Compound AI Systems: Why Architecture is the New Frontier

For the last couple of years, the tech world has been waiting for the next giant model to fix what frustrates us about LLMs—hallucinations and logic gaps. That fix hasn’t arrived. What has become clearer, though, is that the power isn’t in the next model, but in the architecture we could build around the current one. We are moving toward a mature engineering discipline and are no longer just users of models; we are architects of Compound AI Systems.

In this article, Tomas Vilčinskas, Engineering Manager at CyberCare, outlines three shifts that support this transition.

1. The model as a logic engine

A common sticking point is expecting an LLM to “know” everything. In a production environment, treating the model like a database is a recipe for disaster. Instead, we are treating the LLM as a Logic Engine or something along the lines of a CPU.

In that setup, the model doesn’t keep facts; it coordinates how to get them. It reaches out to a vector database for memory, triggers an API for real-time data, and hands off complex math to a Python interpreter.

• The shift: Optimize the links between the model and its tools, not just the model itself.
• The result: Reliability comes from system design. You don’t hope the model remembers a fact; you build the pipeline to ensure it fetches it.

2. A new vector of impact: trading time for intelligence

This system-centric mindset also changes how we think about performance. Instead of optimizing purely for speed, as in traditional software, we can now treat Computation Time as a design variable.

Just as adding “Memory” (via RAG) improves the model’s output, allowing for longer inference compute (test-time scaling) provides a massive boost in reasoning quality.

However, this introduces a critical engineering trade-off:

• The Reasoning Vector: Giving a model “thinking time” (like OpenAI’s o1 or DeepSeek-R1) allows it to verify its own logic and explore multiple paths before answering.
• The Latency Constraint: In time-sensitive use cases (like a customer service voice bot), you cannot afford a 30-second “reasoning” pause.

The Engineering Challenge: If your use case is latency-sensitive, you can’t just wait for the model to “think.” You must compensate in other ways— by using a more specialized, fine-tuned, smaller model or by pre-fetching context to reduce the “cognitive load” on the LLM during the response.

3. Prompts are the code

In standard software, code is a set of static instructions. In an AI-driven world, prompts are the code. We moved from Machine Code (binary) to Assembly, then to high-level Programming Languages like C and Python. LLM Prompt feels like the newest layer in that stack.

Unlike traditional code, though, agentic prompts are dynamic. Within an action loop, the system can:

1. Observe: “I tried to run this SQL query, but the column name was wrong.”
2. Reflect: “The error message suggests the column is actually named user_id, not id.”
3. Rewrite: The model generates a new prompt for itself, effectively rewriting its own “logic” to fix the bug in real-time.

This creates a system that can dynamically adjust its logic depending on the task. We are building autonomous runtimes that can debug their own execution paths until the goal is met.

The New Engineering Mindset

The “magic” of AI is being replaced by the rigor of engineering. The winners of this next phase might not be those who have access to the biggest training clusters, but those who can build the most resilient systems around the models we already have.

We’ve stopped asking, “What can this model do?” and started asking, “How do I architect a system to solve this problem?” The training run provided the engine; now, it’s time to build the car.