From Thinking to Doing: Inside Native Agent Model Architecture

The Paradigm Shift: From “Smarter” to “More Capable”

For the past several years, the main thread of large language model competition has been foundational capability: stronger reasoning, broader knowledge, lower hallucination rates. Evaluation has centered on question answering, mathematics, and code completion. But as model capabilities approach a general baseline, the industry’s focus is undergoing a structural shift: can the model actually get work done?

“Getting work done” means the model acting as an execution agent that completes a complex task end-to-end — one involving multiple steps, constraints, and dependencies. It must understand requirements, break down a plan, call tools, write code, run verification, and self-correct based on feedback until it delivers a usable result. This is the fundamental difference between agent-shaped models and conversation-shaped models.

The new generation of native multimodal agent models is defined by two key characteristics:

• Native multimodality: Visual inputs (images, video) are no longer bolted-on supplements. They share the same reasoning pipeline as text, allowing the model to look directly at screenshots, design mockups, and monitoring dashboards to make decisions.
• Agent engine: The model is trained from the start for long-duration autonomous execution, with built-in capabilities for task planning, tool calling, and state management — rather than being assembled through external scaffolding.

In short, the core of this upgrade is not “smarter” but “more capable” — moving from a model that can think to an execution engine that can autonomously complete complex tasks. Let’s examine this from three angles: architecture, capabilities, and deployment.

MoE Architecture: Unifying 1T Capacity with 32B Inference Cost

The mainstream approach for current open-source LLMs is the Mixture of Experts (MoE) architecture. The core idea is sparse activation: the total parameter count can be very large to carry knowledge and capability, but each token only activates a small subset of parameters during inference, keeping compute costs far below what the total size would suggest.

Taking a typical specification from the new generation of open-source MoE models:

Parameter	Specification	Notes
Total parameters	~1T	Determines capability ceiling
Active parameters	~32B	Determines per-token inference cost
Layers	61 (1 dense layer included)	Only the first layer is dense
Experts	384	Total experts per layer
Active experts per token	8	Sparse routing granularity
Context length	256K	Foundation for long documents and tasks
Attention mechanism	MLA	Multi-head Latent Attention, compresses KV cache
Activation function	SwiGLU	Current mainstream choice
Vision encoder	Lightweight ViT (~400M)	Supports native multimodal input
Vocabulary size	~160K	Coverage for multilingual text and code tokens

The routing process can be understood through this simplified pseudocode:

# Simplified MoE forward pass
gate = softmax(W_gate(hidden))                      # Score all 384 experts per token
topk_w, topk_i = topk(gate, k=8)                    # Select only the top 8 experts
out = sum(w * Expert_i(hidden)
          for w, i in zip(topk_w, topk_i))          # Weighted aggregation

Several design points deserve attention:

1. MLA and long context. Multi-head Latent Attention projects key-value caches into a low-dimensional latent space, significantly compressing memory usage for long contexts. For agent scenarios requiring hundreds of thousands of tokens in context — an entire code repository or hours of task trajectory — KV cache efficiency directly determines serving costs.
2. Dense first layer. Shallow features tend to be more general-purpose patterns, where expert specialization offers limited benefit. Using a dense layer at the first layer proves more stable.
3. Large vocabulary. A ~160K vocabulary is particularly friendly to code — common API names, framework keywords, and mixed Chinese-English-code text can be encoded more completely, reducing token fragmentation and improving encoding efficiency and inference speed.
4. Operational implications. This generation of models shows strong architectural continuity: new versions often maintain the same parameter structure, attention implementation, and vocabulary as their predecessors. This means inference framework adaptations, weight conversion, quantization schemes, and parallelism strategies can be directly reused. Switching models without changing the deployment pipeline keeps migration costs low — for teams maintaining long-running inference clusters, this matters more than leaderboard scores.

Long-Horizon Coding: The Litmus Test for Agent Capability

“Long-horizon coding” does not mean writing a function or completing a code snippet. It means delivering a complete engineering task end-to-end: potentially spanning multiple languages (Rust, Go, Python), covering frontend, DevOps, and performance optimization, and requiring continuous execution for tens of minutes or even hours.

It is difficult for three reasons:

• Error accumulation: Each step’s output becomes the next step’s input. Small early mistakes get amplified down the chain, so the model must develop the habit of periodically verifying its work.
• State management: When a task runs hundreds of steps, the context fills with historical trajectories, tool outputs, and error messages. The model must actively organize, summarize, and discard information while keeping key constraints intact.
• Environment interaction: Real software engineering means running tests in a terminal, installing dependencies, and reading logs. The model’s behavior must form a closed loop with a real execution environment.

Evaluation methods have evolved accordingly: from static code generation benchmarks to agent-style evaluations where models complete tasks in real terminal environments, and software engineering benchmarks based on fixing real repository defects. Public results show that leading open-source native agent models now match or even exceed the strongest closed-source models on these benchmarks. The signal is clear: coding agents have moved from demo-grade to production-grade.

Behind this capability is a training methodology shift: reinforcement learning centered on verifiable rewards — whether tests pass, whether tasks complete — allowing models to learn planning and self-correction from massive real-world engineering tasks, rather than merely imitating human code.

Code-Driven Design: From One Sentence or One Image to Production-Grade Interfaces

Agent models extend coding capability further up the product chain: given a natural language description, or even a single interface screenshot, they can generate structurally complete, interactive frontend pages with animations.

For full-stack developers, the right way to use this capability is not replacement but compression:

• Prototyping: From idea to clickable demo, reduced from days to minutes.
• Implementation: Converting design mockups into maintainable component code, with engineers handling the final engineering polish.
• Iteration: Adjusting layout and interactions through incremental prompts, avoiding regressions from manual edits.

Three practical suggestions: first, explicitly specify design constraints in the prompt (color system, spacing rules, component library versions) — output consistency improves noticeably. Second, require the model to output at component granularity rather than a single monolithic page, making integration into existing projects easier. Third, delegate visual acceptance to the model as well — using its multimodal input, compare rendered screenshots against reference images to create an automated “generate — render — compare — fix” loop.

Multi-Agent Orchestration: From Solo Execution to Commander Mode

Another defining capability of the new generation of agent models is large-scale multi-agent orchestration: the main model acts as a commander, simultaneously dispatching hundreds of sub-agents to collaboratively execute thousands of steps. The engineering pattern is the classic Orchestrator-Worker architecture:

{
  "task": "Launch e-commerce campaign page",
  "plan": [
    {"step": 1, "worker": "Frontend Agent", "goal": "Generate campaign page components"},
    {"step": 2, "worker": "Testing Agent", "goal": "Run end-to-end test cases"},
    {"step": 3, "worker": "Ops Agent", "goal": "Run load tests and produce capacity report"}
  ],
  "orchestrator_duties": ["Decompose and dispatch", "Aggregate and arbitrate", "Retry on failure and degrade gracefully"]
}

The value of this pattern lies in context isolation and parallel acceleration: each sub-agent carries only the local context it needs, avoiding information interference within a single long context. Independent subtasks execute in parallel, dramatically reducing overall wall-clock time. But the engineering challenges are equally clear — message protocol design between sub-agents, consistency of shared state, and rollback/retry after partial failures all require careful system-level design. No level of model capability replaces this layer of architectural work.

Engineering Deployment Recommendations

Combining the capabilities above, here are several actionable recommendations for teams planning to adopt agent systems:

1. Deployment: MoE memory requirements come primarily from expert weights and KV cache. Consider combining expert parallelism with low-bit quantization. For self-hosted inference services, prioritize containerized deployment and reserve elastic scaling capacity.
2. Context engineering: Design a layered strategy for long tasks — persistent system constraints, intermediate artifacts written to disk, retrieval-based injection on demand — rather than stuffing all history into context.
3. Evaluation first: Before adopting any model, build an evaluation set grounded in your own business scenarios (real tickets, real repository tasks). Make decisions based on business data, not public leaderboards.
4. Security and cost: Agents execute real commands and modify code. Always run them in sandboxes with permission boundaries. Set token budgets and step limits per task, and pair them with logging and monitoring to track cost curves.
5. Cloud path: If self-hosting an inference cluster is too operationally heavy, leverage general-purpose cloud capabilities to bootstrap quickly — use an LLM service for inference access, container services for the agent runtime, object storage for task artifacts, and logging and monitoring services for observability. For example, Baidu AI Cloud’s general-purpose cloud offerings can be combined to support a complete agent application pipeline.

Conclusion

The LLM industry is crossing a watershed: from “models that answer questions” to “platforms that complete tasks.” For developers, rather than obsessing over one or two percentage points on leaderboards, it’s worth focusing on three more fundamental selection criteria: architectural compatibility determines migration and operational costs, agent benchmark performance determines real productivity, and toolchain ecosystem determines deployment speed. Understanding these technical factors — whether choosing open-source self-hosting or managed cloud services — is what ultimately converts next-generation model capabilities into business delivery efficiency.