Large-Scale Autonomous AI Agents: Technical Analysis of Long-Horizon Engineering and Operating System Construction

1. Technical Background: From Code Completion to Long-Horizon Autonomous Engineering

Over the past few years, the main value of AI programming tools has been concentrated in short-cycle tasks such as code completion, single-function generation, defect explanation, and draft test cases. These tasks usually have clear inputs, limited context, and fast verification, which makes them easier to deploy.

However, when developers expand the goal from “write a function” to “build a bootable operating system prototype from an empty project directory,” the complexity of the problem changes fundamentally.

Long-horizon engineering tasks no longer rely on a single generation step. Instead, they require agents to maintain continuous planning, module decomposition, interface constraints, dependency management, error recovery, continuous integration, and final acceptance. In other words, the agent is no longer just a “code generator.” It becomes closer to a long-running engineering collaboration system.

In some public examples and industry practices, early forms of this capability can already be observed. Starting from an empty folder, hundreds or even thousands of agents can run continuously for several days and autonomously complete modules such as the kernel, disk and network drivers, file system, window interface, and installer. The final result can be verified by booting on a local device. In this process, humans do not write the code line by line, but they still define the goals, boundaries, acceptance criteria, and key approval points.

This shift from “single-point generation” to “long-horizon autonomous engineering” depends on the maturity of three technical conditions:

1. Stronger long-context and planning capabilities: Agents can understand complex goals and decompose them into executable subtasks.
2. More mature tool-calling and sandbox execution capabilities: Agents can read and write files, run commands, execute tests, inspect logs, and roll back versions.
3. More controllable multi-agent coordination mechanisms: Many agents can work in parallel while avoiding repeated conflicts, context contamination, and uncontrolled verification.

2. Core Capability Model for Long-Running Agent Systems

To build an agent engineering system that can run for a long time, it is not enough to rely only on the model itself. A complete engineering capability system is required. It can be summarized in seven areas: task decomposition, coordination orchestration, context governance, sandbox execution, verification loops, cost accounting, and security governance.

2.1 Task Decomposition and Dependency Management

Complex engineering tasks should not be handed to agents as open-ended “free-form work.” The system must first decompose the overall goal into a traceable task tree or task graph.

For example, a minimal bootable operating system can be decomposed into:

• Project skeleton and build scripts;
• Boot loading logic;
• Kernel entry point and basic scheduling;
• Memory management module;
• Interrupt and exception handling;
• Disk driver;
• Network driver;
• File system;
• Graphical interface;
• User-space process management;
• Installer;
• Boot testing and stability testing.

Each module should have clear inputs, outputs, dependencies, and acceptance criteria. Dependencies between tasks should not rely only on natural-language descriptions. Where possible, they should be expressed as structured contracts, such as interface signatures, data structures, build targets, test commands, and performance metrics.

2.2 Multi-Agent Role Coordination

A single agent running for a long time can easily suffer from context expansion, error accumulation, and goal drift. A more robust approach is to assign different roles to different agents:

• Planner: Responsible for overall architecture, milestone decomposition, and priority decisions.
• Implementer: Responsible for coding specific modules.
• Reviewer: Checks code quality, interface consistency, and potential defects.
• Tester: Writes and executes unit, integration, and stress tests.
• Operator: Handles builds, deployment, log analysis, and runtime monitoring.
• Adjudicator: Makes the final decision when multiple proposals conflict.

The value of role separation is to reduce the impact of single-point errors. A module generated by one implementer can be verified by another reviewer or tester, reducing the bias of “generate it yourself and approve it yourself.”

2.3 Context Governance and Memory Mechanisms

In long-horizon tasks, agents need to remember project goals, historical decisions, failure reasons, and completed modules. However, unlimited context growth leads to higher costs and more noise.

Therefore, a layered memory mechanism is needed:

• Short-term working memory: Stores the current task goal, recent code changes, and current error logs.
• Project long-term memory: Stores architecture decisions, module descriptions, interface contracts, and version records.
• Failure experience library: Records previously failed implementation approaches, triggering conditions, and repair paths.
• Knowledge retrieval library: Provides general coding standards, testing strategies, and debugging experience.

The key to context governance is not to “remember everything.” It is to ensure that agents can obtain the right information at the right time.

2.4 Sandbox Execution and Toolchain

Agents cannot only generate text. They must also be able to execute code and observe the results. A complete execution sandbox usually includes:

• Code repository;
• Build environment;
• Compilation and linking tools;
• Unit test and integration test entry points;
• Simulator or virtual machine;
• Log collection;
• Crash stack traces;
• Version rollback mechanisms.

For high-risk targets such as operating systems, ordinary code testing alone is not enough. Simulators, virtual machines, or hardware-in-the-loop environments are also required. Agents need to determine whether the system is truly usable by inspecting logs, serial output, exception stacks, and performance data.

2.5 Verification Loops: Proving Correctness Matters More Than Generation

The core difficulty of large-scale agent engineering is not “writing a lot,” but “writing correctly.” Without strict verification, agents may generate a large amount of code that looks reasonable but cannot run, cannot pass tests, or contains security defects.

A reliable verification loop should include:

1. Static checks: Syntax, types, formatting, duplicate code, and potential null-pointer or out-of-bounds risks.
2. Unit tests: Verify function-level behavior.
3. Integration tests: Verify interfaces and collaboration between modules.
4. System tests: Verify complete boot, runtime behavior, recovery, and performance.
5. Stress tests: Verify concurrency, resource exhaustion, and abnormal inputs.
6. Regression tests: Ensure new changes do not break existing functionality.
7. Formal verification or proof checking: Used for critical algorithms, protocols, and security modules.

In complex mathematical proofs, physical derivations, and high-reliability system development, verifiers are often more important than generators. Agents can generate candidate proofs, candidate code, or candidate models, but the final result must be decided by deterministic tools or independent review mechanisms.

2.6 Cost Control and Budget Governance

Large-scale agent systems may appear powerful, but their costs can also be very high. Every inference request, tool call, test execution, and log storage operation incurs cost. Therefore, budget governance must be introduced.

Common strategies include:

• Setting a maximum number of turns for each task;
• Setting a cost ceiling for each module;
• Terminating low-value retries;
• Aggregating and analyzing high-frequency failure patterns;
• Caching and reusing successful modules;
• Increasing the budget for critical paths and lowering the model tier for non-critical paths.

A long-running agent system is not about “running forever.” It is about “converging as much as possible within budget.”

2.7 Security and Permission Governance

Once agents have code execution capabilities, security risks increase significantly. The system must follow the principle of least privilege:

• No network access by default;
• Write operations restricted to designated directories;
• Dangerous commands require approval;
• Keys and credentials must not enter the context;
• All operations must be recorded in audit logs;
• Critical releases must be confirmed by humans;
• Models must not modify their own permission boundaries;
• Agents must not infinitely copy their own tasks or expand resources without control.

For projects involving kernels, drivers, file systems, and network protocols, special attention should also be paid to supply-chain security, dependency licenses, build contamination, and runtime stability.

3. Case Abstraction: From an Empty Directory to a Bootable Operating System

Using “build an operating system from an empty folder” as an example, we can observe the complete workflow of a large-scale agent system.

3.1 Initial Phase: Establishing the Project Skeleton

The system first creates the basic directory structure, build scripts, version control, coding standards, test entry points, and log directories. Agents do not immediately start writing kernel code. Instead, they first define the project boundaries:

• Target architecture;
• Boot method;
• Hardware or emulation environment;
• Module division;
• Build process;
• Test process;
• Acceptance criteria.

The key to this phase is to prevent later agents from acting independently and producing conflicting modules or inconsistent interfaces.

3.2 Parallel Development Phase: Advancing Multiple Modules

After the project skeleton is established, the system can distribute tasks to multiple agents:

• One group handles boot and startup;
• One group handles memory management;
• One group handles interrupt processing;
• One group handles the disk driver;
• One group handles the network driver;
• One group handles the file system;
• One group handles the graphical interface;
• One group handles the installer;
• One group handles testing and stability.

After each agent completes a module, it must submit build artifacts, test reports, and interface descriptions. Review agents then check whether the module conforms to the project contract.

3.3 Integration Phase: Resolving Conflicts and Dependencies

The difficulty of an operating system project lies in the high coupling between modules. For example, the file system depends on the disk driver, the network driver depends on interrupt handling, and the graphical interface depends on memory management and process scheduling. The agent team needs to continuously perform integration debugging.

Common failures include:

• Code compiles successfully but crashes at runtime;
• Unit tests pass but the system fails to boot;
• A single module is stable but deadlocks occur under concurrency;
• Driver interfaces are inconsistent with kernel calling conventions;
• File system metadata writes are incomplete;
• Hardware initialization order during boot is incorrect.

Therefore, the system needs to continuously collect runtime logs, crash addresses, call stacks, and resource usage, and feed this evidence back to the relevant agents.

3.4 Acceptance Phase: From “Compilable” to “Bootable”

Final acceptance cannot be based only on the amount of code. It must verify whether the system truly meets the target. For example:

• Can it boot on a simulator or real device?
• Can it mount a file system?
• Can it read from and write to disk?
• Can it perform basic network communication?
• Can it run user-space programs?
• Can it display a graphical interface?
• Can it complete the installation process?
• Can it recover under abnormal conditions?

This type of case shows that the value of agent engineering is not to replace engineers in all creative work. Instead, it automates repetitive implementation, testing, debugging, and integration processes, allowing engineers to focus on architecture design, risk judgment, and key decisions.

4. Complex Scientific Tasks: Formal Proofs and Physical Derivations

In addition to software engineering, another important application of long-horizon agent collaboration is complex scientific computing. These tasks usually have high verification difficulty, long reasoning chains, and error-prone intermediate steps.

4.1 Formal Proofs: Turning Mathematical Reasoning into Verifiable Engineering

Formal proof requires converting natural-language mathematical proofs into formal languages that can be checked line by line by a computer. This process is highly tedious and involves many intermediate lemmas, type constraints, induction steps, and boundary handling.

Under traditional methods, researchers need to manually write proof scripts and repeatedly debug type errors and logical gaps. Agent systems can take on the following work:

• Decompose the overall proof goal into several intermediate theorems;
• Generate candidate proofs for each theorem;
• Search for different proof paths in parallel;
• Call a proof checker to verify each step;
• Attribute failures on unsuccessful paths;
• Combine successful fragments into a complete proof.

The key to this type of task is not to make the agent “guess the answer,” but to let it search around the verifier. The verifier guarantees that the final result can be checked, while the generator improves search efficiency.

4.2 Physical Derivations: Combining Symbolic Computation with Numerical Verification

In physics, many problems require symbolic derivation, numerical simulation, dimensional analysis, and boundary-condition judgment. For example, derivations involving general relativity, quantum theory, and complex system evolution often cannot produce reliable results through a single generation step.

Agent systems can use multi-path verification:

• One agent performs symbolic derivation;
• One agent performs numerical simulation;
• One agent checks dimensions and limits;
• One agent searches for counterexamples;
• One agent organizes references and axiomatic assumptions;
• A final adjudicator agent summarizes consistency.

This method is suitable for interdisciplinary problems because a single model can easily produce bias in a specific domain. Multi-agent retrieval, cross-validation, and independent evaluation can reduce error rates.

5. Enterprise Code Migration and Architecture Upgrades

In addition to building systems from scratch, large-scale agents can also be applied to legacy code migration. For example, migrating core services originally implemented in early scripting languages to lower-level, high-performance languages can improve startup speed, concurrency, and resource utilization.

These projects usually include the following steps:

1. Dependency analysis: Identify external calls, internal modules, configuration items, and data structures.
2. Behavior baseline: Record old-system functionality, performance metrics, and exception paths.
3. Interface mapping: Convert dynamic-language interfaces into statically typed interfaces.
4. Concurrency model conversion: Handle threads, asynchronous tasks, locks, and shared state.
5. Memory and ownership design: Clarify lifetimes, copying, moving, and resource release.
6. Incremental migration: Migrate module by module rather than rewriting everything at once.
7. Dual-track operation: Run the old and new systems in parallel and compare results.
8. Canary release: Gradually shift traffic and monitor anomalies.
9. Performance regression: Compare startup time, memory usage, throughput, and latency.
10. Documentation and knowledge retention: Produce migration notes and subsequent maintenance guides.

In these projects, the advantage of agents is that they can continuously perform repetitive migration work, generate tests, compare logs, and discover missed paths. However, architecture decisions, compatibility judgments, and production releases still require human engineers.

6. System Architecture: Observable, Recoverable Autonomous Engineering Pipeline

To move agent engineering from demonstrations to production, a reliable architecture similar to that of distributed systems is required. The overall architecture can be divided into a control plane, an execution plane, a data plane, and a security plane.

6.1 Control Plane

The control plane is responsible for task scheduling, state management, and budget control. It includes:

• Task queues;
• State machines;
• Dependency graphs;
• Retry policies;
• Timeout control;
• Human approval nodes;
• Milestone management.

The control plane must be able to determine whether a task is stuck, whether it is being executed repeatedly, and whether it exceeds the budget. If necessary, it should pause or terminate the task.

6.2 Execution Plane

The execution plane is responsible for actually running code. It includes:

• Isolated sandboxes;
• Code repositories;
• Build environments;
• Test environments;
• Simulators;
• Log collection;
• Artifact storage.

When each agent executes a task, it should have an independent and reclaimable environment to avoid polluting the main project.

6.3 Data Plane

The data plane is responsible for storing data generated by tasks, including:

• Code changes;
• Test reports;
• Build artifacts;
• Runtime logs;
• Crash stacks;
• Performance metrics;
• Review comments;
• Version snapshots.

This data must be searchable, traceable, and reusable. Only with complete data can agents perform error attribution and continuous optimization.

6.4 Security Plane

The security plane is responsible for permissions, auditing, and risk control. It includes:

• Identity authentication;
• Permission boundaries;
• Operation auditing;
• Sensitive-information masking;
• Dangerous-command interception;
• External dependency review;
• License compliance checks.

On general-purpose cloud platforms, object storage can be used to store build artifacts and logs, message queues can drive asynchronous tasks, container platforms can isolate execution environments, and logging services with monitoring alerts can track anomalies. These general cloud capabilities are not the core of agent engineering themselves, but they are important infrastructure for carrying large-scale, long-horizon tasks.

7. Illustrative Control Flow

The following simplified pseudocode shows the basic control logic of an agent engineering pipeline:

goal = "Build a minimal bootable operating system prototype"
project = initialize_project(goal)
milestones = planner.decompose(goal)
while not is_completed(milestones):
    tasks = scheduler.ready_tasks(milestones)
    for task in tasks:
        result = agent_pool.execute(
            task=task,
            sandbox=project.sandbox,
            context=project.relevant_context(task)
        )
        verification = verifier.run(
            code=result.artifacts,
            tests=task.required_tests,
            environment=project.build_env
        )
        if verification.passed:
            project.commit(result)
        else:
            project.retry_with_evidence(
                task=task,
                errors=verification.errors,
                logs=verification.logs
            )
        emit_metrics(
            cost=result.token_cost,
            latency=result.duration,
            retry_count=result.retry_count,
            test_pass_rate=verification.pass_rate
        )
        if budget_exceeded(task):
            escalate_to_human_review(task)

This flow emphasizes three points:

1. Every task must have clear acceptance criteria.
2. Failures must be retried with evidence.
3. Cost and progress must be observable.

8. Cost-Effectiveness Logic: Why Large-Scale Agents Are Becoming Practical

Large-scale agent systems were once considered expensive and unstable. However, as inference efficiency improves, task scheduling is optimized, and verification mechanisms mature, the cost-effectiveness logic is changing.

First, agents can significantly reduce repetitive manual work. Tasks such as code migration, test generation, log analysis, and module integration often consume a large amount of engineering time. Agents can run continuously and complete batch tasks overnight.

Second, parallelism can compress wall-clock time. Complex projects do not necessarily require a single agent to work continuously. Multiple agents can explore different modules in parallel. As long as the verification mechanism is reliable, parallelism can improve overall throughput.

Third, verification mechanisms can reduce rework costs. Although agents may need to generate a large amount of candidate code in the early stage, complete automated testing, compilation checks, and log analysis can reduce later manual debugging time.

Fourth, cost can shift from “payment per generation” to “measurement by engineering results.” Enterprises care less about how much code an agent writes and more about whether it shortens delivery cycles, reduces defect rates, and improves migration efficiency.

However, cost-effectiveness does not mean unlimited investment. A truly sustainable solution must control invalid generation, low-value retries, and unnecessary tests. For example:

• Use lower-cost models for simple modules;
• Use stronger reasoning capabilities for critical modules;
• Set maximum retry limits for failure paths;
• Cache and reuse modules that pass tests;
• Require human confirmation for high-risk operations;
• Set phased acceptance points for long-horizon tasks.

9. Risks and Boundaries: Being Able to Run Does Not Mean Being Deliverable

Autonomous agent engineering still has many boundaries. Developers should not assume that because agents can generate a bootable system, they can unconditionally replace an engineering team.

Major risks include:

1. Hallucinated interfaces and false dependencies
   Agents may generate nonexistent interfaces, libraries, or configuration items, causing build failures or runtime anomalies.

2. Test overfitting
   Agents may write code specifically to pass tests rather than truly implementing the required functionality. Therefore, hidden tests, stress tests, and human review are necessary.

3. Context contamination
   During long-running processes, error messages, outdated decisions, and irrelevant code may contaminate the context and cause later generations to drift away from the goal.

4. Security risks
   Agents may introduce insecure dependencies, dangerous commands, or scripts with excessive permissions.

5. Unexplainable decisions
   In complex architecture choices, agents may provide results but fail to clearly explain the reasons, requiring human review at key points.

6. Differences between simulators and real environments
   Passing in a simulator does not mean the system will be stable on real devices. Operating systems, drivers, and embedded systems especially require validation in real environments.

7. Uncontrolled self-improvement risk
   To prevent agents from expanding their capability boundaries during training or operation, the system should restrict self-modification, self-replication, self-scaling, and self-authorization capabilities, and should introduce independent auditing.

Therefore, the positioning of an agent system should be an “engineering amplifier,” not an “unattended black box.”

10. Implementation Recommendations: Start with Low-Risk Scenarios

For teams that want to introduce large-scale agent engineering capabilities, a gradual path is recommended.

Phase 1: Choose Low-Risk Modules

Start with scenarios such as:

• Unit test generation;
• Log analysis;
• Code explanation;
• Refactoring suggestions;
• Documentation generation;
• Script migration;
• Defect localization;
• Compatibility checks.

These tasks have lower failure costs and can be rapidly iterated through automated verification loops.

Phase 2: Build a Verification-First Process

Do not first pursue “agents autonomously completing all work.” Instead, build the verification system first:

• Automated builds;
• Automated tests;
• Static checks;
• Log collection;
• Version rollback;
• Metrics monitoring;
• Human approval nodes.

The more complete the verification system is, the larger the usable boundary of agents becomes.

Phase 3: Introduce Multi-Agent Collaboration

After single-agent tasks become stable, multi-role collaboration can be introduced:

• Separate planners from implementers;
• Separate implementers from reviewers;
• Separate generators from verifiers;
• Separate executors from operators.

Multi-agent systems are not simply about stacking more agents. They are about making different responsibilities constrain one another.

Phase 4: Pilot Complex Engineering Tasks

After the verification system matures, more complex goals can be attempted, such as:

• Module-level refactoring;
• Service-level migration;
• Test platform automation;
• Internal toolchain integration;
• Algorithm implementation assistance;
• Formal verification assistance;
• Rapid prototype system construction.

For high-risk domains such as operating systems, kernels, drivers, and high-reliability infrastructure, human engineers should retain final control over architecture, release, and security.

11. Conclusion

The core of large-scale autonomous agent engineering is not simply to make the model write more code. It is to build an engineering system that can run for a long time, continuously verify results, and remain controllable and reproducible. This requires task decomposition, role coordination, context governance, sandbox execution, test loops, cost accounting, and security auditing.

From building an operating system from an empty directory, to formalizing complex mathematical proofs, to cross-disciplinary physical derivations, and to enterprise code migration, these cases demonstrate the same type of capability: under clear goals and strict verification, agents can undertake large-scale, long-horizon, and repetitive engineering work.

For developers, the truly important question is not how strong a single generation capability is. It is how to integrate agents into a sustainable, observable, and governable software production process.