AI Agent Architecture in the Era of Near-Zero-Cost LLMs: Evolution and Engineering Practices

In recent years, the engineering deployment of AI agents has undergone a profound shift from “high-cost trial and error” to “near-zero-cost normalization.” As the capabilities of top-tier large language models approach or even reach practical boundaries, compute cost and response latency have become the last two barriers limiting agent adoption. Recently, the “free intelligence” model jointly promoted by the open-source community and leading model providers has signaled that the singularity of a steep drop in agent invocation costs has arrived.

This phenomenon is not merely commercial subsidization—it is the inevitable result of deep alignment between underlying technical architectures and agent application scenarios. This article sets aside specific commercial brands and, from the perspective of general technical architecture and engineering practices, provides an in-depth analysis of the core technical logic driving AI agents toward low cost and high fluidity.

1. Breaking Through Cost and Latency: Why MoE Became the Top Choice for Agents

When building agents, one of the biggest pain points developers face is “perceived lag.” Agents typically need to intercept and respond to users’ daily operations (such as terminal command execution or text polishing) in real time, which requires the model to have extremely low time-to-first-token (TTFT). Traditional dense models, when their parameter counts reach hundreds of billions or even trillions, incur enormous inference compute overhead and struggle to meet real-time interaction demands.

Against this backdrop, Mixture-of-Experts (MoE) architecture has become the core technology that breaks the impasse.

1. MoE’s Sparse Activation Mechanism
Unlike dense models that activate all parameters on every inference, MoE architecture divides the model into multiple specialized “expert” networks. During each inference, the routing mechanism selectively activates only a small subset of experts based on the characteristics of the current token. For example, an MoE model with a total parameter count in the trillions may activate only tens of billions of parameters in a single forward pass.

This sparse activation mechanism delivers two direct benefits:

• Dramatic reduction in compute overhead: Computation drops from “trillion-level” to “tens-of-billions-level,” greatly reducing the FLOPs required per inference. This significantly lowers cloud inference costs and provides the commercial feasibility for near-zero-cost invocation.
• Leap in response speed: Because the number of parameters actually involved in computation is substantially reduced, inference time shrinks markedly, and TTFT is greatly compressed, giving users a “local software-like” smoothness.

2. Improved Concurrent Throughput
For cloud model service providers, MoE architecture can also improve overall throughput through dynamic batching. Since different requests activate different experts, the system can utilize GPU memory and compute units more efficiently, thereby serving more concurrent requests on the same hardware scale and further diluting marginal costs.

2. Engineering Persistent Memory: Long Context and Caching Benefits

Beyond latency and compute cost, another major challenge for agents is “persistent memory.” Agents need to remember a user’s system settings, operating habits, historical instructions, and other long-term context in order to provide personalized service in subsequent interactions. If all of this massive context is re-fed into the model for computation on every request, it will not only cause token consumption to surge but also multiply response time.

The best engineering practice for solving this problem is combining long context windows with native context caching technology.

1. Breaking Through Context Window Limits
Modern large models commonly support context windows of hundreds of thousands or even millions of tokens. This allows agents to send the complete system prompt, historical operation logs, and current task description to the model in one pass, without relying on complex and error-prone external Retrieval-Augmented Generation (RAG) strategies. Long context not only improves the model’s understanding of complex tasks but also simplifies the agent development pipeline.

2. Context Caching Mechanism
To further reduce the compute cost brought by long-term memory, the industry has introduced context caching technology. Its core logic is: for prefix prompts that remain unchanged across multiple turns of dialogue—such as system settings and personal preferences—the model retains the computation results (such as KV Cache) in GPU memory during the first inference. Subsequent requests only need to send the changed portion, and the model can directly reuse the cached data in GPU memory.

Below is a pseudocode example of using caching to optimize agent invocation:

class AgentContextManager:
    def __init__(self, system_prompt, user_preferences):
        # Long-lived, unchanged context as prefix
        self.static_prefix = f"{system_prompt}\n{user_preferences}"
        self.prefix_cache_id = None
    def send_request(self, dynamic_task):
        if self.prefix_cache_id is None:
            # First call: send full prefix and request caching
            response = llm_api.generate(
                prompt=f"{self.static_prefix}\n{dynamic_task}",
                enable_caching=True
            )
            self.prefix_cache_id = response.cache_id
        else:
            # Subsequent calls: send only the dynamic part, reuse GPU memory cache
            response = llm_api.generate(
                prompt=dynamic_task,
                use_cache_id=self.prefix_cache_id
            )
        return response.text

In this way, the invocation cost of long-term memory drops to nearly zero, and response speed is further accelerated. According to industry estimates, the comprehensive usage cost of adopting long context combined with caching is only about one-fifth that of comparable top-tier models without this approach.

3. Cloud Brain and Local Gateway: The Physical Logic of Agent Architecture

To achieve a truly seamless experience, model capability alone is not enough—reasonable system architecture design is also required. Today’s mainstream agents generally adopt a distributed architecture of “local gateway + cloud brain.”

1. Local Gateway: Lightweight Interception and Routing
The local gateway is typically a lightweight client deployed on the user’s device (such as a personal computer or workstation). Its main responsibilities include:

• Environment awareness: Listening for user operation intents (such as terminal command input or code editor events).
• Preprocessing: Desensitizing sensitive data and assembling current environment variables and context.
• Network communication: Establishing efficient long connections with the cloud brain, such as using gRPC or WebSocket protocols for data transmission.

2. Cloud Brain: Complex Reasoning and Decision-Making
The cloud hosts the large model built on MoE architecture. The local gateway sends preprocessed requests to the cloud. After the cloud brain completes logical reasoning, code generation, or intent parsing, it returns the results to the local gateway, which then translates them into concrete system operations.

The “physical logic” of this architecture is: offloading time-consuming heavy computation to the cloud, leveraging cloud providers’ elastic compute and infrastructure such as message queues to handle high concurrency; meanwhile, keeping the local gateway extremely lightweight to ensure zero intrusion and zero lag in the user interface. In actual engineering deployment, developers can also combine general-purpose container platforms for canary releases of the gateway and use cloud logging services and monitoring/alerting systems to trace agent invocation chains in real time.

4. Summary and Outlook: Technical Foundations for an Intelligent Commons

From dense models to the widespread adoption of MoE architecture, from limited context to the application of native caching mechanisms, the infrastructure of the large model field is undergoing a profound transformation centered on “cost reduction and efficiency improvement.” This is not merely a breakthrough in a single technology but an overall leap in systems engineering capability.

When the TTFT of large models approaches the response speed of local processes, and when the marginal cost of long-term persistent memory approaches zero, the era of AI agents will truly have crossed the chasm between cost and experience. For developers and enterprises, understanding this underlying technical logic and making rational use of long context, caching mechanisms, and cloud-native architecture will become a core competitive advantage for building next-generation cost-effective AI applications. In this expanding “intelligent commons,” the dividends of technological innovation will ultimately benefit every team committed to embracing intelligence.