The Era of Long-Horizon AI Agents: From Code Capabilities to 24/7 Task Execution

1. Core Shift in Model Capabilities: From “Answering” to “Continuous Work”

The competitive landscape for next-generation large language models (LLMs) is undergoing a significant shift. Over the past two years, the industry focused heavily on improving scores on static benchmarks, such as multi-turn QA, text summarization, and code completion. However, when models are deployed into real-world production environments, more complex issues emerge: tasks often cannot be resolved with a single Q&A turn. Instead, they require the model to execute continuously under multi-step, multi-tool, and long-time-span constraints.

This is why the concept of “long-horizon agents” is gaining traction. It emphasizes not just whether a model can generate a correct answer, but whether it can act like a junior engineer or researcher—understanding objectives, decomposing tasks, invoking tools, handling failures, and driving progress until an end-to-end workflow is completed.

From a technical implementation perspective, this capability upgrade relies on three key dimensions:

1. Significant increase in post-training compute, especially the proportion of compute allocated to the reinforcement learning (RL) phase.
2. Expansion of RL coverage domains, scaling from a few high-signal areas like math and code to hundreds of downstream scenarios.
3. Reconstruction of training and inference infrastructure, including RL frameworks, long-context inference engines, sandbox environments, and task scheduling systems.

These three aspects are not isolated; they form a complete training and deployment loop. The more diverse the environments the model experiences during training, the stronger its generalization ability when facing real tasks during inference. Furthermore, the better the inference framework supports long contexts, the less likely the model is to lose critical state information during actual long-horizon task execution.

2. Quantifying Code Capabilities: End-to-End Tasks in Terminal Environments as the New Benchmark

Code capability has always been a crucial dimension for measuring LLM practicality. However, traditional evaluation methods, such as single-file code generation or simple function completion, are no longer sufficient to reflect real-world development scenarios. A more effective evaluation method is testing the model’s ability to complete complex programming tasks within resource-constrained terminal environments.

These evaluations typically feature:

• Real terminal environments: The model operates within a Unix-like terminal rather than outputting code in a closed text box.
• Resource constraints: Tasks may limit execution attempts, time, or available tools, closely mimicking real working conditions.
• Specific test validation: Each task is paired with independent test cases or validation scripts; completion is only counted if validations pass.
• Multi-step execution and tool invocation: The model autonomously decides when to view files, run commands, and modify code, rather than passively waiting for instructions.

In these types of benchmarks, current top-tier models show significant performance divergence. Some models score high in pure text-based code generation but drop drastically when executing complete tasks in terminal environments due to a lack of environment awareness, state maintenance, or error recovery capabilities. Conversely, models that heavily incorporate terminal interaction, command execution feedback, and long-horizon task training during the post-training phase demonstrate a more stable end-to-end completion rate.

For example, in terminal task benchmarks, some latest models score above 65%, while previous-generation models might hover around 55%. A near 10-percentage-point increase might seem small, but translated into real development tasks, it means the model has evolved from “occasionally running simple tasks” to “reliably handling medium-complexity multi-file engineering tasks.”

Breaking this down further, the capabilities of a code agent can be categorized into the following levels:

Capability Level	Description	Common Failure Modes
Single-file generation	Generates a function or class based on requirements	Ignores boundary conditions, lacks exception handling
Multi-file engineering	Understands project structure, modifies across files	Cannot find the correct entry point, breaks existing logic
Command execution	Autonomously runs tests, installs dependencies	Incorrect commands corrupt the environment
State maintenance	Remembers completed steps during long tasks	Context loss leads to repeated operations
Error recovery	Adjusts strategy upon encountering failures	Falls into infinite loops or gives up directly

Current top-tier models are relatively mature in the first three levels. The true differentiator lies in the latter two: state maintenance and error recovery. This is also the key factor distinguishing long-horizon agents from traditional code generation tools.

3. Expanding Reinforcement Learning from Single Tasks to Hundreds of Downstream Domains

In early post-training of LLMs, the application of reinforcement learning primarily focused on mathematical reasoning and code generation. The reason is straightforward: these tasks possess clear reward signals. Math problems have standard answers, and code correctness can be verified through unit tests. This “verifiability” allows RL to optimize model policies efficiently.

However, when models need to acquire broader downstream capabilities, challenges arise. Fields like financial research, 3D creation, data analysis, and document processing often lack simple, direct automated verification methods. It is difficult to judge the quality of a financial research report with a single scalar score, and a 3D model generation task might have multiple valid answers.

The technical path to solving this issue involves expanding the RL objective from “pursuing a single optimal answer” to “learning general policies in diverse environments.” Specific approaches include:

1. Building hierarchical reward models: Decomposing ambiguous domain goals into multiple verifiable sub-goals. For instance, a financial research task can be broken down into steps like “correctly reading data,” “using appropriate analysis tools,” and “outputting structured conclusions,” where each step can be designed with relatively objective verification mechanisms.
2. Leveraging environmental feedback over manual annotation: Many downstream tasks inherently run in interactive environments. Whether a 3D tool call succeeds, whether a data query returns expected results, or whether code compiles can all serve as automated feedback signals. This approach provides training samples for RL without requiring massive manual annotation.
3. Introducing curriculum learning strategies: Starting training with simpler domains and gradually increasing task complexity and domain diversity, preventing the model from facing overly sparse and scattered reward signals early in post-training.

This large-scale joint training imposes extreme demands on infrastructure. The RL framework must support parallel sampling, reward computation, and policy updates across hundreds of domains. Sandbox environments must ensure the secure isolation and rapid provisioning of tens of millions of tasks. Scheduling systems must rationally allocate compute resources under high concurrency while preventing low-frequency domains from “starving.”

Essentially, scaling RL from single to multi-domain is an upgrade in training engineering capabilities. It is no longer just an algorithm problem, but a distributed systems problem.

4. High-Speed Inference: Balancing Output Speed and Intelligence

The trade-off between inference speed and model intelligence is a classic challenge in production deployment. A larger model usually implies a higher upper limit of intelligence, but it also brings slower response times and higher compute costs. For scenarios requiring high real-time performance, such as interactive programming assistants, online customer service, or real-time data analysis, slow response speeds severely degrade user experience.

A common optimization approach is “distillation”: training a smaller model to mimic the larger model’s outputs. However, distillation inevitably leads to capability loss, especially in complex reasoning tasks where smaller models often fail to replicate the deep thought processes of large models.

Another approach is optimizing from the inference side—improving decoding strategies and computation scheduling without altering the model’s parameter scale. Examples include:

• Speculative decoding: A lightweight model quickly generates candidate tokens, which are then verified in parallel by the main model. When the verification pass rate is high, overall generation speed can increase severalfold.
• Dynamic batching: Dynamically adjusting batch sizes based on the compute density of requests, maximizing GPU utilization without exceeding latency limits.
• Cache reuse: Reusing the KV cache for repetitive prompt prefixes to reduce redundant computation.

In practice, high-speed inference versions can increase output speed by 2 to 3 times while maintaining the same model weights and intelligence level. The tradeoff is typically a higher unit cost, as additional compute resources are required to support speculative decoding or more aggressive batching strategies.

It is important to note that high-speed inference is not a silver bullet. It yields the most significant benefits in compute-intensive scenarios like long-text generation and code completion. In short Q&A scenarios, where first-token latency accounts for a larger portion of the time, the room for speed improvement is relatively limited. Therefore, a more rational approach in production environments is dynamically routing to the standard or high-speed version based on task type, rather than applying high-speed inference uniformly.

5. Infrastructure for Long-Horizon Agents: Context Management, Tool Invocation, and Task Scheduling

When a model needs to work continuously for hours or even days, the importance of infrastructure surpasses that of the model itself. A typical 24/7 long-horizon task may involve the following steps:

• Reading and understanding the initial task description.
• Formulating an execution plan and decomposing sub-tasks.
• Sequentially invoking different tools: querying data, running code, generating documents, sending notifications, etc.
• Maintaining state between steps, recording completed and pending items.
• Adjusting plans or retrying in the face of exceptions.

This workflow imposes several key requirements on infrastructure.

First, long-context management. Long-horizon tasks accumulate massive amounts of intermediate results. If all are pushed into the context window, the length limit will be reached quickly, and computation costs grow super-linearly with context length. A viable approach is introducing a “memory hierarchy” mechanism: retaining recent critical states in the working context, compressing historical records into summaries stored externally, and retrieving them when needed.

Second, reliability of tool invocation. Long-horizon tasks often require dozens or even hundreds of tool calls. Each call can fail due to network timeouts, parameter errors, insufficient permissions, or abnormal return formats. The infrastructure must possess automatic retry, degradation, and error classification capabilities, rather than throwing all exceptions directly to the model to handle.

Third, task scheduling and concurrency control. In multi-agent collaboration scenarios, different agents may share the same set of tools or data sources. The scheduling system must coordinate execution sequences to avoid resource competition and data inconsistency. Simultaneously, it must support basic operations like task prioritization, cancellation, and pause/resume.

From an engineering perspective, these capabilities can be abstracted into an “agent runtime.” Its role is similar to an operating system: the model serves as the application logic, while the runtime manages resource allocation, task scheduling, and exception handling. Without this runtime, even the most powerful model will struggle to execute long-horizon tasks stably in real production environments.

6. Enterprise Selection Perspective: Evaluating the Practical Value of Long-Horizon Agents

Enterprise users should not focus solely on a model’s scores on public benchmarks, but rather on its end-to-end performance within their own business scenarios. The following dimensions can serve as reference criteria for evaluating long-horizon agents:

1. Task completion rate over single-step accuracy: A model with high single-step accuracy has limited real-world business value if it cannot execute multi-step operations continuously. It is recommended to design end-to-end test tasks with 5 or more steps and observe the model’s completion rate.
2. State maintenance and failure recovery capabilities: Artificially introduce intermediate failures, such as returning tool errors or temporarily disabling data sources, and observe whether the model can recover on its own instead of aborting directly.
3. Context utilization: During long task execution, check whether the model can effectively utilize already acquired information, avoiding repeated reads or forgetting critical conclusions.
4. Cost and speed balance: For different task types, evaluate the cost-effectiveness difference between standard and high-speed versions. Compute-intensive long-text generation tasks might be better suited for the high-speed version, while short interactive tasks can use the standard version.
5. Infrastructure dependency: Confirm whether additional scheduling platforms, sandbox environments, or monitoring systems are required to run long-horizon tasks. If so, evaluate the setup cost and operational complexity of this infrastructure.

It is worth noting that evaluating long-horizon agents cannot rely solely on the model itself; it must also consider the synergy between the model and the runtime environment. The same model may exhibit significant differences across different infrastructures. Therefore, during enterprise selection, the “model + runtime” should be evaluated as an integrated solution, rather than comparing model parameters or benchmark scores in isolation.

7. Future Trends: Verifiable Rewards, Multi-Agent Collaboration, and Engineering Capabilities

From the perspective of technological evolution, LLM post-training is entering a new phase. The focus over the past two years was on “scaling model size”; the upcoming focus is on “expanding task scope” and “extending task duration.” The driving force behind both is the continuous pursuit of verifiable reward signals.

Code and math became pioneers in RL precisely because they possess automated verification conditions. Next, fields like financial research, 3D creation, and data analysis will gradually build verifiable sub-task reward systems. As more domains acquire automated feedback capabilities, the generalization ability of LLMs will be further unleashed.

Multi-agent collaboration is another trend worth watching. A single agent’s context window and working memory are ultimately limited. Decomposing complex tasks into multiple specialized agents can effectively reduce single-point failure rates. However, multi-agent collaboration also introduces new complexities: communication protocols between agents, task allocation mechanisms, result merging, and conflict resolution all require system-level design at the infrastructure layer.

Ultimately, the competition in long-horizon agents will occur not only at the model layer but also at the engineering layer. Whoever can build a more stable, efficient, and cost-effective agent runtime will gain the advantage in real production scenarios. This may well be one of the most critical technical propositions in the second half of the LLM era.