Many businesses today grapple with intricate processes. In fact, according to a report, two-thirds of business leaders perceive their organizations as overly complex and inefficient. [1] These "overly complex" business operations often involve multiple steps, diverse data sources, and require careful decision-making. A new approach, using collaborative multi-agent AI systems, is helping organizations manage these challenges effectively. This system moves beyond general large language models (LLMs) or basic workflow automation.
Breaking Down Agent Types
You often hear about Artificial Intelligence (AI) changing how businesses operate. One powerful approach is using collaborative multi-agent systems. These systems employ different types of AI agents, where each agent has a specific role. Together, they tackle complex tasks.
Let's look at these configurable AI agents:
- Planner Agents: These agents act like project managers. They take a large goal and break it down into smaller, manageable tasks. They then assign these tasks to other specialized agents. SyncIQ utilizes Planner Agents to decompose goals and auto-assign tasks to various agents in a workflow.
- RAG Agents (Retrievers): These agents act as a smart intermediary, handling tasks like query reformulation, multi-step reasoning, and orchestrating retrieval across various data sources. Unlike traditional retrievers, it actively interprets user intent, formulates dynamic queries, selects appropriate data sources, and ranks/filters results before passing them to another agent.
- Structured Data Agent: This agent specializes in working with organized data, such as information found in databases, APIs, and spreadsheets. It can query, analyze, and extract information, identify trends, generate reports, and provide recommendations based on this structured data.
- Tool Calling Agents (Executors): These agents are the doers that interact with other software systems and perform specific, deterministic tasks. They use "tools" to carry out their functions. In this context, tools can be any API call used to fetch, push, or update data in systems like CRMs, ERPs, or other databases. This allows them to execute precise operations as part of a larger workflow.
- Generation Agent (Summarizing, Inferencing, etc.): This agent focuses on creating new content or understanding existing information at a deeper level. Its tasks can include summarization of long documents, inferencing to draw conclusions from data, or generating reports and answers based on the available information.
- A2H Agents (Agent-2-Human): A proprietary SyncIQ agent, this agent's primary role is to facilitate seamless collaboration and handoffs between AI agents and human users. When a workflow needs human input for tasks like review, editing, or approval, the A2H Agent dynamically generates the right user-facing screen on the fly so that users can quickly jump in, make decisions, and maintain control, with every action traced for audit purposes.
How Collaboration Looks in Practice: A Claims Processing Example
The real capability of these AI agents emerges when they collaborate within a multi-agent system. Let's look at an example like claims processing in an insurance company, a process SyncIQ has helped streamline.
Imagine a claim arrives. Instead of manual handling, which can take anywhere from 30 to 60 days, a multi-agent system gets to work much faster. [2] Initially, an agent receives the First Notice of Loss (FNOL) and plans the next steps. Other agents then retrieve and cleanse relevant data, such as policy documents and submitted information. Finally, agents execute tasks like auto-filling forms, perform validation checks, and route the claim for either automated processing or human review.
Throughout this process, every action taken by each agent can be logged, providing a stable audit trail for traceability and verification.
Stay tuned for our breakdown of this intelligent workflow in the next part of our article series!
Beyond Basic AI: The Multi-Agent Advantage
This multi-agent approach is different from using a generic Large Language Model (LLM) or simpler workflow automation tools. Let’s take a closer look at the how:
- vs. Generic LLMs: LLMs are powerful for generating text or answering broad questions. However, they are not designed for executing specific, multi-step business processes with the same level of precision and control.
An LLM might help draft an email, but it won't manage the entire claims pipeline, interact with multiple disparate systems, ensure data integrity specific to your business rules, and maintain a traceable record of every action. SyncIQ's Multi-agent platform breaks down this complex process into manageable tasks for specialized agents, leading to more accurate, verifiable, and controlled outcomes.
- vs. Simpler Workflow Automation: Simpler workflow automation tools also fall short. They often follow rigid, pre-defined paths and lack the dynamic task allocation and specialized capabilities of a multi-agent system.
SyncIQ’s agentic workflows, for instance, can combine defined business logic with the nuanced understanding of AI agents, allowing them to handle a wider range of inputs, adapt to minor deviations, and even involve human experts when needed through protocols like an "Automated Agent 2 Human Interface (A2H Protocol)".
Want to know more about SyncIQ's A2H Protocol? Checkout our latest Newsletter issue here to find out!
Another significant advantage of the multi-agent approach is "Rapid Workflow Configuration". Because agents are designed for specific functions, they can be assembled and configured in various ways to address different business needs. This adaptability means businesses can respond much faster to changing requirements.
At SyncIQ, we build agentic systems that think, plan, and execute alongside humans, bringing structure and traceability to the most complex of business operations.
Could a multi-agent system streamline your complex workflows? Request a personalized demo to know how.
References
[1] The State of Organizations 2023: Ten shifts transforming organizations
[2] Insurance Claim Processing: What Really Affects Your Wait Time
