Author: Daniel Warner
Agentic AI: From Emerging Trend to Practical Framework
As the AI landscape matures beyond single-shot, text-based models, we’re seeing the rise of more dynamic systems, ones that reason, act, and integrate seamlessly with tools. This shift is bringing Agentic AI into the spotlight. What began as an emerging approach is rapidly maturing into a practical and scalable framework for building systems that can independently handle delegated tasks, often spanning multiple tools, services, and contexts.
What makes these systems so compelling is their ability to operate with minimal oversight, taking in information, making decisions, and taking actions, all while adapting to changing conditions. As the tools to build these systems become more accessible, we’re starting to see them embedded across real-world use cases, from smart assistants to workflow automation platforms.
The Role of LLMs and Multimodal Models in Agentic Systems
At the heart of these agentic systems are Large Language Models (LLMs), like OpenAI’s GPT series, which bring robust reasoning and communication skills to the table. These models excel at understanding and generating human-like language, enabling agents to interact naturally, draw insights from unstructured data, and apply learned knowledge to new scenarios.
But language alone isn’t always enough. That’s where multimodal models come in, combining text with other forms of input like images, audio, and structured data. This broader perspective gives agents the kind of situational awareness needed to operate effectively in more complex environments.
Together, LLMs and multimodal models form the cognitive foundation for Agentic AI, enabling agents not just to respond, but to perceive, reason, and act with a goal in mind.
n8n: A Versatile Platform for Building Agentic Workflows
When it comes to actually building these systems, one of the tools we’ve been exploring is n8n, an open-source workflow automation platform that’s surprisingly well-suited for constructing agentic workflows.
Unlike traditional workflow tools that stick to rigid if/then rules, n8n offers the flexibility to bring in AI, inject logic, and react to changes in data or context in real time. A few standout features that make it a great fit:
- Visual Workflow Builder: Intuitively design workflows using a drag-and-drop interface. This makes it easier to prototype complex flows, even if you’re not writing much code.
- Extensive Integrations: With over 400 integrations, n8n connects to everything from CRMs to cloud storage, making it easy to orchestrate actions across your existing stack.
- Custom Code Support: For more advanced use cases, developers can drop in Python for full control over logic and data handling.
- Self-Hosting Option: Ideal for teams that need to maintain full control over their data or infrastructure for compliance or governance reasons.
This blend of accessibility and power makes n8n a compelling base layer for building agentic systems, particularly those that operate across both traditional APIs and modern AI capabilities
Bringing AI into the Loop
n8n also plays nicely with AI. Whether it’s OpenAI or Google models for text generation, calling out to custom APIs for classification, or chaining logic based on AI-generated responses, the platform gives you the scaffolding to create workflows that feel much more like collaborative agents than static scripts.
Here’s how we’re typically bringing AI into n8n workflows:
- Agent Nodes: Native support for calling LLMs lets you bring reasoning into your workflows.
- Decision Logic: Combine AI outputs with conditional paths to build workflows that can reason and adapt on the fly.
- External APIs: Enrich workflows with third-party data sources or services, enabling broader context for decision-making.
These pieces come together to support workflows that are context-aware, dynamic, and capable of adapting their actions based on input, an essential hallmark of Agentic AI.
Our Experience: Exploring Agentic AI in Practice
At exposé, we’ve been actively exploring how agentic architectures can be applied using n8n in real-world settings. Whether it’s enabling AI agents to make decisions across multiple systems or orchestrating multi-step reasoning pipelines, we’ve found that n8n provides a highly adaptable canvas for experimentation.
In the next section, I’ll walk through one of the agentic AI workflows we’ve built, highlighting how decision-making, AI reasoning, and orchestration come together in practice. This isn’t just about automation, it’s about building agents that can think, act, and improve over time.
Building an Agentic Workflow in n8n: A Loan Application Example
Let’s walk through a practical example of how an Agentic AI system can be used to process loan applications, specifically focusing on the orchestration of different AI agents working together within an n8n workflow.
Rather than relying on a single all-knowing model, agentic systems work best when composed of multiple specialised agents, each designed to carry out a specific task, such as validation, document extraction, or application scoring. These agents are coordinated by an orchestrator agent, which receives inputs, delegates tasks to appropriate agents, and routes these results accordingly.
Agent Configuration in n8n
In n8n, each of these agents is powered by the Agent node, which offers flexible configuration options tailored to building intelligent, context-aware workflows:
Model: Choose from various LLM providers including Azure OpenAI, Google Gemini, and Anthropic.
Memory: Maintains context across interactions, allowing the agent to recall previous conversations. It is a powerful tool for both operational reasoning and business insight.
Tools: The real engine of agentic workflows. Tools can include external API calls (e.g., querying a database), third-party services (e.g., updating a CRM), or even other agents. You can configure as many as needed, each with a clear description that helps the LLM know when and why to use it.
Output Parser: Enforces strict output formats, which are essential for chaining agents together reliably in a workflow.
Phase 1: Extraction and Validation
Let’s start at the beginning of the loan application process: the user uploads a set of supporting documents as well as the application itself via frontend UI.
The orchestrator agent receives this input and, recognising it as the user’s first interaction (via memory), passes the data to a dedicated extraction agent. This agent is designed to handle a variety of formats, including PDFs, images, and CSVs, and outputs structured data that downstream agents can process.
One of n8n’s biggest strengths is how transparent and inspectable each node is. Developers can click into a node like the extraction agent and clearly see what data went in and what came out:
From here, the validation agent takes over. It checks the extracted information against existing customer records (via a tool call to a database) and is instructed to provide a rationale for any discrepancies it finds. In this scenario, the agent identifies two issues:
- A mismatch in the applicant’s address
- Ambiguity in the salary_frequency
The orchestrator then constructs a custom UI form and sends it back to the user to confirm or correct these specific fields:
Phase 2: Dynamic Routing and Decision-Making
What we’ve described so far is a fairly structured and linear flow:
But the real power of agentic systems emerges when the orchestrator takes on a more autonomous role, making its own decisions based on context, without human input.
Let’s say the extraction agent captures a street address but misses the corresponding state code. The validation agent flags this as incomplete:
Rather than sending the issue back to the user, the orchestrator infers the missing data using the postcode in the residential_address field, injects the result into the workflow, and re-runs validation, ultimately reaching the scoring agent:
We ultimately end up with a dynamic flow looking like:
This dynamic back-and-forth, where the orchestrator selectively routes tasks, retries failed steps, and supplements missing context, is what makes agentic workflows so powerful. It’s not just about automating predefined tasks; it’s about enabling systems that can reason, adapt, and act in real time. The final workflow in n8n that achieves such functionality ends up looking like:
Scaling Beyond the Example
While updating a missing state code might sound like a simple task, it demonstrates a scalable pattern. With the right prompting and clear tool descriptions, these systems can scale from three steps to thirty, handling everything from basic data processing to nuanced multi-step interactions that span services, databases, and APIs.
That’s the core strength of agentic systems: they aren’t locked into a fixed number of nodes or rigid flows. They grow and adapt with the task’s complexity: automatically and intelligently.
Wrapping Up
As we’ve shown in this example, combining multiple agents with an intelligent orchestrator unlocks entirely new possibilities for workflow design. By leveraging tools like n8n, we can create systems that do more than follow a script; they respond to uncertainty, adapt to change, and make decisions with context in mind.
At exposé, we’re continuing to explore how agentic systems can reshape the way businesses automate and scale their operations. Whether it’s processing applications, surfacing insights, or orchestrating tools across the enterprise, we’re excited about the possibilities this unlocks.
To learn more about how we can help you with your AI, contact our friendly team for a no-obligation discussion HERE