Engineering Multi-Agent Systems for Long-Horizon Complex Tasks

1. The Industry Focus Is Shifting from Model Capability to Task Completion

Recent large-model releases point to one important change: the key question is no longer how well a model performs in a single turn of question answering. Instead, models are increasingly expected to take on longer, more complex, and more open-ended tasks.

Examples include:

• Coordinating a large number of agents to complete the formal verification of mathematical proofs.
• Building a software system from an empty directory.
• Continuously operating tools, generating code, running tests, fixing errors, and maintaining a consistent objective over hours or even longer periods.

This shift changes how models are evaluated. In the past, the industry often focused on whether a model could achieve high scores on benchmark tests. Today, the more important question is whether a model can complete end-to-end delivery within a realistic task chain.

A long-horizon agent system must do more than “understand the requirement.” It must also:

• Decompose the task.
• Schedule tools.
• Execute operations.
• Verify results.
• Handle exceptions.
• Keep the process traceable.
• Keep the system controllable.
• Support evaluation.

This also has a direct impact on developers. When building agent applications, it is no longer enough to rely on a single model call. The model must be placed inside a complete engineering system.

The critical design questions are therefore no longer whether a prompt is well written, but whether:

• Task orchestration is stable.
• Context is controllable.
• Tool calls are safe.
• Result verification is sufficient.
• Failure recovery is reliable.

2. Core Characteristics of Long-Horizon Complex Tasks

Long-horizon complex tasks usually have several defining characteristics.

1. Open-Ended Goals

Users often provide only high-level goals, such as:

• “Build a runnable prototype of an operating system kernel.”
• “Complete the formal proof of a mathematical theorem.”
• “Design a hardware module that can be simulation-verified.”

These goals do not have a single predefined path. The system must decompose them on its own.

2. Long and Multistep Execution

A task may last for hours, days, or even longer. Each step may produce intermediate artifacts, such as:

• Code.
• Configuration files.
• Documentation.
• Test results.
• Verification scripts.

The system must preserve intermediate state. Otherwise, once execution is interrupted, it may be impossible to continue.

3. Collaboration Across Multiple Roles

Complex tasks often require different agents to take on different roles, such as:

• A planner that breaks down tasks.
• An executor that generates content.
• A verifier that checks quality.
• A reviewer that identifies risks.
• A coordinator that merges results.

4. Results Must Be Verified

Long-horizon tasks cannot be considered complete just because the output “looks reasonable.”

Depending on the domain, verification may require:

• Formal verification for mathematical proofs.
• Compilation and testing for software projects.
• Simulation for hardware designs.
• Safety boundary checks for robotic control.

5. Errors Can Propagate

If an early planning step is wrong, all subsequent steps may be built on incorrect assumptions.

Therefore, the system must detect errors as early as possible and use feedback mechanisms to correct the path.

These characteristics show that a long-horizon agent is not simply a model with a longer conversation. It is a complex engineering system.

3. Basic Patterns of Multi-Agent Collaboration

Multi-agent collaboration is an important approach for completing large, open-ended tasks. Common architectures can be grouped into the following categories.

1. Centralized Orchestration

In centralized orchestration, a main control agent is responsible for:

• Understanding the goal.
• Decomposing tasks.
• Assigning subtasks.
• Aggregating final results.

Its advantages are clear structure and strong controllability. However, the main control agent may become a performance bottleneck, and decision quality may decrease when the context grows too long.

This pattern is suitable for scenarios where:

• Task boundaries are relatively clear.
• Acceptance criteria are well defined.
• Strong control is required, such as in enterprise workflows.

2. Decentralized Collaboration

In decentralized collaboration, multiple agents interact autonomously around a shared goal. Each agent advances its own subtask and coordinates through message exchange.

This pattern is suitable for exploratory tasks, such as:

• Searching for multiple proof paths.
• Generating multiple candidate solutions.
• Performing open-ended design.

However, it can lead to:

• Goal drift.
• Duplicated work.
• Resource contention.
• Conflicting results.

Therefore, it requires a strong shared-state management mechanism.

3. Hybrid Collaboration

Hybrid collaboration combines centralized orchestration with local autonomy.

In this model:

• The main control agent manages overall goals, milestones, and resource allocation.
• Sub-agents execute autonomously within local scopes.
• Verification agents perform gate checks on results.

This approach is often more suitable for production environments because it balances flexibility and controllability.

A typical multi-agent system can be abstracted as a task graph.

Each node represents a subtask, and nodes are connected by dependency relationships. An executor advances the graph in topological order. After each node is completed, it enters the verification stage.

Example pseudocode:

Task node:
  id: text
  goal: text
  inputs: list of text
  outputs: list of text
  owner: agent role
  status: pending / in progress / completed / needs retry / human takeover
Execution flow:
  topologically sort the task graph
  for each task node, perform the following steps:
    load context required by the node
    assign the goal to the corresponding agent
    the agent invokes tools and produces intermediate results
    the verifier checks whether results meet acceptance criteria
    if verification passes:
      persist results
      mark the node as completed
      trigger downstream tasks
    if verification fails:
      record the failure reason
      decide whether to retry, switch paths, or escalate to a human

The core of this process is not “keep generating with the model.” It is to form a closed loop around:

• Generation.
• Verification.
• Persistence.
• Retry.

4. Infrastructure Capabilities Required for Long-Horizon Tasks

Running agents continuously for hours or even days requires more than the model itself. Engineering teams must build several layers of supporting infrastructure.

1. Context and Memory Management

Long-horizon tasks generate a large amount of context, including:

• Requirement descriptions.
• Historical steps.
• Tool outputs.
• Code snippets.
• Error logs.
• Verification results.

If all of this information is injected into a single model call, it can quickly exceed the context window and introduce noise.

A layered memory mechanism is therefore needed:

• Short-term context: information required by the current task node.
• Medium-term summaries: stage goals, completed steps, and key decisions.
• Long-term archives: complete logs, artifact files, version records, and verification reports.

Retrieval mechanisms are also important. The system should automatically extract relevant historical fragments based on the current task, rather than injecting all content into the model indiscriminately.

2. Execution Environments and Sandboxes

Agents may need to:

• Execute code.
• Read and write files.
• Invoke test tools.
• Run simulation programs.

For safety, these operations must be performed in isolated environments.

A sandbox should provide:

• Restrictions on filesystem access scope.
• Restrictions on network access permissions.
• Limits on resource usage.
• Logging of all commands and file changes.
• Support for snapshots, rollback, and replay.

For high-risk domains, such as cybersecurity, life sciences, and industrial control, additional human approval mechanisms should be added. Agents may generate proposals, but critical operations must be authorized.

3. Tool Invocation System

Tools are the key bridge that allows agents to move from “text generation” to “real execution.”

Common tools include:

• Code executors.
• Test frameworks.
• Version management systems.
• Compilers.
• Simulators.
• Database query interfaces.
• Log search systems.

Tool invocation requires attention to three issues.

1. Interface Standardization

Each tool should have clearly defined:

• Inputs.
• Outputs.
• Error codes.
• Timeout behavior.

This helps prevent misuse by agents.

2. Idempotency

Some tools may be called repeatedly. If an operation is not idempotent, repeated calls may cause:

• Duplicate writes.
• Duplicate deletions.
• Resource pollution.

3. Permission Isolation

Different tools should have different risk levels.

For example:

• Reading public documentation and executing production database changes should not have the same permissions.

4. Observability and Cost Control

Long-horizon tasks must be observable. The system should record:

• Inputs and outputs of each agent.
• Parameters and results of each tool call.
• Time consumed by each task node.
• Reasons for each verification failure.
• Total token consumption.
• Compute resource consumption.
• Number of human interventions.
• Nodes where human intervention occurred.

Without observability, a long-horizon task becomes a black box.

If results are abnormal, it becomes difficult to determine whether the problem comes from:

• Insufficient model capability.
• Incorrect task decomposition.
• Tool call failures.
• Overly strict verification standards.

5. Technical Challenges of Formal Proof Tasks

One of the most notable scenarios in public discussions is the use of many agents to complete formal verification of mathematical theorems. For example, converting existing human-written mathematical proofs into machine-verifiable formal scripts.

This type of task places high demands on agent systems.

Formal proof is not ordinary text generation. It is strict logical construction.

The main challenges include:

1. Complex Proof Goals

A theorem may depend on many lemmas, auxiliary propositions, and intermediate conclusions.

The system must determine:

• Which step should be proved next.
• Which conclusions are already available.
• Which definitions must remain consistent.

2. Difficult State Maintenance

The proof process forms a continuously expanding dependency tree.

Agents need to know:

• Which propositions have already been proved.
• Which prior conclusions each proposition depends on.
• Which proof attempts have failed.
• Which paths are still valid.
• How far the current goal is from the final goal.

3. Closed-Loop Feedback from Verifiers

Formal verification tools return strict error messages.

The system should not simply ask the model to “try again.” Instead, it should feed the semantic meaning of the error back to the agent so that the agent can identify the problem type, such as:

• Definition errors.
• Type mismatches.
• Missing lemmas.
• Logical jumps in reasoning.

4. High Reproducibility Requirements

Mathematical proofs must be reproducible.

The system needs to preserve:

• Proof scripts.
• Versions.
• Random seeds.
• Tool versions.
• Execution logs.
• Verification results.

Otherwise, a “success” cannot be confirmed.

This type of task also has broader enterprise relevance. Formal verification can be used to automatically check:

• Critical protocols.
• Safety rules.
• Compliance processes.
• Complex business rules.

The essence is not to make the model “write more like a human.” It is to let the model complete verifiable constructions under strict constraints.

6. Engineering Challenges of Open-Ended Software Development Tasks

Another typical task is to have agents develop a software system from an empty directory.

This may involve:

• Generating the project structure.
• Writing modules.
• Creating build scripts.
• Writing tests.
• Running tests.
• Fixing errors.

At first glance, this may look similar to a conventional coding assistant. However, it is more difficult.

Conventional coding assistants usually provide help around a single file or a single function. Open-ended software development requires the agent to complete a full lifecycle:

1. Understand requirements.
2. Design architecture.
3. Establish the directory structure.
4. Write core modules.
5. Write tests.
6. Build and run the project.
7. Fix issues based on errors.
8. Produce documentation and delivery notes.

The biggest challenge is acceptance criteria.

Without clear acceptance criteria, the model may generate a large amount of code that “looks complete” but cannot actually run.

Therefore, enterprises should convert the task into verifiable milestones.

For example:

• Stage 1: The project can be built.
• Stage 2: Core modules can run.
• Stage 3: Unit test pass rate meets the target.
• Stage 4: API documentation can be generated automatically.
• Stage 5: Security scanning finds no high-risk issues.
• Stage 6: Human review is passed.

Each stage should act as a gate, rather than being treated as a one-time delivery.

7. Cross-Modal Tasks: Hardware, Robotics, and Games

Long-horizon agents can also extend to cross-modal tasks, such as:

• Hardware design.
• Electronic device design.
• Robotic control.
• Game production.

These tasks differ from pure text or code generation because they often involve:

• Physical constraints.
• Time constraints.
• Resource constraints.
• Safety constraints.

For example:

• When designing electronic devices, the model must understand constraints such as circuits, power consumption, signals, packaging, and cost.
• When controlling physical robots, the model must handle sensor feedback, motion control, emergency stop mechanisms, and exception recovery.
• When producing games, the model must manage resource files, script logic, state machines, and user experience.

These tasks carry higher risk and should not rely only on automatic generation.

Recommended strategies include:

• Perform digital simulation first, then move to real execution.
• Require human confirmation for high-risk actions.
• Make all operations rollbackable.
• Apply physical isolation to physical devices.
• Use multiple rounds of review for generated results.
• Preserve complete operation audit records.

In other words, cross-modal agent systems must separate “capability” from “permission.”

The model can propose solutions, but execution rights should be controlled by security policies.

8. Evaluation Systems: Benchmarks Alone Are Not Enough

As the industry shifts from model releases to real task execution, evaluation systems must also evolve. A single benchmark test cannot reflect the true capability of long-horizon tasks.

More appropriate evaluation dimensions include:

Dimension	Description
Task completion rate	The proportion of goals completed within a limited time.
Milestone achievement rate	Whether key nodes are completed stage by stage.
Verification pass rate	Whether outputs pass tests, compilation, formal checks, or human review.
Human intervention count	How much human involvement is required to complete the task.
Error recovery capability	Whether the system can automatically correct failures.
Reproducibility	Whether the same inputs can consistently produce usable results.
Cost efficiency	Token, compute, and storage cost consumed per unit of task completion.
Security and compliance	Whether the system accesses data beyond its authority, touches sensitive data, or performs dangerous operations.

For enterprise users, evaluation should also include business metrics, such as:

• Whether delivery cycles are shortened.
• Whether rework rates decrease.
• Whether code defect rates are reduced.
• Whether documentation coverage improves.
• Whether operational alerts decrease.
• Whether the burden of manual review is reduced.

Only when both technical metrics and business metrics improve does a long-horizon agent have production value.

9. Common Failure Modes and Governance Methods

In real deployments, long-horizon agents may encounter several common problems.

1. Goal Drift

As execution reaches later stages, the agent may gradually deviate from the original goal.

Governance method:

• Re-validate the goal at each milestone.
• Treat original constraints as mandatory checks.

2. Error Accumulation

Early errors may not be detected, and later steps may continue to build on incorrect results.

Governance method:

• Add early verification.
• Use incremental verification.
• Conduct reviews at critical nodes.

3. Ineffective Loops

An agent may repeatedly try the same failing approach, consuming large amounts of resources without making progress.

Governance method:

• Set retry limits.
• Introduce strategy switching mechanisms.
• Add human escalation mechanisms.

4. Cost Loss of Control

Long-running tasks can cause resource consumption to rise rapidly.

Governance methods include:

• Budget caps.
• Task prioritization.
• Layered model invocation.
• Cache reuse.
• Asynchronous batch processing.

5. Security Risks

Agents may accidentally delete files, access data beyond their permissions, execute dangerous commands, or leak sensitive information.

Governance methods include:

• The principle of least privilege.
• Sandbox isolation.
• Operation approval.
• Sensitive data masking.
• Audit logs.

6. Non-Explainable Results

If the system only outputs final results without process records, business stakeholders may find it difficult to trust the system.

Governance method:

• Preserve the complete execution trace, including plans, tool calls, verification results, and change history.

10. Enterprise Adoption Path

Enterprises that want to build long-horizon agent systems can start with the following steps.

1. Select High-Value and Verifiable Scenarios

Prioritize tasks that have:

• Clear goals.
• Testable results.
• Rollbackable errors.

Examples include:

• Code migration.
• Documentation generation.
• Test completion.
• Log analysis.
• Rule review.

2. Build Task Templates

Break tasks into standard nodes, such as:

• Requirement analysis.
• Solution generation.
• Code implementation.
• Test verification.
• Report output.

For each node, define:

• Inputs.
• Outputs.
• Acceptance conditions.

3. Set Up Execution Sandboxes

Use container platforms to provide isolated execution environments. Use object storage to store code, documents, and build artifacts. Use message queues to handle asynchronous tasks. Use logging services to record execution traces.

4. Introduce Verification Gates

Every critical node must have automatic verification.

If verification fails, the task should not directly enter the next stage.

5. Establish Monitoring and Alerting

Monitor:

• Task success rate.
• Failure reasons.
• Resource consumption.
• Token consumption.
• Human intervention rate.
• Abnormal access behavior.

6. Start with a Small-Scale Pilot

First validate effectiveness in low-risk, observable, and easily rollbackable scenarios. Then gradually expand to more complex business use cases.

At the infrastructure level, teams can rely on general-purpose cloud capabilities:

• Container platforms can host agent execution environments.
• Object storage can store intermediate artifacts and final deliverables.
• Message queues can support asynchronous scheduling of long-running tasks.
• Logging services can record execution processes.
• Monitoring and alerting can track cost and failure rates.

The emphasis should be on combining general capabilities, rather than depending on a single product or closed interface.

11. The Key to Future Competition: System Integration

From an industry perspective, the next stage of large-model competition will not focus only on model parameters or single-turn inference capability. It will focus more on system integration capability.

Key capabilities include:

• Model planning capability.
• Multi-agent orchestration capability.
• Tool invocation and execution capability.
• Long-horizon state management capability.
• Automatic verification and quality gate capability.
• Security isolation and permission governance capability.
• Cost optimization and observability capability.
• Business system integration capability.

This means enterprises should not treat the model only as a chat interface. They should treat it as a decision component inside a complex task execution system.

Truly valuable applications are not about generating a piece of text. They are about allowing the model to continuously complete verifiable tasks in a controlled environment.

Long-horizon complex tasks represent an important direction for agent technology. They show the potential for models to move from “understanding language” to “executing work,” while also placing higher demands on engineering architecture.

For developers and enterprises, the key is not to chase one-off demonstration effects, but to build a stable, controllable, evaluable, and reusable agent execution system.

Only in this way can long-horizon agents evolve from demonstration cases into sustainable productivity tools.