Why Should AI Agents Be Specialists, Not Generalists (MoE in Practice)?
Shaked
Over the last few years, general-purpose AI agents have found their way into everything from customer support workflows to internal DevOps copilots. These AI workloads often involve diverse tasks from multilingual translation to code generation, which poses a choice between generalist models (one big model for everything) and specialist models (many targeted experts).
However, most workloads are built on generalist models that attempt to handle everything. At first, this approach may seem convenient. Still, it can cause technical debts and performance issues when these generalist models are deployed at scale, especially in production environments with real latency budgets and domain-specific edge cases.
The real debate here is, why should AI agents be specialists and not generalists? There are several reasons. One of them is spikes in latency when generalist models are used to resolve long context windows. Another is that decision quality drops in high-stakes situations such as access control or infrastructure management, where precision and reproducibility cannot be compromised.
In practice, generalist agents frequently overfit to the wrong intent, select suboptimal tools such as selecting APIs or function calls that aren’t ideal for the user’s actual intent, or fail altogether in ambiguous flows. This is not a prompt engineering issue, but a structural limitation.
These deployments make one thing clear. General-purpose agents try to do too much with a single model architecture. Fine-tuning provides short-term improvements in narrow domains, but maintaining multiple downstream variants or constantly updating task-specific weights creates overhead that does not limit growth.
That’s why specialists offer a better solution, especially those with a Mixture of Experts (MoE) architecture. These models often achieve 95% to 99% accuracy within their specific domains, such as medical imaging or fraud detection.
Moreover, MoE models activate only a small subset of specialized subnetworks, known as experts, for each input. This allows high model capacity without incurring the full inference cost. This design provides a modular and scalable foundation for building agents that behave predictably under load.
In this blog, we will learn more about how Mixture of Experts (MoE) is helpful, especially for AI agents. We’ll also look at some real-world examples to understand its impact better and to decide when to choose MoEs over generalists. Let’s get started.
Limitations of Generalists
Before understanding why your AI agent should be a specialist, we need to understand the limitations of why it shouldn’t be a generalist.
- Token Bloat: Handling multiple domains requires extended prompts and long context windows, often exceeding 8K tokens. This increases both inference time and cost.
- Sluggish Inference: Routing every input through a dense model results in non-deterministic latency, especially when chaining external tools or APIs.
- Unclear Boundaries: Generalist agents often mix intents and misuse internal tools, leading to poor reliability in high-precision tasks.
- Monitoring Complexity: Debugging missteps becomes harder, as there is no clear ownership of specific logic or decision-making paths.
Generalists vs Specialists
Generalist models are trained on broad data (common crawl, books, code) to handle diverse tasks. For example, GPT‑4, Claude, LLaMA‑2 70B are one model trying to “know everything.” In contrast, specialist models focus on a narrow domain or task. Examples include domain-tuned LLMs like Kubiya (DevOps), Med-PaLM (healthcare), or CodeLLaMA (code), each fine-tuned on specialized data.
Below is a comparative table illustrating the differences between generalists and specialists:
What is a Mixture-of-Experts (MoE)?
A Mixture of Experts (MoE) model is a type of neural network architecture where only a subset of the model, called "experts" or “subnetworks,” is activated for each input. This technique is referred to as sparse activation, which helps to distribute computation across multiple expert sub-networks.
This enables models to scale to billions (or trillions) of parameters while keeping inference and training compute within manageable bounds. Each expert is typically a feedforward block, transformer layer, or even a specialized module trained for certain input patterns.
Sparse Routing and Conditional Compute
MoE has great dynamic specialization, meaning different experts can learn to handle different domains or tasks. A gating network (often a small neural network) decides which experts to use based on the input.
All these processes work on conditional computation. Let’s understand this better: A gating network evaluates the input and selects a small number of experts (usually 1 or 2) to process that input, while the rest remain idle.
This sparsity reduces computational load and inference time without sacrificing model capacity. For developers, this means more compute-efficient architectures that adapt better to growing data volumes and complexity.
Architecture Overview
Let’s see what architecture Mixture of Experts follows. An MoE layer sits between standard transformer blocks. It contains:
- A gating network: a lightweight module (often a single-layer linear model with softmax or top-k selection) that scores and selects which experts to activate.
- N experts: independent feedforward sub-networks. Each expert is trained on a portion of the input distribution.
- Top-k routing: the gating network routes each token to the top-k scoring experts, and the outputs are combined, often weighted by the gate scores.
In a network configuration MoE, the input goes to a router, which selects a subset of expert sub-networks to apply. Only those expert parameters are used, making the layer’s compute proportional to “active” parameters rather than total parameters.
This design introduces conditional compute, where only a fraction of the full model processes each input. It improves throughput and allows specialization of different experts without a linear increase in compute cost.
Real-World Examples of MoE Specialists
To understand the impact of the Mixture of Experts model, it's essential to look at its real-world applications. These architectures are adopted across various industries and use cases, demonstrating advantages over traditional, monolithic generalist models. Let’s see the top real-world examples of MoE models in different domains:
Kubiya
Kubiya builds domain-specific AI agents for internal enterprise operations, such as DevOps workflows, approval automation, and infrastructure queries. Kubiya doesn’t follow a traditional MoE model internally, but its platform takes a modular, MoE-inspired approach to adapting AI agents.
Instead of relying on a single generalist agent, Kubiya routes tasks to specialized sub-agents based on context, permissions, and task type, mirroring how expert selection works in Mixture of Experts architectures.
At KubeCon + CloudNativeCon EU 2025, the team showcased an on-call engineering assistant built over Microsoft Teams. This AI agent dynamically routes incidents, queries, and alerts to specialized workflows behind the scenes, whether handling a CI/CD failure, pulling logs, or triggering infra changes.
Each agent operates with focused knowledge, such as infrastructure syntax, approval workflows, or GitOps state, which allows the system to scale across teams without becoming bloated or unpredictable. It’s MoE, not at the model level, but applied at the orchestration layer with clear gains in enterprise DevOps environments.
DBRX (Databricks)
DBRX is an open MoE model by Databricks with 132B total parameters, of which only 36B are active per forward pass. It uses a 16-expert MoE architecture with 2 experts activated per token.
DBRX has high performance-per-dollar across common LLM benchmarks and delivers inference efficiency suitable for enterprise deployment. It validates MoE’s scalability by maintaining strong performance at a fraction of dense model compute.
GShard and Switch Transformer (Google)
Google’s GShard introduced the idea of training massive, sparse models with expert routing. Switch Transformer extended this with a single active expert per input, reducing inference cost while outperforming dense counterparts in translation and language modeling tasks. These models are foundational to Google's deployment-scale infrastructure, showing how MoE supports production-scale efficiency.
NLLB-200 (Meta)
Meta's NLLB-200 focuses on multilingual translation across 200 languages. It incorporates Mixture of Experts (MoE) to specialize experts by language families and token types, ensuring performance doesn't degrade in low-resource settings.
The MoE design helps maintain translation quality without increasing compute for every language equally. This specialization would be infeasible in a dense model due to context overlap and parameter constraints.
MoE Specialists vs Dense Generalist Models
When comparing Mixture of Experts (MoE) specialists to dense generalist models, the trade-offs revolve around efficiency, specialization, performance scaling, and resource allocation. Here's a breakdown of the key differences and considerations:
Parameter Use
Dense models activate all parameters on every input, regardless of task relevance. MoE models activate only a subset, typically 2 out of 8 or 16 experts, leading to lower active compute despite higher total parameter count. For example, Mixtral uses 12.9B active parameters per token out of 45B total.
Inference Cost and Performance-per-FLOP
FLOP stands for Floating Point Operations. It is a metric used to measure the computational workload of a model, often in terms of the number of floating-point calculations required to perform a task, such as processing a piece of data.
In the context of AI models, Performance-per-FLOP refers to how efficiently the model performs a task relative to the amount of computation (FLOPs) it requires. The fewer FLOPs needed for a given task while maintaining strong performance, the more efficient the model is.
Log processing Mixture of Experts (MoE) models achieve higher performance per compute unit. By activating fewer experts, they reduce FLOPs while maintaining strong task performance. Dense models, in contrast, consume full compute capacity for every token, which limits scalability.
Specialization vs Uniformity
Dense models apply the same weights to all inputs, leading to average performance across diverse tasks. MoE allows each expert to specialize; some may focus on code, others on legal or customer queries, making the system more predictable and accurate in domain-specific scenarios.
Below is a table for better clarity:
Why Not Just Fine-Tune a Generalist?
One may think we can fine-tune a generalist for a specific domain, i.e., start with a strong generalist LLM and adapt it to specific domains (e.g., code models, medical models, enterprise chatbots). Fine-tuning can yield excellent domain models, but it has downsides:
- Duplication of parameters: Each fine-tuned model typically has the full base size. An enterprise must maintain and serve many large models if it supports many verticals. Mixture of Experts (MoE), by contrast, hosts all expertise in one model for different domains and shares parameters where possible.
- Inference cost: Serving multiple fine-tuned models means allocating GPU/CPU capacity to each, even when traffic is low. An MoE automatically routes requests to the relevant experts in one model, potentially reducing total resources.
- Data efficiency: Fine-tuning risks overfitting or catastrophic forgetting of the base knowledge, unless done carefully. An MoE’s experts can specialize without interfering with each other.
When to Choose MoE over Dense or Fine-Tuned Models
When deciding between Mixture of Experts (MoE) and traditional dense or fine-tuned models, it's important to consider the workload's complexity and scale. Below, we’ll highlight scenarios where MoE excels over dense models and fine-tuned alternatives.
Workloads with Domain Diversity
If your AI workload spans multiple domains, such as code generation, mathematical problem-solving, or machine translation, MoE is an ideal choice. MoE models like Kubiya can activate different experts for specific tasks, allowing them to efficiently handle various domains within the same architecture. For example, a specialized expert can handle Python queries in a code generation scenario, while another can focus on JavaScript or C++.
Cost-effective Inference at High Scale
For applications requiring high-scale inference, MoE models provide cost-efficiency. Since only a subset of experts is activated per task, the computational overhead is significantly lower compared to dense models.
This allows faster processing at a lower cost, making MoE models particularly suitable for large-scale deployments, such as chatbots, recommendation systems, or customer support.
Serving Multiple Use Cases with One Model
Mixture of Experts (MoE) serves multiple use cases without needing separate fine-tuned models. A single MoE model can be trained to handle different tasks by activating the appropriate experts. So, teams can reduce the complexity and cost of maintaining several specialized models for different tasks, making it an adaptive solution for enterprises that must address diverse requirements.
Avoiding Maintenance Overhead of Many Fine-Tuned Models
Maintaining multiple fine-tuned models for specific tasks can be resource-intensive and challenging. MoE reduces this burden by allowing a unified model to specialize dynamically. This removes the need for continual retraining and maintenance of individual fine-tuned models for each task or domain, simplifying model management and improving efficiency in deployment.
So, MoE modals like Kubiya are the perfect choice when scale and diversity are top priority. They trade higher memory costs (all experts loaded) for much lower compute costs and task-specific accuracy.
For senior developers and architects, the takeaway is that designing AI agents as collections of experts can yield superior scalability and performance. Rather than one monolithic model, building an MoE with routing logic lets you scale capacity cheaply and infuse domain expertise directly.
The increasing adoption of MoE in production (Kubiya, Google, Meta, NVIDIA, Databricks, etc.) suggests this is more than a niche idea; it’s a core architectural pattern for next-generation AI systems.
Conclusion
While generalist AI models may seem convenient, they face significant challenges when deployed at scale, particularly in domain-specific applications. These models often struggle with latency issues, unclear intent handling, and performance degradation, especially in critical tasks requiring high precision.
The Mixture of Experts (MoE) architecture provides a compelling solution, reduces computational load, improves inference efficiency, and supports scalability without compromising performance. MoE models, as seen in real-world applications like Kubiya, DBRX, and Google's GShard, show how specialized AI agents can outperform traditional generalist models.
FAQs
1. What is MoE in LLM?
MoE (Mixture of Experts) in LLM refers to a model architecture where only a subset of specialized experts is activated for each input. This allows the model to focus resources on the most relevant experts for a given task, improving efficiency and performance compared to traditional dense models.
2. What is the difference between MoE and sparse MoE?
While both MoE and sparse MoE use multiple experts, the key difference lies in the number of experts activated. MoE models often use a larger number of experts for each task, which can be computationally intensive. Sparse MoE, on the other hand, activates only a small subset of experts, reducing computational costs without significantly compromising performance.
3. What is the concept of MoE?
The core idea behind MoE is to use a set of specialized experts, where only a few are activated for each input. This makes the model more efficient, as it doesn't require activating all experts for every task, optimizing both performance and computational resources.
4. What is the difference between generalists and specialists?
Generalists are models or experts capable of handling a wide range of tasks, but may not be as efficient or optimized for any specific task. On the other hand, specialists are experts trained to perform particular tasks with high efficiency, making them more effective for specific use cases.
5. What is the objective of MoE?
The primary goal of MoE is to enhance the performance of large models by activating only the necessary experts, thereby reducing computational overhead while maintaining strong performance. This makes MoE models more scalable and efficient, especially for complex tasks.