Latest   Machine Learning Principles for Building Model-Agnostic AI Systems 1 like April 25, 2025 Share this post Author(s): Justin Trugman Originally published on Towards AI. While individual AI models dominate headlines, the spotlight often misses where true progress happens: the systems that put those models to work. Each new release promises more nuanced understanding, deeper reasoning, richer comprehension — but capabilities alone don’t move industries. It’s the architecture that wraps around them, the orchestration layer that knows when and how to use them, that turns raw potential into applied intelligence. “I think in the long term, the most value will not come from the foundation models themselves, but from the systems that can intelligently call foundation models.” — Andrew Ng The next wave of AI breakthroughs won’t come from betting on the right model. They’ll come from building systems that can continuously integrate the best model for the job — whatever that model is, and whenever it arrives. Understanding the Building Blocks of AI Models Before designing any model-agnostic system, it’s important to understand what actually goes into a model. AI models aren’t just standalone entities — they’re built in layers, each contributing a different dimension of capability. You typically start with a base architecture. Most models today — especially those that handle text, tool use, or autonomous agent behavior — are based on transformers. These are the underlying neural network designs that make modern language models possible. If you’re working with visual generation, like images or video, you’re more likely dealing with diffusion models, which are optimized for high-fidelity synthesis through noise and denoising processes. On top of the architecture, you then define the scale and scope. A Large Language Model (LLM) refers to a model with dozens of billions (sometimes hundreds of billions) of parameters, enabling broad, generalized capabilities across tasks. A Small Language Model (SLM) is a scaled-down version — lighter, faster, and often used for edge deployments or specific roles where compute efficiency matters more than versatility. Once you have your base model, you can tailor it to specific domains or behaviors through post-training, commonly referred to as fine-tuning. Fine-tuning allows a model trained on general data to specialize in law, healthcare, finance, or any other area where nuanced understanding is critical. It’s also how instruction-following and tool-use behaviors are often reinforced. From there, models can be extended with architectural practices or runtime techniques. A model might adopt a Mixture of Experts (MoE) approach, dynamically routing queries to different subnetworks based on the task. Or it might feature enhanced reasoning capabilities, such as chain-of-thought prompting, multi-step logic execution, or even structured planning frameworks. These capabilities allow the model to go beyond surface-level outputs and begin engaging in more deliberate, process-driven problem-solving. Finally, you have specialized capabilities layered on top. A model might be multimodal, meaning it processes and generates across text, image, and audio inputs. It might combine different generative architectures — like transformers for text and diffusion for visuals — to handle diverse output modalities. These layers don’t exist in isolation — they compound. And understanding how they stack is foundational to building systems that know what kind of model to use, where, and why. Blueprints for Building Adaptable, Model-Agnostic Architectures Designing a model-agnostic system means building for constant evolution. Models will change. Capabilities will shift. Your infrastructure needs to keep up without requiring a rebuild every time something new comes along. The first principle is decoupling logic from inference. This means separating the definition of a task from the model that executes it. Your system should understand the task that needs to be done — without baking in assumptions about how it gets done. That choice — what model to use for that task — should be abstracted out so that it’s easy to switch between models without rewriting the system’s logic. Many modern inference providers have aligned on the OpenAI-compatible API standard (e.g., OpenAI, Anthropic, Groq, HuggingFace and others), which makes it easier to build systems that can flexibly switch models without changing the surrounding infrastructure. Designing around this standard helps ensure your system remains portable and compatible as the ecosystem grows. It’s this layer of abstraction that enables true model-agnostic design — giving your system the ability to evolve, adapt, and scale without being anchored to any single provider or model lineage. The next principle is treating models as specialists, not generalists. Every model has its own strengths — some are better at planning, others at creativity, some excel in reasoning, and others in speed or low-cost inference. Your system should be designed to route tasks to the model that’s best suited to handle it. This may mean assigning specific models to specific functions, or designing agents with models optimized for their assigned roles in a multi-agent system. For example, a fast, efficient planner might use a small reasoning model; a writer or content generator might use a highly expressive LLM; a fact-checking agent might use a more literal model with lower variance in output. Whether it’s routing tasks directly to models or delegating them to agents with purpose-built model stacks, this approach acknowledges that no single model can do everything well — and that the highest-performing systems intelligently delegate tasks in ways that respect and leverage each model’s unique strengths. Modularity means building systems where each component can be independently swapped or upgraded. Whether you’re dealing with a workflow, a multi-agent system, or something entirely custom, the principle stays the same: no single component should create friction for the rest of the system. When planning a module — whatever the function or responsibility — it should be consumable in isolation and replaceable without downstream disruption. This allows your system to evolve incrementally as new tools and models emerge, rather than forcing wholesale rewrites just to integrate something better. The final principle is observability. If you can’t measure how well a model is performing in context, you can’t make informed decisions about when to keep it, replace it, or reconfigure how it’s being used. Model performance should be treated as a live signal — not a one-time benchmark. That means tracking metrics like latency, cost, token efficiency, and output quality at the system level, not just during eval runs. Is a cheaper alternative producing comparable results in certain contexts? Are reasoning agents making consistent errors under certain loads? Telemetry is what turns gut checks into data-driven decisions. It’s what gives you confidence to experiment — and evidence to justify when a change actually makes things better. Designing systems this way sets the stage — but actually choosing the right model for each role requires careful evaluation, not guesswork. Evaluating and Testing Models for Fit Building a modular, model-agnostic system only pays off if you have a clear, structured way to evaluate which model belongs where. It’s about finding the right model for each specific function within your system. That requires moving beyond general benchmarks and looking at how models behave in your context, under your constraints. Start by assessing output consistency. A model that performs well in a vacuum but produces unstable or hallucinated results under pressure isn’t viable in production. You’re not just testing for correctness — you’re evaluating whether the model can behave predictably across similar inputs and degrade gracefully in edge cases. Next, evaluate performance in the context of your system through A/B testing. Swap models across real user flows and workflows. Does a new model improve task success rates? Does it reduce fallbacks or speed up completion times? System-level testing is how you reveal performance trade-offs that aren’t visible in isolated prompts or benchmarks. A useful tool for running these kinds of evaluations is PromptFoo, an open-source framework for systematically testing LLM prompts, agents, and RAG workflows. It lets you define test cases, compare model outputs side-by-side, and assert expectations across different providers. It helps turn model evaluation into a repeatable process rather than an ad-hoc exercise. Not every evaluation is universal — some depend on the specific capabilities your AI system is built to support. Two areas that often demand targeted testing are tool use and reasoning performance. If your AI system revolves around tool calling, it’s important to evaluate how well a model handles zero-shot tool use. Can it format calls correctly? Does it respect parameter structures? Can it maintain state across chained calls? Some models are optimized for structured interaction, while others — despite being strong at open-ended generation — struggle in environments that require precision and consistency. For systems that depend on complex decision-making, reasoning performance becomes a critical axis. Can the model follow a chain-of-thought? Break down a problem into substeps? Resolve conflicting information? These evaluations are most useful when they mirror your actual workflows — not when they’re pulled from abstract reasoning benchmarks that don’t reflect real-world demands. Evaluating a model’s capabilities is only half the picture. Once a model looks viable functionally, the next question is: can your system run it efficiently in production? Start with inference latency. Some models are inherently faster than others based on their architecture or generation behavior. But just as important is where and how the model is hosted — different providers, runtimes, and hardware stacks can significantly affect speed and responsiveness. Then consider token usage and cost efficiency. Some models are more verbose by default, or take more tokens to arrive at a meaningful answer. Even if the model performs well, inefficient token usage can accumulate into significant costs at scale. These operational realities don’t determine which model is the most capable — but they often determine which one is actually deployable. The pace of model development isn’t slowing down — it’s accelerating. But chasing the latest release won’t give your organization an edge. The real advantage lies in building systems that can flex, adapt, and integrate whatever comes next. Model-agnostic systems aren’t about hedging bets — they’re about making better ones. They allow you to continuously evaluate and adopt the best tool for each job without rewriting your stack every quarter. They support experimentation, specialization, and modular upgrades — all without breaking what’s already working. In the long run, the intelligence of your system won’t be defined by which model you chose today — it will be defined by its ability to continuously adapt and integrate the right model as new ones emerge. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post