and improving operational efficiency across industries.
They don’t just respond, but they also act and take actions such as:
Interpreting instructions
Planning the approach
Executing task
Handling follow-ups
Such autonomy minimizes human oversight and frees up employees to focus on high-value work.
Improved Problem Solving and Multi-Step Task Execution
LLM agents can break down complex problems into smaller tasks and reason through each part, choose the right tools and actions at each step of the process to solve tasks that traditionally were impossible or required a lot of human oversight.
They can handle end-to-end process execution with minimal human input, whether it’s processing documents, searching databases, making decisions, creating reports, or handling a wide range of business operations.
Contextual Awareness and Integration
Agents are trained using company data, and they can access company-specific data, remember prior interactions, and integrate with third-party external systems like CRMs, databases, email tools, and more.
Such deep contextual awareness allows agents to take more relevant actions, make accurate responses and decisions, and operate as a true extension of the team.
Relying on company-specific data and being tailored to the company’s workflows, goals, and customers, LLM agents can continue learning and improving over time.
Key Components of LLM Agents
The easiest way to understand how LLM agents operate is to visualize their components and architecture.
Implementations of these agents vary, but the most common components are:
Agent core (brain)
Memory
Planning
Tools
User request (prompt)
Together, these components help LLM agents interpret input, reason through tasks, and take action.
Agent Core (Brain)
LLM is the core of an agent, helping the agent interpret instructions, generate outputs, and decide on the next steps.
Agent core is best visualized as the agent’s brain, which is where the reasoning happens to help the agent make sense of goals and the next actions to take.
Memory
Memory is an easy component to understand as it helps agents to recall past conversations and interactions (and even facts or results).
Sometimes this is short term memory (within one session), and sometimes it’s long term memory, helping agents to improve their understanding, coherence, responses, and avoid redundant work.
Planning
The planning mode is where complex tasks are broken down into smaller, actionable steps. This helps agents execute multi-step tasks, handle logic, and adapt strategies to reach the end goal.
Tools
Tools help agents extend their capabilities beyond language generation.
Often, these include API connectors or search functions, helping agents interact with external systems and complete tasks that they otherwise couldn’t with natural language processing only.
It also helps minimize the need for human intervention, allowing the multi-step execution.
User Request (Prompt)
The user request (known as a prompt) is the starting point, as it defines the task or question the agent will tackle.
It’s worth mentioning that this isn’t technically part of the agent itself, but it’s what activates the agent’s entire system.
Also, it’s important to keep in mind that the quality of the input plays a huge role in the success of the agent’s output.
Challenges With Implementing LLM Agents
The most common challenges with implementing LLM agents include:
Hallucinations
System integration, scalability, and complexity
Security and compliance risks
1. Hallucinations
LLM agents can generate incorrect information, even though it can sound very confident. Therefore, ensuring accuracy in industries like finance and insurance is a major challenge.
This is often solved by using quality company data, validation layers to fact-check outputs, or retrieval-augmented generation to ground responses using trusted data.
Combining AI and humans for the review of sensitive or high-impact outputs is also a good plan.
2. System Integration, Scalability, and Complexity
Integrating LLM agents into existing business systems without ripping and replacing the existing systems can be a technical hurdle.
Most integrations often face challenges such as compatibility issues with legacy systems or inconsistent data formats.
As organizations scale, they need to address the complexity of multiple AI agents, increased computational resources, and potential performance bottlenecks.
This is best solved by using middleware or integration layers that help bridge the systems, secure APIs that allow agents to act safely and effectively, and automating high-value workflows first before expanding into more complex workflows.
3. Security and Compliance Risks
Deploying LLM agents comes with risks of data privacy, access control, and regulatory compliance, especially in high-stakes industries like finance and insurance.
Ensuring data trails, encrypted data handling, relying on company data, and using AI to manage compliance are some of the ways to handle the risks.
Logging decisions and having transparent reasoning chains of why and how LLM agents took actions with human oversight can also help minimize the risks.
How AgentFlow Helps Build, Deploy, and Manage LLM Agents
AgentFlow is a vertical agentic AI framework designed for regulated industries like finance and insurance. It helps companies build, deploy, and manage LLM-powered agents with security, compliance, and scalability.
It orchestrates specialized AI agents to automate complex workflows end-to-end while maintaining human oversight and auditability.
Here’s how AgentFlow helps build, deploy, and manage LLM agents in 4 steps:
Configuration
Orchestration
Monitoring and review
Fine-tuning
Configuration of LLM Agents
AgentFlow offers flexible configuration options. Organizations can choose between a fully managed (white-glove) or a self-service setup.
Deployment can be on private cloud or on-premise to keep data secure, while an easy-to-use interface allows tailoring of compliance rules and document handling to business needs.
Orchestration of LLM Agents
The platform allows for easy multi-agent orchestration, where companies can use:
Process agents for data extraction
Search agents for real-time data retrieval
Decision agents for applying business logic
Creation agents for generating reports
AgentFlow intelligently routes tasks based on context and compliance requirements and integrates human oversight by flagging uncertain cases for manual review. This ensures smooth, accurate, and compliant workflows.
Monitoring & reviewing LLM Agents
AgentFlow provides real-time monitoring with confidence scores to highlight decisions needing human attention.
It maintains detailed audit trails of all AI actions for transparency and compliance. Companies can track performance metrics like processing times and error rates in the intuitive dashboard, while explainability features help users understand how agents reach conclusions.
Such explainability is essential for regulatory audits and trust.
Fine-tuning Large Language Models
The AgentFlow framework supports ongoing fine-tuning by adjusting prompts and workflows with domain-specific language, improving memory for recurring tasks, and incorporating feedback to improve accuracy.
AgentFlow can also swap underlying foundation models to leverage the latest AI advancements without disrupting operations.
Automate Your Workflows End-to-End With AgentFlow
Would you like to see AgentFlow in action? Want to see how you can implement it in your system and automate your workflows end-to-end?
Book a demo today, see it live in action, and learn how you can deploy LLM agents in 90 days or less.
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