Latest / TeqTalk with Jas / Enterprise AI Architecture Explained in 13 Minutes
Transcript
- Teqfocus: Welcome to Tech Talk, where we explore practical developments across AI, data, cloud, and enterprise technology. And more importantly, what those developments actually mean for organizations trying to put them into production. The objective is to go beyond the buzzwords and look at the architectures, operating models, real-world use cases, and challenges that technology and business leaders are dealing with today. In this episode, I want to focus on an important shift happening in the enterprise AI, moving from workflow automation to intelligent decision systems. Enterprises already have mature CRM, ERP, service platform, data platforms, APIs, integration layers, and thousands of workflows are moving from one system to another. These systems are extremely good at executing predefined processes. But execution and intelligence are not the same thing. What AI introduces is the ability to bring context, reasoning, and judgment into special decision points inside those workflows. So the enterprise question is starting to evolve from how do I really automate this workflow to how do we operationalize intelligence inside the workflow in controlled, governed, and measurable ways. Let's first see the evolution of intelligence. If you look at enterprise technology over time, there was an interesting evolution in where intelligence actually lives. Initially, most intelligence lived with people. People understood the customer, interpreted policies, handled exceptions, made decisions, and then started encoding more of that knowledge into software, then into the business rules, and later into the statistical model in machine learning. ERP and CRM systems standardize the process. Cloud and APIs connect them, and RPA allowed organizations to automate the repetitive work across those systems. Now with Gen AI agents and tool using systems, we are reaching another inflection point. Intelligence can increasingly operate inside the workflow itself. The system can receive a signal, understand the context surrounding it, evaluate with different options. Apply business policies and participate in deciding what should happen next. That changes the unit of work. Historically, systems primarily optimize the tasks, transactions, and processes. Increasingly, AI systems are being asked to participate in decisions and outcomes. One thing I want to make very clear is that this doesn't mean that we are replacing every deterministic workflow with an LLM. That's not an idea. In fact, that would often be the wrong architecture. Traditional automation remains incredibly valuable. If the answer is known, the rules are stable, the next step is predictable, go ahead with that. AI becomes valuable when the workflow reaches a point where context materially changes the outcome, where there are exceptions, incomplete information, completing objects, or some level of judgment is involved. Once AI starts participating in the enterprise decisions, the architecture becomes much broader than simply putting an LLM in an application. I like to read this architecture. If you see this architecture from left to right, Business event occurs, the system assembles the right context. The AI reasons inside the controlled harness. The decision becomes a governed action. And then we capture what happened so that the system can improve. Everything starts with a trigger or business event. It could be a refund request, a payment failure, an inventory change, a service escalation, a capacity alert, or a change in customer behavior. That even normally enters an existing application such as Salesforce ServiceNow. An ERP platform or any other internal portal or platform. Most of that workflow can and often should continue through the deterministic automation. But eventually, we may reach a point where the next action cannot be decided through a simple rule. This is the decision point where the intelligence is required. And before the model can reason, the first thing we need is decision-ready enterprise context. A model doesn't automatically know your business. It doesn't inherently know the customer's contact, account history, entitlement, transaction history, previous interactions, companies' policies, authorization levels, inventory positions, or what happened with the workflow. So the system has to assemble the relevant context for this particular decision. That context may come from your API, structured databases, enterprise applications, document through racks, sementec model. workflow state or the real-time events. And one important architectural principle here is that more context is not necessarily the better context. We want the most relevant, trusted, current and permission appropriate information required to make this specific decision. Once the context is assembled, we moved into the agent harness. I consider this as one of the most important layers in the enterprise agent tech architecture because The model provides reasoning capability, but the harness makes the reasoning operational inside the enterprise. And it has got several layers. The first is context assembly. If you want to know more about the harness and harness engineering, I have recorded a separate video that has got all the layers of harness, and I will be pasting the link below in the description. If you're interested, you can go ahead and watch that video. Inside this architecture also sits a model layer. It could be your Claude, GPT, Gemini, Lama, or any other model. Different models may be participated in different parts at the same time in the workflow. So the important principle is that model layer should remain pluggable by design. Models are going to evolve rapidly as you can see the current situation. Organizations shouldn't have to redesign their workflow architecture based on the model. The architecture should be the same and like how I mentioned the model should be pluggable. And once the reasoning is complete, we move on to governed action. This is the moment when AI stops simply analyzing something and it starts affecting the business. The action might be to update a record, issue a refund, send a notification, create a case, invoke another system. And this is where we establish bounded autonomy. And finally, we have the feedback and observability loop. The action should never be treated at the end of the system. We need to capture what happened. Afterwards. As the technical level, that includes things like latency failures, retries, performance costs, execution traces. But we also want to understand what happened to the decision itself. Was the recommendation accepted? Was it overridden? Did it comply with the policy? Did the customer respond as expected? Did the intended result actually occur? That evidence can be used to improve the system. And importantly, improvement doesn't always mean. retraining the model. Often the biggest gains come from changing the system around the model, improving the context, changing the prompt, updating the policy threshold, selecting better tools, improving routing or changing the approval process. Underneath this entire architecture is enterprise foundation such as identity and access. Auditability, compliance, monitoring, tracing, cost managing. So if you want to remember the architecture in one simple flow, it is event gets created at decision point. Context makes the decision informed, the harness makes the reasoning enterprise ready, the model moves intelligence, governance controls the action, and the feedback loop tells what happened afterward. So we need to understand the workflow end-to-end. It's not only about one agent working. The whole business workflow needs to be understood. And once we understand the workflow end-to-end, we can ask a much important question, which is How should we measure whether AI has actually worked? I think about that across three layers. First is system intelligence. Did the workflow execute correctly and efficiently? Here we care about latency, availability, API and tool failures, retries, timeouts, transaction success, token consumption, compute usage, and overall cost. The second layer I define is decision quality. Now we are asking whether the AI behaved correctly. Was the decision grounded in right evidence? Did it call the correct tool? Did it follow the policy? Did it stay with the appropriate guardrails? Was human approval required and obtained? Was the recommendation later over it? And then the third layer comes, which is business outcomes. Did the decision actually create a result in the organization that they really wanted? Depending upon the workflow, that might mean Better retention, protected revenue, improved SLA, reduced cost to serve, higher productivity, improved customer experience, increased conservation, or reduced risk. So these are the three different measures of success. A technically successful execution doesn't guarantee a good decision. And a good decision doesn't always automatically guarantee business value. Connecting those three dimensions System performance, decision quality, and business impact is what we call outcome intelligence. And this is exactly where Tech Focus Agently AI comes into the picture. At Tech Focus, we have built agently AI around the simple idea that enterprises already generate enormous amount of AI telemetry, but those signals are often disconnected from actual business process. And from the result that the process is supposed to produce. Agently AI is designed as outcome intelligence layer across enterprise AI workflows. Instead of only looking at an individual agent or a model, agently looks at the workflow and the connected views. The first view is system intelligence. How did the workflow run? Which agents, tools, APIs, automation, and systems participated? Where did failures or delay occurred? What was the execution cost? Where was the weakest step? The second view is decision quality. Was the decision itself sound? What evidence supported it? Was it grounded in the appropriate context? Did the decision comply with policy? Was the correct approval process followed? Did the system operate within the level of autonomy it had been given? And the third view is business outcome. What happened because of that decision? Did resolution improve? Did the customer stay? Did the revenue improve or remain protected? Did cost decrease? Did SLA performance change? Was risk reduced? The important thing here is that success is workflow specific. A customer support process doesn't have the same definition of success as a finance process or a revenue process. For a support workflow, success might mean resolution, customer retention. For finance, it may look like risk compliance, cost. For revenue, it may mean conversion renewal protected ERR. So each workflow needs a clear business objective, relevant KPIs, policy and risk criteria, and an established baseline. And that gives organizations a way to evaluate based on what the workflow was actually designed to accomplish. Rather than just relying on one generic agent success metric. As enterprises put more AI into operational workflows, I don't think maturity will ultimately be measured by how many agents an organization has deployed. A much more meaningful measure is how effectively those systems participate in business decisions and how safely they operate within the enterprise boundaries, and what measurable results they create. Traditional observability will continue to be essential, but the next layer is connecting technical execution with decision behavior and business performance. So that the organizations can identify where AI is actually creating value, where the risk or friction is accumulating, and where improvements should be prioritized. So that is the idea behind outcome intelligence, and that is the direction we are taking with Tech Focus Agently AI. So I'll leave you with one question: Where does intelligence need to live inside your business? And how will you measure whether it is delivering? the outcome you intended. Thank you so much for watching and I'll see you on my next episode.