It can be hard to keep track of all of the buzzwords flying around as the pace of AI innovation and adoption continues to accelerate.  Add to that the noise from critics and enthusiasts (AI is overhyped, AI is a waste of money!, AI is the future of humanity, and every piece of software will be replaced with AI!) and it is easy to lose sight of how AI is actually changing business in progressive and practical ways.

Lately, there’s been a lot of talk about AI Agents and Agentic AI. And while the tech industry loves coining new terms and making you feel like you’re falling behind, we’re going to break things down. Let’s explore the evolution of AI, with a focus on how these new technologies are challenging traditional software development.

What are AI Agents?

So, what exactly is an AI Agent? While definitions vary, and every AI platform (including Contextual.io) has its own take, here’s a simplified version: an AI Agent combines an AI model or assistant that can generate insights or conclusions, flexible functions that the AI can choose to perform, and the technology to execute those actions, with feedback looping back into the system.

In short, it’s more than just an AI making decisions—it’s an AI taking action, crossing different systems, data sources, and platforms, and making real-world things happen. This is what Contextual’s AI Agent Orchestration platform does at its core, so we may be a bit biased.

How AI Agents Work : Real-World Example

Let’s take a practical example. Imagine we’re an infrastructure provider responsible for monitoring critical structures like bridges and telephone poles. We’ve got sensors installed on the guidewires that collect data—temperature, stress, vibration, and more.  

Contextual’s AI Agent solution might include an OpenAI Assistant, enhanced with retrieval-augmented generation (RAG) capabilities, that uses the sensor data to analyze the guidewires. These sensors continuously feed data, such as temperature, stress, vibration, and even audio events, directly into the system for real-time feedback.

The AI Assistant can be regularly fed new sensor data and tasked with determining whether any patterns appear anomalous. Current models, like OpenAI’s o1, can easily process raw data, perform statistical analysis, and generate conclusions. It might even pull data from a third-party source to provide context—like checking if a major shipping container was passing under the bridge at the time, which could explain the deviation.

That said, at its core, this is just math—anomaly detection. It’s an AI-assisted monitoring solution, but it’s still just a workflow. To truly become an AI Agent, the system needs to make active, independent decisions without human involvement.

The first independent action the AI Assistant might take is to perform a visual inspection. Let’s say we have a fleet of drones stationed near critical infrastructure. The OpenAI Assistant determines that additional information would help explain the data variance. It then makes a function call to request a drone flyby of the bridge for a visual and thermal inspection. (Fly here, take pictures, return to dock, upload the data.)

The drone captures several images—both visual and thermal—which are then processed by a visual classification machine learning model, perhaps from a provider like Nyckel. This model has been trained on thousands of images of guidewires in various conditions: normal operation, under stress, in high winds, during heatwaves, and more. The model returns a classification, perhaps indicating: "normal under these conditions."

But the AI Agent’s work isn’t done yet. Since it initiated the drone inspection, it receives the new information and can use it to make the next decision. Based on all the data it now has, it determines whether everything is fine or if there’s cause for concern. If there's a problem, the AI Agent issues another function call, creating an emergency work order. A team is dispatched immediately to inspect the situation and make any necessary repairs.

What’s important here is the ‘Agency’ of the AI Agent—meaning its freedom to operate—isn’t self-contained. It’s a system. It crosses data sets, models, platforms, and integrations. It can compel action outside of its boundaries that is not explicitly defined in workflow software (which would just be a series of if/then steps). This is where AI Orchestration comes in.

The Essential Role of AI Orchestration

In the example described above, the AI Assistant has the ability to execute actions outside of its defined boundaries. If it wants to retrieve recent shipping information, it can request that. Of course, to do that something (some system - some platform) must receive the request, process the request, connect to the third party system, retrieve the requested information, likely format the data in a manner the AI is expecting, and pass along the results. Likewise managing the coordination across the multiple AI models described above (the AI Assistant and the machine learning visual classification model) requires hand-offs, staging, asynchronous connection, and data transformation.

AI Orchestration sits at the center of this federated data (both data internal to the organization building the AI Agents and sources of data externally), multiple models, connections, and the structured workflows that bridge them all. 

‍“AI orchestration refers to the process of coordinating and managing the deployment, integration, and interaction of various artificial intelligence (AI) components within a system or workflow.” - Pure Storage

In our scenario, when the AI Agent wants to call a function, someone has to be listening, ready to receive that function in the format that the AI is using, and have the systems in place to execute on that function. Otherwise, the AI is simply screaming into the void.

This is where Contextual comes in. By providing a visual AI orchestration layer, Contextual simplifies the process of defining your AI Agents, capturing, transforming, and optimizing the data those Agents need to operate, receiving the instructions that give the AI ‘agency’, and seamlessly executing those requests. If you’re ready for AI Agents, you need AI Orchestration.

The Future of Software 

There are many pundits talking about how the future of software is agentic, and companies like Salesforce are at the forefront. (Though they’ll charge you an arm and a leg for it—that’s a discussion for another time.) Still, the reality is that, over time, complex workflow-based software will be replaced by agentic AI … (gasp…we did it). Imagine AI systems that can decide to issue a refund based on sentiment analysis when a customer is really upset. Or ones that can place a purchase order because they’ve predicted supply chain delays. And yes, even AIs that can send out a drone to inspect a bridge.

For companies looking to embrace this future, the key is to start small and focused. Unlike Klarna, which plans to replace Salesforce and Workday entirely with AI, it’s smarter to focus on one specific task, workflow, or process that can first be enhanced, and eventually automated by an AI Agent. If there’s an area that requires a lot of human intervention because the rules aren’t always clear (and can’t easily be written into traditional software), that’s where an AI Agent can really shine. Once trained on why certain decisions or actions were made in the past, the AI can start replicating that same logic chain. Plus, by starting small, it’s easier to measure the ROI, which helps justify further AI investment.

If you’re ready to start embracing AI across your organization—and, to be clear, you should be—get in touch. We can help you implement everything from simple AI-enhanced workflows to fully autonomous AI Agents. 

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