Agentic AI Systems in Decision Intelligence
Organizations need to go beyond automation into autonomous agentic tooling if they want to turbocharge their decisions.
In the rapidly evolving landscape of artificial intelligence, one trend is rising above most others, that of agentic AI systems. These are neither smarter chatbots, a simple rebranding of copilot-led initiatives, nor glorified automation scripts, but the art and science of applying intelligence to and within systems and applications.
Quantexa sees two powerful drivers in particular support the transformation, maturing Agentic AI into a demonstrably useful enterprise-ready capability. The first, the Model Context Protocol implemented through MCP Servers, is rapidly establishing as a functional and powerful communication standard. The second, an agent factory approach, offers a scalable, modular approach to deploying intelligent agents across enterprise workflows.
From LLMs to agents: A new era of intelligence
The journey from large language models (LLMs) to AI agents marks an evolution. Early LLMs, like the first iterations of ChatGPT, were reactive tools—answering questions, summarizing text, and generating content based on prompts. But they lacked autonomy. They didn’t plan, adapt, or make decisions.
Agents, by contrast, are proactive problem-solvers. They take input from the world, make decisions, and act upon them. They use tools, interact with services, and adapt to feedback. They’re not just passive responders, but they’re dynamic actors in a system: systems of agents. Their adaptability to their environment is what sets agentic AI apart from traditional rule-based automation, and a reason why it represents technological progress that, despite the social media and ups and downs that accompany fast-paced technology change, is destined to last.
What is an agentic AI system?
An agentic AI system is more than a single agent. It’s a managed, orchestrated system of agents, or if you prefer, a network of specialized agents, each with a defined role, working together to solve complex problems. Consider them as being like a managed army of interns, willing, enthusiastic learners, or as a fully-fledged, mature, impactful, progressive team, with individual highly skilled agents to perform and improve their own unique tasks. Our team or system of agents can, together:
Plan and execute multi-step tasks
Choose tools dynamically based on context
React to feedback and adapt strategies
Collaborate with other agents
Access and update memory to maintain context
This orchestration of agents creates a system that is flexible, scalable, and capable of handling unpredictable, open-ended tasks—ideal for the Decision Intelligence domains in which Quantexa operates, like fraud detection, anti-money laundering (AML), and customer intelligence.
Workflows vs. agentic systems
It is important to distinguish between automated workflows and agentic systems. Workflows follow predefined paths—if A > 0, go left; if A < 0, go right. They’re predictable, efficient, and ideal for structured, repeatable tasks. They can include LLMs, but they don’t adapt or learn.
Agentic systems, on the other hand, determine their own path. They respond dynamically to inputs, make decisions based on context, and adapt to changing environments. They’re best suited for complex problems with messy data, evolving requirements, and the need for human-like reasoning.
It is important to realize that not every task requires an agent. Here’s a simple guide of when to use agents, and when not to:
Use Agents When... | Use Workflows When... |
Tasks are open-ended or unpredictable | Tasks are structured and repeatable |
Flexibility and adaptability are key | Speed and cost-efficiency are priorities |
Multiple tools must be orchestrated | A single tool or LLM call suffices |
Human oversight is needed at scale | Human-in-the-loop is critical at each step |
Agents shine in flexible, adaptive environments. They are ideal for personalization, complex customer interactions, and tasks where the data is messy or constantly changing. But for predictable, high-speed operations, workflows remain the best choice.
MCP Servers: the backbone of agentic integration
A key technology is the Model Context Protocol (MCP), an open standard developed by Anthropic, the creators of Claude. MCP is enabled through MCP Servers which act as smart adapters, giving agents a consistent way to connect with tools, services, and data. They do this through:
Standardized APIs: Agents know how to call tools, pass data, and interpret responses.
Tool selection: Agents can choose from multiple “approved” tools based on task requirements.
Interoperability: Vendors and customers can deploy MCP servers to manage internal and external tools and applications.
Scalability: MCP servers enable seamless integration across organizations and platforms.
While MCP is still evolving, it has already surpassed the need for hand-coded interfaces around a common de facto standard for agent-tool interaction within agentic AI systems.
Building intelligent applications with an agent factory
Let’s take the systems of agents a stage further, multiple systems of agents.
In the first agentic AI system, each agent has a specific skill—some write reports, others analyze data, some monitor documents and reports. You assign tasks, they collaborate, and together they deliver a comprehensive solution.
But what if there is a second agentic AI system, one which consists of agents which individually manage the components of a database, select quality leads for sales to prioritize, reconcile duplicates, and tailor content? How do we ensure both systems work consistently?
It’s important for your organization to ensure systems of agents are holistically managed, governed and implemented, particularly where regulation and system transparency applies. Here’s where having a consolidated agent factory approach comes in.
Think of an agent factory as a design pattern or architecture focused on building, orchestrating, and scaling your agents, each with a specific role or task, for example where there are:
Multiple specialized agents (e.g., for UI generation, data retrieval, summarization) work together.
An orchestrator agent which coordinates the workflow.
Context providers supply relevant data—historical decisions, system scores, documents, likely to be MCP Protocol-based.
Memory modules maintain state, track progress, and ensure continuity across interactions.
The system is dynamic, with agents adapting to inputs and collaborating to achieve goals.
The modular design of the conceptual agent factory allows for scalable, intelligent applications that can be tailored to specific business goals. Whether it’s processing customer interactions, monitoring transactions, or generating personalized recommendations, the agent factory model provides a managed, transparent robust framework that can underpin multiple use cases across an enterprise.
The road ahead
Agentic AI systems are capable, flexible, and scalable. They represent a new class of intelligent applications—ones that can reason, adapt, and collaborate.
With MCP Servers providing the infrastructure and the agent factory offering the blueprint, we’re entering a new era of AI-driven decision intelligence.
As we move forward, expect to see:
Agent workforces deployed across enterprise functions and systems. In customer intelligence , consider orchestrated individual agents that perform tasks in the CRM such as data quality, data enrichment, conflict resolution, segmentation and scoring, harnessing their intelligence to improve systems and efficacy autonomously.
Cross-organizational agent collaboration via MCP. When collaborating on Suspicious Activity Reports (SARs), a bank’s SAR agent could query a law enforcement database via their MCP Server to validate suspicious behavior patterns. Agents can delegate tasks to agents in other organizations; e.g., a bank’s agent asks a regulator’s agent to check for prior SARs on an entity. MCP protocols could also enforce access controls and audit trails, ensuring compliance with privacy laws.
Intelligent applications that combine agents, tools, and data in novel ways. Case management workflows can manage and orchestrate agents that triage cases, select tools, perform core tasks such as entity resolution, data enrichment, scoring, invoking tools, e.g. graph analytics or NLP tools, as required to support a claims, fraud or customer support investigation.
At Quantexa, we’re excited to be at the forefront of the agentic Decision Intelligence transformation, building and helping our customers implement systems that do more than automate, but which understand and improve.
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