The word “agent” gets thrown around a lot right now. If you string two API calls together, someone is going to call it an autonomous AI agent. But if you’ve actually tried to build a system that you can run in production, and get real work done without constant hand holding, you know this is not going to cut it. Building production ready agentic workflows requires a specific architecture. Over a lot of customer engagements Aviato have found it easiest to think about this stack in four distinct layers plus the underlying plumbing that keeps it all from exploding in production. Here’s a practical look at how the modern agentic stack is actually built, and what you need to productionise AI Systems: Layer 1: Large Language Models (LLM’s) At the absolute bottom of the stack sits your foundation model. This is where you’re dealing with the raw mechanics: pinging APIs, handling tokenization, tweaking inference parameters, and prompt engineering. You give it instructions, and it responds. On its own, it doesn’t care about your long term objectives, it forgets what happened five minutes ago, and it definitely can’t orchestrate a complex, multi step workflow. To get that, you have to move up the stack. Layer 2: Agents This is where we take a reactive model and actually turn it into an agent. We’re wrapping the LLM in code that gives it persistence, structure, and a goal. Instead of just answering a question, a Layer 2 agent can actually pursue an objective. To make that happen, you have to bolt on a few things: Unlike a standalone model, a Layer 2 agent acts, looks at the intermediate result of that action, and adapts its next move based on what just happened. Layer 3: Multi-Agent Systems Eventually, you’re going to give a single agent a task that’s simply too big. The context window is exhausted, it loses focus, and the whole thing falls apart. That’s when you need to bring in a multi-agent system. Instead of writing one prompt to rule them all, you build a distributed team of specialist sub agents. This layer handles the collaboration between them, including: By splitting up the work, the whole system becomes drastically faster, more robust, and way less prone to hallucinating under pressure. Layer 4: Agentic Ecosystems When you have a bunch of specialized agents running around asynchronously, things turn into chaos fast. Without structured orchestration, a multi-agent setup is just a cool local demo. With it, you get a scalable, reliable system that can actually survive real-world constraints. For a reliable production system you need: These are not sexy, but are how you ensure accountability, mitigate failure modes, and actually preserve trust in the automated decisions your software is making. Aviato have run a number of PoC’s our current cost is 6 weeks and 80k AUD, to prove an agentic system can meet your needs. Moving these to production requires a team (or Aviato SRE’s) to manage them, and a lot of additional thought.