From Single Responses to Long-Horizon Task Organization

In the past, large language models were mostly used for single-turn question answering, code completion, text summarization, or short automation workflows. As engineering scenarios become more complex, truly difficult tasks often cannot be completed in a single response. Instead, they require continuous progression, repeated correction, and step-by-step validation until a deliverable result is produced.

Examples include:

• Translating a classical mathematical theorem into a formal proof;
• Designing a runnable prototype operating system from an empty directory;
• Coordinating software modules, hardware interfaces, driver logic, and test cases;
• Maintaining a consistent goal across multiple subtasks while continuously repairing failed paths.

These tasks usually share several characteristics: many steps, long execution cycles, complex dependencies, verifiable intermediate results, and recoverability after failure. Multi-agent collaboration systems have gained attention in this context. They are not simply about splitting a task into multiple small subtasks and running them in parallel. Instead, they use a closed loop of planning, execution, validation, repair, and integration to make the system behave more like a long-running project executor.

1. The Key to Multi-Agent Collaboration Is Organization, Not Parallelism

A common first impression of multi-agent systems is that “multiple agents work together.” In real engineering practice, however, success is not mainly determined by the number of parallel agents. It is determined by the system’s ability to organize work.

A typical long-horizon collaboration system may include the following roles:

1. Planner
   Understands the overall goal, breaks down complex tasks into executable subtasks, and defines dependencies among them.

2. Executor
   Completes concrete subtasks, such as writing code, generating documentation, designing interfaces, constructing test cases, or analyzing logs.

3. Reviewer
   Performs quality checks on execution results, including logical consistency, interface compatibility, boundary conditions, security, and runnability.

4. Integrator
   Merges the outputs of different subtasks into a complete system and handles conflicts, duplication, missing pieces, and version inconsistencies.

5. Recorder
   Saves context, decision rationale, failure causes, retry strategies, and intermediate artifacts so the system can recover from errors.

Without these divisions of responsibility, simply having multiple agents generate content at the same time often leads to several problems:

• Different subtask agents repeatedly generate identical or similar content;
• Interfaces between subtasks are inconsistent;
• After one agent produces an incorrect output, other agents continue to build on that incorrect result;
• Context is lost during long-horizon tasks, causing the goal to drift;
• The final result cannot be verified and must be checked manually item by item.

Therefore, the essence of multi-agent collaboration is not merely opening multiple windows or launching multiple prompts. It is to establish an engineering mechanism that can schedule, verify, and roll back work.

2. Capability Boundaries Through Complex Tasks

Theorem Formalization: High Constraints and Strong Verification

Theorem formalization is a representative task that places high demands on a multi-agent system. A formal proof is not simply a natural-language argument. It requires semantic expression, dependency references, type checking, and logical derivation within a strict proof system.

In such tasks, the value of multi-agent collaboration mainly appears in several areas:

• Subtask decomposition: Breaking a large proof into lemmas, definitions, intermediate propositions, and a final conclusion;
• Parallel exploration: Having different agents attempt different proof paths to improve exploration efficiency;
• Failure diagnosis: When a proof path fails, the system can analyze the error type and adjust the strategy;
• Dependency management: Proof steps often have dependencies, so reference relationships need to be maintained;
• Result validation: The final result must be confirmed by a formal verification tool or checker.

The core difficulty of these tasks is not generating text. It is generating results that can be verified. Without a validation stage, a multi-agent system may produce reasoning that appears reasonable but cannot actually be proven.

As a result, long-horizon multi-agent systems must introduce external validators in high-constraint tasks. A validator can be viewed as the system’s “fact judge.” Agents may propose solutions, but whether a solution is valid should not be decided by the agents themselves. It should be determined by deterministic rules, compilation results, test outcomes, or formal checking tools.

Building a Prototype Operating System from an Empty Directory

Another typical scenario is giving a system an empty directory and asking it to design a prototype operating system on its own. This task is much more complex than generating a single piece of code because it involves multiple layers:

• Boot process;
• Basic kernel scheduling;
• Hardware drivers;
• File storage;
• Window interface;
• User-space programs;
• Installer;
• Test cases;
• Build scripts;
• Documentation.

The key to this task is not generating a large amount of code in one pass. It is continuously maintaining system consistency.

For example, driver modules need to understand hardware interfaces. The window interface depends on the graphics subsystem. The file system must remain consistent with the storage layer. The installer needs to know how to initialize the target environment. If any module changes, it may affect other modules.

Therefore, the system needs the following capabilities:

1. Architecture constraint preservation
   At the beginning of the task, define module boundaries, interface specifications, and directory structures to prevent later agents from arbitrarily expanding scope.

2. Intermediate artifact management
   Store design documents, interface definitions, code snippets, test results, and error logs in a unified way so later steps can reference them.

3. Build and test closed loop
   After each module is completed, execute compilation, startup, unit tests, or integration tests as far as possible.

4. Failure rollback mechanism
   If a step causes the system to fail to build, the system should be able to roll back to the last stable state instead of continuing to stack errors.

5. Human checkpoints
   For high-risk modules, such as memory management, device access, and permission control, human review or automated audit checkpoints should be introduced.

This type of task demonstrates the potential of multi-agent systems in open-ended engineering work. The system can work for a long time and attempt to complete a system-level project that would otherwise require multiple people and multiple review rounds.

Hardware/Software Co-Design Prototypes

In addition to pure software tasks, multi-agent systems can also be extended to hardware/software co-design scenarios. For example, designing a voice assistant, a USB speaker, or an electronic pet prototype.

These tasks usually include the following workflow:

• Clarify the product goal and usage scenario;
• Select the hardware solution and interfaces;
• Design firmware and drivers;
• Write control software;
• Design the interaction interface;
• Generate test cases;
• Simulate or verify key paths;
• Output documentation and deployment instructions.

In this scenario, the multi-agent system needs to handle transitions across different knowledge domains: from requirements to architecture, from architecture to interfaces, from interfaces to implementation, and from implementation to testing. It cannot merely generate a piece of code. It must maintain consistency among requirements, design, implementation, and testing.

This is also an important difference between multi-agent systems and ordinary generative tools. Ordinary tools focus on “writing a piece of content.” Multi-agent systems focus on “completing a deliverable result.”

3. Cost-Effectiveness Cannot Be Judged by Unit Price Alone

When discussing multi-agent systems, one common mistake is to focus only on the price of a single call. For example, people may compare only input prices, output prices, or cost per million tokens, while ignoring the overall resource consumption required in long-horizon tasks.

In reality, the cost structure of long-horizon tasks is much more complex than that of a single request.

A more complete cost model can be expressed as follows:

Total cost =
    planning cost
  + execution cost
  + verification cost
  + retry cost
  + storage and retrieval cost
  + monitoring and logging cost
  + human review cost

Where:

• Planning cost: Resources consumed while decomposing tasks, generating plans, and maintaining dependencies;
• Execution cost: Inference resources consumed when multiple agents generate code, documents, or analysis results;
• Verification cost: Compute and time consumed by compilation, testing, formal checking, and runtime validation;
• Retry cost: Additional consumption caused by re-planning, re-execution, and re-repair after failure;
• Storage and retrieval cost: Resources used to save intermediate artifacts, context summaries, knowledge indexes, and version information;
• Monitoring and logging cost: Resources used to track agent status, error paths, tool calls, and exception recovery;
• Human review cost: Resources used to review, correct, and approve key results.

Therefore, when judging cost-effectiveness, a more reasonable metric is not “how much does one million tokens cost?” but rather:

Effective task cost = total resources consumed to complete one acceptable task

It can be further defined as:

Cost-effectiveness = value of acceptable results / total resource consumption

If a system has a low price per call but requires extensive manual correction, its final cost-effectiveness may not be high. If a system has a higher price per call but significantly reduces rework, shortens delivery cycles, and improves the first-pass acceptance rate, its overall cost may actually be lower.

4. Task Tiering: Not Every Task Needs the Strongest Capability

Long-horizon multi-agent systems are not suitable for all scenarios. For simple tasks, using a high-capability, high-cost system for scheduling often leads to wasted resources. A more reasonable approach is task tiering.

Tasks can be divided into three categories:

Lightweight Tasks

Examples include:

• Copy polishing;
• Simple code completion;
• Single-file modification;
• Format conversion;
• Routine question answering;
• Log summarization.

These tasks usually involve short steps, limited context, and low validation costs. They are suitable for lightweight models or a single agent.

Medium-Complexity Tasks

Examples include:

• Multi-file code modification;
• Interface design;
• Test generation;
• Fault localization;
• Document organization;
• Data cleaning pipeline construction.

These tasks may introduce a small number of agents and basic validation mechanisms, but clear boundaries should still be set to avoid over-decomposition.

Long-Horizon Open Tasks

Examples include:

• Complex system prototype design;
• Large-scale code refactoring;
• Multi-module integration;
• Formal proof assistance;
• Hardware/software co-design prototypes;
• Continuous exploratory research tasks.

These tasks are more suitable for multi-agent collaboration systems. Their characteristics include high uncertainty, many steps, repeated validation, and the difficulty of completing the task with a single model in one pass.

In practice, a dispatch strategy can be established as follows:

If the task has few steps and the result is easy to verify:
    use a lightweight single-agent approach
If the task has moderate steps and requires code or document collaboration:
    use a small multi-agent setup with automated testing
If the task is open-ended, long-running, and has complex dependencies:
    use long-horizon multi-agent collaboration with human checkpoints and budget control

This tiered approach helps control costs and prevents over-engineering for simple tasks.

5. Engineering Implementation: From Generation to Deliverables

For multi-agent systems to be truly usable in production, engineering capabilities must be added. The following are several key practices.

Define Task Boundaries

Before starting, define:

• What is the input?
• What is the output?
• What is the acceptance standard?
• Which steps are allowed to run automatically?
• Which steps must be confirmed by humans?
• How should the system roll back after failure?
• What is the budget limit?

Without clear boundaries, a multi-agent system can easily fall into infinite retries or goal drift.

Establish State Management

Long-horizon tasks must maintain state. At a minimum, the state should include:

• The current task goal;
• Completed subtasks;
• Failed subtasks;
• Paths to intermediate artifacts;
• Interface definitions;
• Test results;
• Current budget consumption;
• Suggestions for the next execution step.

State management can use files, databases, or cloud object storage. The key is not the specific storage method, but ensuring that the state is traceable, recoverable, and auditable.

Add Verification Closed Loops

A multi-agent system cannot only generate results. It must also validate results. Common validation methods include:

• Compilation checks;
• Unit tests;
• Integration tests;
• Static scanning;
• Runtime log inspection;
• Formal verification;
• Human review;
• Rule-engine checks.

The more complete the validation loop, the less likely the system is to produce results that appear complete but are actually unusable.

Control Parallelism

Higher parallelism is not always better. Excessive parallelism can lead to the following problems:

• More conflicts among sub-agents;
• Lower context consistency;
• Competition for tool calls;
• Higher validation pressure;
• Faster error propagation.

A more stable approach is to dynamically control parallelism based on task dependencies. For example, tasks without dependencies can run in parallel, while strongly dependent tasks should be executed serially or in phases.

The core idea is shown in the following pseudocode:

def run_long_task(goal, budget):
    plan = decompose(goal)
    tasks = assign(plan)
    state = []
    while not done(goal):
        result = execute(tasks, state, budget)
        checked = validate(result)
        if checked.passed:
            state = merge(state, checked.artifact)
            tasks = next_tasks(plan, state)
        else:
            tasks = repair(tasks, checked.error)
            budget = budget.consume(checked.retry_cost)
        if budget.exceeded():
            return escalate_to_human(state)
    return finalize(state)

The core idea expressed by this pseudocode is that the system does not generate an answer in one pass. Instead, it progresses through a loop of execution, validation, repair, and convergence.

Use Cloud Platform Capabilities as Support

If a multi-agent system is deployed in the cloud, general cloud platform capabilities can be used to improve stability:

• Use container platforms to host agent execution instances;
• Use object storage to save intermediate artifacts and build results;
• Use message queues to decouple task production from task consumption;
• Use logging services to record call chains and exception information;
• Use monitoring and alerting to detect task stalls, abnormal costs, or rising failure rates;
• Use access control and key management mechanisms to limit agent permissions.

These capabilities do not determine whether an agent is intelligent, but they determine whether the system is operable.

6. Main Risks and Governance Methods

Long-horizon multi-agent systems are powerful, but they also have clear risks. Without governance, they may lead to cost overruns, incorrect results, or security issues.

Goal Drift

In long-horizon tasks, sub-agents may deviate from the original goal in intermediate steps. Governance methods include:

• Re-inject the overall goal before each execution step;
• Require human confirmation at key checkpoints;
• Maintain a goal constraint checklist;
• Check whether the final artifact satisfies the original requirements during validation.

Error Accumulation

If one subtask produces an incorrect output, later tasks may continue to build on that incorrect result. Governance methods include:

• Introduce validators;
• Set up phase-based checkpoints;
• Perform independent review on high-risk artifacts;
• Allow rollback to a stable state.

Cost Runaway

Multi-agent systems may produce many retries and parallel calls, causing costs to rise quickly. Governance methods include:

• Set budget limits;
• Limit the maximum number of retries;
• Require human approval for expensive steps;
• Record the cost contribution of each subtask;
• Prefer low-cost paths for routine steps.

Security Risks

Agents may need to access code, files, logs, test environments, or even hardware simulation environments. Governance methods include:

• Apply the principle of least privilege;
• Isolate working directories for different tasks;
• Require approval for sensitive operations;
• Record complete audit logs;
• Prohibit agents from directly accessing irreversible resources;
• Protect against prompt injection from external inputs.

Insufficient Reproducibility

Outputs from multi-agent systems may be affected by randomness, context order, and tool state. Governance methods include:

• Save complete inputs, plans, intermediate results, and version information;
• Fix key configurations;
• Compare test results through snapshots;
• Build a repeatable evaluation set.

7. How to Evaluate Whether a Multi-Agent System Is Worth Using

When evaluating a multi-agent system, do not only ask whether it can generate content. Ask whether it can reliably complete the target task. It can be evaluated from the following dimensions:

1. Task completion rate
   The proportion of tasks successfully completed within the given budget and time limit.

2. First-pass acceptance rate
   The proportion of results that pass acceptance on the first submission.

3. Average number of rework rounds
   The number of correction rounds required for each task to move from failure to acceptance.

4. Human intervention ratio
   The proportion of key steps that require human involvement.

5. End-to-end latency
   The time from task start to an acceptable result.

6. Cost per effective task
   The total resources consumed to complete one acceptable task.

7. Security and compliance rate
   Whether there is unauthorized access, sensitive information leakage, or un-auditable behavior.

8. Stability
   Whether the system can recover under continuous operation, complex dependencies, or multiple rounds of failure.

For open-ended long-horizon tasks, additional metrics can be considered:

• Solution diversity;
• Intermediate artifact reusability;
• Subtask parallel efficiency;
• Validator coverage;
• Distribution of failure causes;
• Context retention capability;
• Interface consistency across modules.

8. Conclusion: The Value of Multi-Agent Systems Lies in Organizing Complex Work

The true meaning of multi-agent collaboration systems is not to make models “better at chatting.” It is to enable models to take on more complex work organization tasks. Such a system can break down goals, assign subtasks, execute in parallel, validate repeatedly, and combine different modules into deliverable results.

From a capability perspective, it has clear value in the following scenarios:

• Long-cycle complex engineering;
• Multi-module collaborative development;
• Tasks requiring continuous testing and repair;
• Formal verification or high-constraint logic tasks;
• Hardware/software co-design prototypes;
• Open-ended exploratory tasks.

From a cost-effectiveness perspective, however, a more complete cost model is required. The cost of long-horizon tasks is not limited to single-call fees. It also includes planning, execution, verification, retries, storage, monitoring, and human review. The real criterion is whether the system can consistently produce acceptable results at an acceptable overall cost.

Therefore, for developers and enterprise users, a more mature approach is not to blindly pursue the strongest model. Instead, build a tiered scheduling mechanism: use lightweight paths for simple tasks, introduce limited collaboration for medium-complexity tasks, and enable multi-agent systems for complex long-horizon tasks, combined with budget control, validation loops, and human checkpoints.

In one sentence: the core competitiveness of a multi-agent collaboration system is not how much content it can generate, but whether it can decompose complex goals into executable steps, advance uncertain tasks toward verifiable results, and balance cost, quality, security, and efficiency.